quick Reference Guide

on
Geographic Information System

Regional Remote Sensing Centre-North

National Remote Sensing Centre, ISRO

 Quick Reference Guide
On
Geographic Information System

Regional Remote Sensing Centre-North

National Remote Sensing Centre
Indian Space Research Organisation
 Index

1. Introduction to GIS………………………...........................1
Dr. Vinod Kumar Sharma
2. GIS Data Models… …………...…………………...............9
Khushboo Mirza
3. Coordinate Systems and Map Projections ………………20
Anurag Mishra
4. Spatial Analysis: Vector Analysis & Raster
Analysis…………………………………… ……................31
Khushboo Mirza
5. Network Analysis…………………………………........... 41
Abhinav Kumar Shukla
6. Map Composition………………………………............... 53
Anurag Mishra
7. FOSS4G - Open-Source Tools & Techniques
………………………………………………………..........59
Dr. Vinod Kumar Sharma
8. Global Navigation Satellite System (GNSS)
…………………………………………………….............74
Jayant Singhal
9. AI Applications in Remote Sensing and GIS
…………………………………………………………….83
Anurag Mishra
 Chapter – 1
Introduction to GIS
1.1 Introduction

Geographic Information Systems (GIS) are systems specifically

designed for the collection, storage, manipulation, analysis,
management, and presentation of spatial or geographic data. GIS can
be defined as a computer-based tool that encompasses four key
functions for handling georeferenced data: inputting data, managing
and retrieving data, manipulating and analysing data, and outputting
data. This comprehensive functionality allows users to explore
spatial patterns and relationships, thereby enhancing decision-
making processes in various fields and areas.

In the 1960s, the need for spatial data management and analysis
began to emerge. The first recognized GIS was developed by Roger
Tomlinson in Canada in 1963, known as the Canada Geographic
Information System (CGIS). This system was designed to analyse
land use and assist in resource management and planning.
Throughout the 1970s and 1980s, advancements in computer
technology and the development of more sophisticated software,
such as the introduction of the Environmental Systems Research
Institute (ESRI) by Jack Dangermond, propelled the growth of GIS.
The advent of personal computers in the 1980s and the subsequent
proliferation of the internet in the 1990s further expanded the
accessibility and functionality of GIS. Today, GIS is an
indispensable tool in various fields, including urban planning,
environmental management, and public health, driven by continuous
technological innovations and an ever-growing repository of spatial
data. Recent advancements in GIS, National Spatial Data
Infrastructure (NSDI) emphasis on information transparency and
sharing, with the recognition that spatial information is a national

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resource and citizens, society, private enterprise and government
have a right to access it, appropriately.

Geographic Information Systems (GIS) and remote sensing play

crucial roles in a variety of sectors across India. The Indian Space
Research Organisation (ISRO) leverages GIS and its Indian Remote
Sensing (IRS) satellites to monitor and manage natural resources
effectively. In agriculture, these technologies are used to estimate
crop acreage and yields, which are vital for ensuring food security
(https://bhuvan-app1.nrsc.gov.in/agriculture/agri.php). GIS also
enhances disaster management by providing real-time data and
predictive models to assess and mitigate the effects of natural
disasters like floods and earthquakes(https://bhuvan-
app1.nrsc.gov.in/bhuvandisaster/). Urban planning benefits from
GIS through its applications in designing smart cities, gram
panchayat management, controlling urban sprawl, and improving
infrastructure development(https://bhuvanpanchayat.nrsc.gov.in/).
Moreover, environmental monitoring programs use GIS to track
deforestation, soil erosion, and biodiversity, thereby promoting
sustainable development initiatives nationwide (https://bhuvan-
app1.nrsc.gov.in/moef/).

1.2 Components of GIS

Geographic Information Systems (GIS) are composed of several

essential components that collaborate to capture, store, analyse, and
present spatial data. These key components include hardware,
software, data, people, and methods.

Hardware: This encompasses the physical devices necessary for

GIS operations, such as computers, GPS units, and servers, which
supply the required computing power and storage.

Software: GIS software includes the tools and applications used to

process and analyse spatial data. Notable examples are ArcGIS,

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QGIS, and GRASS GIS, which provide functionalities such as data
input, spatial analysis, and map creation.

Data: Serving as the core of GIS, data comprises spatial data

(geographic locations) and attribute data (descriptive information).
Data can be sourced from satellite imagery, aerial photography, and
field surveys.

People: Skilled personnel are vital for the effective operation of GIS
systems. This category includes GIS specialists, analysts, and
managers who interpret data, conduct analyses, and make informed
decisions based on GIS outputs.

Methods: These involve the procedures and techniques used for data
collection, analysis, and presentation, ensuring the accuracy and
consistency of GIS operations, including best practices for data
management and spatial analysis.

Each of these components is integral to the successful

implementation and operation of GIS, enabling it to provide valuable
insights across diverse applications.

Table 1.1: List of GIS software’s and their main products

GIS Software Producer Main Product(s)

Esri (Environmental Systems ArcGIS (ArcMap, ArcGIS Pro,
Research Institute) ArcGIS Online)
QGIS QGIS (Quantum GIS)
Autodesk AutoCAD Map 3D, Autodesk
InfraWorks
Bentley Systems Bentley Map, MicroStation
Hexagon Geospatial ERDAS IMAGINE, GeoMedia
Pitney Bowes Software MapInfo Pro
GRASS GIS GRASS GIS (Geographic
Resources Analysis Support
System)

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 Intergraph Corporation (now GeoMedia, ERDAS Imagine
part of Hexagon)
Mapbox Mapbox GL, Mapbox Studio
Boundless Boundless Suite (including
OpenGeo Suite, Boundless Server)
Cadcorp Cadcorp SIS (Spatial Information
System)
SuperMap Software Co., Ltd. SuperMap GIS
Maptitude Maptitude GIS
Manifold System Manifold System
Global Mapper Global Mapper

1.3 Geospatial data

Spatial data refers to information that describes the geographic

location and characteristics of features on the Earth's surface (Fig.
1.1). Attribute data enriches spatial information with additional
details, such as population density or land use classifications.
Integrating spatial and attribute data empowers analysts to perform
thorough spatial analyses, aiding decision-making in diverse fields
like urban planning, environmental management, and public health.

Figure 1.1: Type of Geographical data.

Spatial data can be categorized into discrete features and

continuous features. Discrete features represent specific,

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identifiable objects with clear boundaries, such as buildings, roads,
and trees. Continuous features, on the other hand, represent
phenomena that vary continuously across space, such as elevation,
temperature, and precipitation.

Geospatial database models are structures used to organize and

store spatial data efficiently. Common models include the vector-
based feature dataset model and the raster-based grid model (Fig
1.2). The vector-based feature dataset model represents geographic
features as points, lines, or polygons with attributes, ideal for
complex shapes and precise spatial relationships. Conversely, the
raster-based grid model organizes spatial data into cells, suitable for
continuous phenomena and tasks like terrain modelling and remote
sensing analysis. The raster model prioritizes simplicity and
computational efficiency, whereas the vector model emphasizes
accuracy.

Figure 1.2: Vector & Raster data model.

Topology refers to the spatial relationships and connectivity

between geographic features. It helps ensure data integrity and

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facilitates spatial analysis by defining rules for how features can be
spatially related. Topology in GIS is defined as the spatial
relationships between adjacent or neighbouring features in
geographic plane. Mathematically, the topology assumes that
geographic features occur on a two-dimensional plane. Through
planar enforcement, spatial features can be represented through
nodes (0-dimensional cells); edges, sometimes called line (one-
dimensional cells); or polygons/area (two-dimensional cells).
Because features can exist only on a plane, lines that cross are broken
into separate lines that terminate at nodes representing intersections
rather than simple vertices. Different software offers different tools
for maintaining and querying these spatial relationships. For
example: disjoint, meet, equal, inside, covered by, contains, covers,
overlap etc.

Coverages and shapefiles are common file formats for storing

vector-based spatial data. Coverages are a legacy format developed
by Esri, while shapefiles are widely used for their simplicity and
compatibility across GIS software. A coverage stores a set of
thematically associated data considered to be a unit. It usually
represents a single layer, such as soils, streams, roads, or land use.
In a coverage, features are stored as both primary features (points,
arcs, polygons) and secondary features (tics, links, annotation).

A Triangulated Irregular Network (TIN) is a surface

representation model used to depict continuous features, such as
terrain elevation, using a network of irregularly spaced triangles. It
is efficient in terms of data storage. The irregularity of TIN allows
for lesser points to be used to represent smooth terrains. In this sense,
TINs are more efficient than the raster format, where all cells are
allocated a value, even if it is the same as the value of neighbouring
cells. Dynamic segmentation models allow for the linear referencing
of spatial data along linear features, such as roads or rivers, by
dynamically segmenting them into smaller units based on specific
attributes. Attribute data provide additional information about

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spatial features, such as population density, land use classification,
or building height. Joining spatial and attribute data allows analysts
to combine and analyse both types of data simultaneously, enabling
more comprehensive spatial analysis and decision-making
processes. Combining spatial and attribute data involves linking
geographic information with detailed descriptive data to enhance
dataset analysis and visualization. Spatial data, which encompasses
coordinates and shapes representing various locations and areas, is
integrated with attribute data that describes these locations. This
combination facilitates comprehensive GIS analysis, enabling tasks
such as mapping, spatial queries, and geostatistical analysis. By
merging these datasets, users can uncover deeper insights into spatial
patterns, relationships, and trends, thereby supporting more
informed decision-making in fields.

1.4 GIS operations

GIS activities can be grouped into following; however, sequence can

vary based on application:

1. Data capture and input

2. Data management
3. Data integration and display
4. Data exploration
5. Spatial analysis
6. GIS modelling
GIS operations involve systematic processes for managing,
analysing, and visualizing spatial data. These operations begin with
data capture from sources like GPS, remote sensing satellites, and
surveys. Data management then ensures the quality and organization
of both spatial and attribute data. Data integration combines different
datasets for comprehensive analysis, while spatial analysis

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techniques such as overlay, buffering, and querying reveal patterns
and relationships. Geocoding translates addresses into geographic
coordinates, and network analysis optimizes routes and connectivity.
Remote sensing processes satellite and aerial imagery, and 3D
visualization creates detailed models for examination. Spatial
statistics uncover trends and correlations, and mapping and
visualization produce informative maps and graphics. GIS models
simulate the real-world processes and provide insights of geographic
data patterns and relationships between them. Digital Elevation
Models (DEM) represents terrain surface in 3D and created using
terrain elevation data. Hydrological models like SWAT (Soil and
Water Assessment Tool and HEC-HMS (Hydrologic Modelling
System), models simulate the movement and distribution of water
on the earth’s surface. Network Models, analyse the connectivity
and flow within networks. Spatial statistical models, apply
statistical techniques to spatial data to identify patterns and
relationships like Kriging and Hot spot analysis.

1.5 Conclusion

Geographic Information Systems (GIS) is a powerful technology

that merges spatial and non-spatial data, offering deep insights into
the geographic context of various phenomena. Utilizing GIS allows
users to gather, manage, analyse, and visualize geographic
information, uncovering patterns, relationships, and trends not easily
seen through traditional data analysis. GIS is essential in fields like
urban planning, environmental management, transportation, and
public health, providing vital support for decision-making and
strategic planning. The core functions of GIS—data acquisition, data
management, spatial analysis, and visualization—collaborate to
enable precise and detailed spatial analyses. As GIS technology
continues to advance, its range of applications is set to grow,
reinforcing its status as an indispensable tool in scientific research
and practical applications across various domains.

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 Chapter 2
GIS Data Models
2.1 Introduction

Geographic Information Systems (GIS) are essential tools for

analysing and managing spatial data. Central to the operation of GIS
are data models, which provide the structure for storing, organizing,
and analysing geographic information (Fig. 2.1). These models
define how spatial data is represented within the system, making an
understanding of their types crucial for effective GIS analysis and
application. This chapter explores the two main types of data models
used in GIS: raster and vector data models.

Figure 2.1: Geographic features represented by layers.

2.2 Spatial Data Models

Spatial data models describe the representation of geographic

features within a GIS. These models can be broadly classified into

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vector and raster models (Fig. 2.2), each suited to different types of
spatial data and analyses.

Figure 2.2: Raster and vector representation of real world.

2.2.1 Vector Data Model

The vector data model uses geometric objects such as points, lines,
and polygons to represent spatial features. Features are real-world
objects such as roads, property boundaries, electrical substation sites
and so on. A feature has a geometry (which determines if it is
a point, polyline or polygon) and attributes (which describe the
feature) (Fig. 2.3).

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Figure 2.3:Representation of vector feature.

Every point or vertex associated with vector data contains the x,y
location of the point. In vector data layers, the feature layer is linked
to an attribute table (Fig. 2.4). Every individual feature corresponds
to one record (row) in the attribute table.

Points

Points are the simplest form of vector data, representing discrete

locations defined by a pair of coordinates (x, y). Examples include
wells or bus stops.

Lines

Lines (or polylines) are sequences of points connected by straight

segments. Lines are recorded as a series of ordered x,y coordinates;
They are used to represent linear features such as roads, rivers, and
pipelines.

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Polygons

Polygons are enclosed areas formed by connecting multiple lines.

Polygons are recorded as a series of x,y coordinates defining line
segments that enclose a polygon. They represent features such as
lakes, parks, and land parcels.

Figure 2.4: Vector and raster data model in geographic case.

Advantages of vector data-

- Precision: Vector data provides high precision in

representing geographic features.
- Topological Relationships: Vector data can easily
represent topological relationships, which is essential for
network analysis and understanding spatial relationships.
- Storage Efficiency: Generally, vector data can be more
storage-efficient for certain data types compared to raster
data.

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Disadvantages of Vector Data

- Complexity: Managing and processing vector data can be

complex, especially with large datasets.
- Computation Intensive: Some spatial operations, like
overlay analysis, can be computationally demanding.

2.2.2 Raster Data Model

The raster data model represents geographic features as a grid of

cells or pixels, where each cell contains a value representing a
specific attribute, such as elevation or land cover (Fig. 2.5). The
value can be in the form of an integer, floating points or
alphanumeric character. A point can be represented by a single pixel
in the raster model. A line is a chain of spatially connected cells with
the same value. Similarly, a water body in the raster data is
represented as a set of contiguous pixels having the same value,
representing a homogeneous area.

Figure 2.5: Raster data model.

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Cells and Grids

In raster models, the geographic area is divided into a regular grid of

cells. Each cell holds a value that represents a particular attribute
(e.g., elevation, land cover type).

Continuous and Discrete Data

- Continuous Data: Represent phenomena like elevation or

temperature, which change gradually over space.
- Discrete Data: Represent phenomena with distinct
boundaries, such as different land use types (Fig. 2.6).

Figure 2.6: Type of raster data.

Advantages of raster data-

- Simplicity: The raster model is straightforward and easy to

understand.
- Analytical Efficiency: Well-suited for mathematical
modelling and spatial analysis, especially for continuous
data such as satellite imagery and environmental data.

Compatibility: Integrates well with remote sensing data, useful in

applications like climate modelling and terrain analysis.

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Disadvantages of raster data-

- Resolution Dependency: The quality of raster data

depends on its resolution, with higher resolutions requiring
more storage.
- Storage Requirements: Large raster datasets can be
storage-intensive.
- Less Precision: Compared to vector data, raster data can
be less precise in representing boundaries and linear
features.

2.3 Attribute Data Models

Attribute data models, store descriptive information about

geographic features. It consists of the characteristics of spatial
features that are independent of all geometric considerations. This
information is typically organized in tables, with each row
representing a feature and each column representing an attribute
(Fig.2.7).

Figure 2.7: Attribute information for spatial data.

Attribute Tables

Attribute tables link non-spatial data to spatial features, enabling

detailed descriptions and analyses. Each record (row) in the table
corresponds to a geographic feature, while each field (column) holds
a specific attribute.

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Field Types

- Numeric Fields: Store numbers, such as population or

area.
- Text Fields: Store descriptive text, such as names or types.
- Date Fields: Store dates and times.
- Boolean Fields: Store true/false values.

Relational Databases

Relational databases are commonly used to manage non-spatial data.

They organize data into tables and define relationships between
them using keys. This structure supports complex queries and
efficient data management.

Advantages of Non-Spatial Data

- Detailed Information: Provides comprehensive

descriptions of spatial features.
- Flexibility: Easily updated and queried.
- Integration: Can be integrated with spatial data for complex
analyses and comprehensive reports.

Disadvantages of Non-Spatial Data

- Maintenance: Managing large datasets requires regular

updates and maintenance.

- Complex Queries: Advanced queries can require

significant computational resources and expertise.

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2.4 Database Models

A database model serves as the theoretical framework of a database,

dictating how data can be stored, organized, and manipulated within
a database system. It establishes the structure provided by a specific
database system. Different methods of organizing databases are
known as database models. The primary database models include
relational, network, hierarchical, and object-oriented database
models (Fig. 2.8):
 In the hierarchical model, data are organized into a tree-
like structure with parent-child one-to-many relationships.
 In the network model, data are structured with records
connected through pointers, classified into record types.
 In the relational model, data are stored in tables, with
records organized into rows and columns.
 In the object-oriented model, data are represented as
objects, each with unique attributes and operations, and
classified into object types or classes.

Figure 2.8: Database models; (a) relational, (b) network, (c) hierarchical
and (d) object-oriented database models.

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2.5 Integrating Spatial and Non-Spatial Data

Effective GIS analysis often requires integrating spatial and non-

spatial data. This integration allows for more detailed and informed
decision-making.

Joining Data

Joining data involves linking attribute tables to spatial features using

a common identifier. This process enables the combined analysis of
spatial locations and their attributes.

Spatial Analysis

Spatial analysis involves a range of techniques that help in

interpreting and understanding geographic data. These techniques
enable the assessment of patterns, relationships, and trends within
spatial data. Common types of spatial analysis include:

- Overlay Analysis: Overlay analysis is a fundamental

technique in spatial analysis that involves superimposing
multiple layers of spatial data to identify relationships and
patterns. This method allows the integration of different
datasets, such as land use, vegetation cover, and population
density, to generate new insights.

- Proximity Analysis: Proximity analysis assesses the

distance between spatial features and determines the spatial
relationship based on distance. It is crucial for identifying
the influence of one feature on another.

o Buffer Analysis: A common proximity analysis

technique is buffer analysis, where buffer zones
(areas within a specified distance) are created
around a feature to analyse their impact or
influence.

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 o Nearest Neighbour Analysis: This technique
evaluates the closest distances between features.

- Network Analysis: Network analysis examines the

relationships and flows within networks, such as
transportation, utilities, and communication systems. It
helps in optimizing routes, improving connectivity, and
managing resources effectively.

o Route Optimization: Network analysis can find

the shortest or fastest route between two points.

o Service Area Analysis: This identifies the area

covered within a certain distance from a service
location.

2.6 Conclusion

Understanding spatial and non-spatial data models is fundamental to

leveraging the full potential of GIS. Spatial data models provide the
framework for representing geographic features, while non-spatial
data models enrich these features with descriptive attributes. The
integration of these models enables powerful spatial analysis,
supporting a wide range of applications from urban planning to
environmental management. As GIS technology continues to
evolve, the effective use of both spatial and non-spatial data models
will remain crucial for addressing complex geographic challenges.

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 Chapter 3
Coordinate Systems and Map Projections

This chapter covers the basic concepts of coordinate systems and

map projections that are central to a GIS system. These concepts are
used for accurate representation of the complex, curved surface of
the Earth on flat maps or digital screen.

3.1 Coordinate Systems

A coordinate system is a framework that allows for the precise

location of geographic features on the Earth. It provides a
standardized method to define positions through coordinates,
typically using a set of numbers. There are two primary types of
coordinate systems used in GIS, namely, geographic coordinate
systems (GCS) and projected coordinate systems (PCS).

3.1.1 Geographic Coordinate Systems

Geographic coordinate systems are used for determining the location

of a feature on three-dimensional spherical earth surface. It uses
latitude and longitude to specify the location. Latitude measures the
angle north or south of the Equator, while longitude measures the
angle east or west of the Prime Meridian (passing through
Greenwich England). This system is ideal for global or large-scale
maps where the curvature of the Earth needs to be taken into account.
The figure below shows the different latitude parallels with respect
to equator and longitudes with respect to prime meridian.

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Figure 3.1: The latitude lines with respect to equator on the left and
longitude lines with respect to prime meridian on earth surface on the right
(https://www.britannica.com/science/latitude).

The latitude is determined by the angle between a location on earth’s

surface and equatorial plane (Fig. 3.1). Similarly, longitude is a
measure of angle between prime meridian plane and north-south
plane crossing the feature location. For instance, the approximate
latitudinal extent of India is from 6.7° N to 37.1°N and longitudinal
extent is from 68.1° E to 97.5° E.

The determination of latitude and longitude of an earth feature for

its visualization on a geographic information system involves the
mathematical modelling of the curved earth surface.

3.1.1.1 Ellipsoid

An ellipsoid is a mathematically defined surface that approximates

the shape of the Earth. It is an elongated sphere, or spheroid, with
two principal radii: the equatorial radius (semi-major axis) and the
polar radius (semi-minor axis). The equatorial radius is slightly
larger than the polar radius, making the ellipsoid an oblate shape,
which tries to capture the elongated shape of the Earth around the
equator. This shape is used because it provides a simpler, more
uniform model for calculations compared to the Earth's actual shape.

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Commonly used reference ellipsoids include the WGS84 and
GRS80, which are defined by their semi-major and semi-minor axes,
as well as their flattening factor. The figure 3.2 shows a comparison
of a sphere and an ellipsoid.

Figure 3.2: Sphere and ellipsoid (https://www.caliper.com/).

3.1.1.2 Geoid

A geoid is a model of the earth's shape that represents the mean sea
level across the planet's oceans, extended through the continents. It
is a more complex and irregular surface compared to the ellipsoid
because it accounts for variations in earth's gravitational field caused
by factors such as mountain ranges, ocean trenches, and density
differences in the Earth's interior. The geoid undulates due to these
gravitational anomalies, providing a more accurate reference for
measuring elevations and understanding the earth's gravitational
field.

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Figure 3.3: The Earth’s geoid (https://www.esa.int/).

The geoid (the surface of equal gravitational potential of a

hypothetical ocean at rest) serves as the classical reference for all
topographical features (Fig. 3.3). The above image shows the Earth’s
geoid. The areas having stronger gravity field are shown by red-
yellow color and those having weaker gravity are depicted by blue
color.

3.1.1.3 Datum

A datum is a reference framework that defines the position of the

ellipsoid relative to the center of the Earth and provides a basis for
geographic coordinates. There are two main types of datums:
horizontal datums, which specify latitude and longitude coordinates
on the Earth's surface, and vertical datums, which measure
elevations relative to the geoid. Horizontal datums, such as WGS84
(used in GPS) and NAD83, are crucial for mapping and navigation,
ensuring consistent and accurate spatial data across different regions
and applications. Vertical datums, such as the North American
Vertical Datum of 1988 (NAVD88), are used for measuring and
comparing elevations.

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 3.2 Projected Coordinate System

A projected coordinate system is a method of mapping the Earth's

three-dimensional surface onto a two-dimensional plane. This
system allows for the representation of geographic features in a flat,
rectangular coordinate system, which facilitates easy calculation of
distances, areas, and angles. A projected coordinate system uses
linear units in place of angular units as are used in geographic
coordinate system. Therefore, a projected coordinate system is
composed of a geographic coordinate system along with a map
projection (Fig. 3.4). The map projection provides a mathematical
transformation function that converts geographic coordinates to
planar coordinates. The figure below shows the geographic
coordinates of a location converted to planar coordinates.

Figure 3.4: The coordinates of a location in GCS (left) and PCS (right)
(https://naarm.org.in/).

3.2.1 Map projections

The map projections facilitate mathematical transformation of

location information of features on a curved surface to a 2-D plane.
The term ‘projection’ comes from the idea of placing a light source
within a transparent globe and projecting shadows of the meridians,
and parallels onto a sheet of paper placed tangent to the globe. Each

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map projection has certain strengths and weaknesses in terms of the
accuracy of shape, area, distance and direction. It is not possible for
any projection to retain more than one of these characteristics over a
large part of the Earth. It is important to identify that because of the
curvature of the Earth, all map projections distort distance and
directional relationships. The figure 3.5 shows the projection of
point P’(ϕ, λ) on a 3D sphere to P(x, y) on 2D plane.

Figure 3.5: Map projection from reference surface to map plane

(https://kartoweb.itc.nl/geometrics/Map_projections/mappro.html).

There exist many different types of map projections, each type is

intended for a different application. These map projections are
broadly classified as following types:

1. Projection surface (cylindrical, conical or azimuthal),

2. Point of secancy (tangent or secant),
3. Aspect (normal, transverse or oblique), and
4. Distortion property (equivalent, equidistant or conformal).

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 3.2.1.3 Map projections based on projection surface

Based on the projection surface, map projections are classified as,

(i). Cylindrical, (ii). Conical, and (iii). Azimuthal. The projection
system is depicted in the figure 3.6, where each type of projection
surface is wrapped around earth in such a way that projection surface
is a tangent at points of contact with reference surface.

Figure 3.6: Map projections based on projection surface

(https://kartoweb.itc.nl/geometrics/Map_projections/mappro.html)

3.2.1.4 Map projections based on point of secancy

Another class of projections is obtained if the surfaces are chosen to

be secant to (intersect with) the horizontal reference surface. In this
case, the reference surface is intersected along one closed line
(plane) or two closed lines (cone and cylinder). Secant map surfaces
are used to reduce or average scale errors because the line(s) of
intersection are not distorted on the map (Fig. 3.7).

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Figure 3.7: Map projections based on point of secancy
(https://kartoweb.itc.nl/geometrics/Map_projections/mappro.html)

3.2.1.5 Map projections based on aspect

This class of projection is defined based on the projection plane’s

orientation with respect to the globe. There are three possible
aspects: normal, transverse, and oblique, depending on how the
projection plane intersects Earth's axis. The projection plane is
normal, parallel, and at an angle (non-parallel and non-normal) to
Earth’s axis in normal, transverse, and oblique projections,
respectively (Fig. 3.8).

Figure 3.8: Transverse and oblique projections

(https://kartoweb.itc.nl/geometrics/Map_projections/mappro.html).

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 3.2.1.6 Map projections based on distortion property

The distortion properties of a map are typically classified according

to what is not distorted on the map. For instance, in a conformal map
projection the angles between lines in the map are identical to the
angles between the original lines on the curved reference surface.
This means that angles and shapes are accurately represented on the
map. The figure below shows a Mercator projection which preserves
the conformal property by accurately representing local angles and
shapes. However, it exhibits large area distortions. Greenland,
having area 1/8th of that of South American continent, appears larger
than the South America.

Similarly, in an equal-area (equivalent) map projection, the areas on

the map are identical to the areas on the curved reference surface
(taking into account the map scale), ensuring that areas are
represented correctly on the map (Fig. 3.9). No map projection can
be both conformal and equal-area. A projection can only be
equidistant (true to scale) at certain places or directions.

Figure 3.9: Mercator projection exhibiting area distortions.

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 3.3 Commonly used map projections

3.3.1 Universal Transverse Mercator

The Universal Transverse Mercator (UTM) projection is a widely

used map projection system that divides the world into a series of
six-degree longitudinal zones. Each zone uses a transverse Mercator
projection cantered on a meridian. This projection is conformal,
meaning it preserves local angles and shapes, making it ideal for
detailed and accurate mapping. This projection is used extensively
for detailed topographic maps, especially for large-scale mapping.

3.3.2 Lambert Conformal Conic

The Lambert Conformal Conic (LCC) projection is a conic map

projection that is widely used for aeronautical charts, regional
mapping, and weather maps. This projection is conformal, which
means it preserves local angles and shapes, making it highly suitable
for detailed and accurate representations of regions with larger east-
west extents.

3.3.3 Albers Equal Area Projection

The Albers Equal-Area projection is a conic map projection that is

designed to minimize distortion of area, making it ideal for thematic
and statistical maps where accurate area representation is crucial.
The Albers projection maps the Earth's surface onto a cone that
intersects the globe along two standard parallels. These parallels are
chosen based on the specific region being mapped and represent the
lines of latitude where the cone touches the globe. This projection
preserves area, ensuring that the size of features on the map is
proportional to their size on the Earth. This is particularly important
for thematic maps that require accurate representation of regions
sizes, such as population density or land use maps.

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 3.4 Conclusion

Coordinate systems and map projections form essential cornerstones

in geographic information systems (GIS), integral to accurately
representing and analysing spatial data. The selection of a coordinate
system dictates how geographic positions are defined on the Earth's
surface, whether through spherical coordinates like latitude and
longitude or projected coordinates on a flat map. Map projections,
in turn, convert the Earth's curved surface into a two-dimensional
map, balancing considerations of area, shape, distance, and direction
depending on the application's requirements. Proficiency in these
concepts is critical for GIS professionals, ensuring precise analyses
in fields ranging from urban planning and environmental
management to navigation and scientific research. The process of
choosing the appropriate coordinate system and map projection
involves evaluating factors such as scale, distortion characteristics,
and the specific intended use of spatial data, underscoring GIS's
interdisciplinary relevance across various domains today.

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 Chapter 4
Spatial Analysis: Vector Analysis & Raster Analysis
Spatial analysis is a crucial component of Geographic Information
Systems (GIS), enabling the examination of spatial data to discern
patterns, relationships, and trends. In geographic information
systems (GIS), vector and raster analyses are fundamental methods
for manipulating spatial data. Both techniques offer unique strengths
and are applied based on the nature of the data and the requirements
of the analysis. In this chapter, we will delve into two primary forms
of spatial analysis: vector analysis and raster analysis. Each of these
methodologies has unique characteristics, applications, and
advantages that make them suitable for different types of spatial data
and analysis tasks.

4.1 Vector Analysis

Vector data models represent geographic features as points, lines,

and polygons. Vector data is ideal for representing discrete features
with precise locations and boundaries, such as buildings, roads, or
administrative boundaries. Each feature is defined by its vertices and
can store various attributes. Vector analysis focuses on the
relationships and interactions between these features.

Basic operations in vector data analysis are-

Buffering creates zones around vector features to analyse spatial

relationships and proximity. This operation is fundamental in many
GIS applications, such as environmental impact assessments or
urban planning (Fig. 4.1).

 Point Buffers: Useful for identifying areas within a certain

distance of a specific location (e.g., determining the area
within 500 meters of a school).

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  Line Buffers: Applied to linear features like roads or rivers
to define influence zones (e.g., creating a buffer around a
highway to assess noise pollution).
 Polygon Buffers: Surround entire areas with a buffer zone,
useful in environmental studies (e.g., establishing a buffer
around a protected area).

Figure 4.1: Buffer around Point, Line, Polygon features.

Overlay analysis is a powerful technique for combining multiple

vector layers to investigate spatial relationships. This is crucial for
integrating various datasets and drawing insights from complex
spatial data. The primary overlay operations are (Fig.4.2):

 Intersection: Identifies common areas where two layers

overlap. The resulting layer contains features where both
input layers intersect, preserving attribute data from both.
For example, finding areas where commercial zoning
overlaps with high flood risk zones.
 Union: Merges all features from both input layers into a
single layer, combining their geometries and attributes.
 Difference: Subtracts the features of one layer from
another, identifying areas present in the first layer but not
in the second.

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Figure 4.2: A visualization of the vector overlay operations.

Spatial querying involves selecting and analyzing features based on

their spatial relationships or attributes. It enables users to extract
specific subsets of data for further analysis or visualization.

 Attribute Query: Selects features based on attribute values

(e.g., all parks larger than 10 hectares).
 Location Query: Selects features based on spatial criteria
(e.g., all hospitals within 2 km of a major road).

Advanced techniques in vector data analysis are explained below-

Network analysis examines connectivity, flow, and accessibility

within spatial networks, such as transportation systems, utilities, or
communication networks. It is essential for optimizing routes,
evaluating service areas, and understanding network dynamics.

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  Shortest Path Analysis: Determines the most efficient
route between two points, considering distance, travel time,
or other costs (Fig.4.3).
 Service Area Analysis: Identifies areas reachable within a
certain distance or time from a facility, useful for
emergency response planning.
 Network Flow Analysis: Analyses the flow of goods,
services, or people through a network, crucial for logistics
and supply chain management. E.g. calculating the shortest
path between two locations in a road network.

Figure 4.3: Shortest path from source node 0 to target node 10 using
network analysis.

Topological modeling examines the spatial relationships between

features, such as adjacency, connectivity, and containment.
Topology ensures data integrity and supports complex spatial
analysis by maintaining consistent relationships between vector
features (Fig. 4.4).

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  Adjacency: Identifies features that share a common
boundary (e.g., neighboring land parcels).
 Connectivity: Analyses how features are connected,
crucial for network analysis (e.g., road intersections).
 Containment: Determines if one feature is contained
within another (e.g., determining which land parcels fall
within a specific zoning area).

Figure 4.4: Topological Relationships showing adjacency, connectivity,

and containment.

Spatial interpolation estimates values at unsampled locations based

on known values from surrounding points. It is used to create
continuous surfaces or predict spatial patterns from discrete
observations.

 Inverse Distance Weighting (IDW): Estimates values

based on the inverse of the distance to known points, giving
more weight to closer points. Inverse Distance weighting
models work on the premise that observations further away
should have their contributions diminished according to
how far away they are. The simplest model involves
calculating the weighted mean for all of the observations

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The basic IDW interpolation formula is as below

𝑤1𝑥1 + 𝑤2𝑥2 + 𝑤3𝑥3 + ⋯ + 𝑤𝑛𝑥𝑛

𝑥∗ =
𝑤1 + 𝑤2 + 𝑤3 + ⋯ + 𝑤𝑛
Where x* is unknown value at a location to be determined, w is the
weight, and x is known point value. The weight is inverse distance
of a point to each known point value that is used in the calculation.
Simply the weight can be calculated as below

1
𝑤1 = 𝑝
𝑑𝑖𝑥 ∗

p is a positive real number, called the power parameter.

A value at position x will be determined from sampling points 1, 2,

and 3, with the distances to x point are d1x, d2x and d3x. Using the
equations, each respective weight will be calculated and then the
value at position x will be determined.

 Kriging: Kriging is an advanced geostatistical technique

for spatial interpolation that leverages both the distance and
the degree of variation between known data points to
estimate values at unknown locations. Kriging provides not
just predictions but also measures of the prediction
uncertainty, making it highly valuable for various
applications in geology, environmental science, and
resource management.

Ordinary Kriging is the most commonly used type. It predicts the

value Z(x0) at an unknown location x0 based on weighted sums of
the known values Z (xi).
𝑁

𝑍(𝑥0 ) = ∑ 𝜆𝑖 𝑍(𝑥𝑖 )
𝑖=1
where:
o 𝑍(𝑥0 ) = estimated value at location 𝑥0

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 o 𝑍(𝑥𝑖 ) = estimated value at location 𝑥𝑖
o 𝜆𝑖 = weight assigned to the known value at 𝑥𝑖
o N= number of known sample points

4.2 Raster Analysis

Raster data represents spatial information through a matrix of cells

or pixels, where each cell contains a value representing a specific
attribute, such as elevation, temperature, or land cover. This format
is particularly suited for modeling continuous phenomena and
analyzing spatial patterns over large areas.

Basic operations in raster data analysis-

Map Algebra involves performing mathematical operations on

raster datasets to create new outputs. It is a flexible tool for spatial
analysis and modeling, allowing for a range of operations from
simple arithmetic to complex spatial analyses (Fig. 4.5).

 Local Operations: Apply a function to each cell

individually based on one or more input rasters. For
example, calculating the sum of two elevation rasters.
 Focal Operations: Apply a function to a cell based on its
neighborhood. This can be used to calculate the average
elevation within a specified radius around each cell.
 Zonal Operations: Apply a function to all cells within
each zone of a raster, such as calculating the mean
temperature within different climate zones.

Figure 4.5: Map algebra operations.

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Reclassification involves changing the values of raster cells based
on a set of rules or criteria. It simplifies complex datasets, making
them easier to analyze and interpret (Fig. 4.6).

 Binary Reclassification: Converts continuous or

categorical data into a binary format (e.g., reclassifying
land cover into forest and non-forest).
 Categorical Reclassification: Groups data into categories
(e.g., converting elevation ranges into altitude classes).
 Continuous Reclassification: Adjusts continuous values
into new ranges or classes (e.g., reclassifying temperatures
into hot, warm, and cold).

Figure 4.6: Reclassification by range of values.

Suitability analysis evaluates multiple criteria to determine the best

locations for a specific purpose, often using weighted overlay
techniques.

Advanced Techniques in raster data analysis-

Surface analysis derives terrain attributes from raster data, typically

digital elevation models (DEMs). It helps in understanding
topography, landform characteristics, and environmental processes
(Fig. 4.7).

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  Slope: Calculates the steepness of the terrain, useful for
erosion studies and infrastructure planning.
 Aspect: Determines the direction of slope faces, important
for solar radiation and vegetation analysis.
 Hillshade: Creates a shaded relief map that simulates how
terrain appears under specific lighting conditions,
enhancing topographic visualization.

Figure 4.7: Slope, aspect and the hillshade derived from the DEM. a) Slope,
b) Aspect, c) Hillshade.

Raster to vector conversion transforms raster data into vector

format, enabling integration with vector datasets and facilitating
certain types of spatial analyses (Fig. 4.8).

 Contour Extraction: Converts elevation rasters into

contour lines, useful for topographic maps.
 Polygonization: Transforms classified raster data into
vector polygons, such as converting land cover classes into
vector polygons for land use mapping.
 Stream Network Extraction: Converts flow accumulation
grids into vector stream networks for hydrological analysis.

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Figure 4.8: Elevation plot from contour map.

4.3 Conclusion

Both vector and raster analyses are integral to spatial analysis, each
offering unique capabilities and suited to different types of data and
applications. By mastering spatial analysis techniques, GIS
professionals can effectively analyze spatial data, uncover patterns,
and inform decision-making processes in various applications.
Understanding their differences and strengths allows for the
effective application of GIS techniques in various fields, from urban
planning to disaster management.

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 Chapter 5
Network Analysis
5.1 Network Analysis in GIS

Network analysis in GIS examines relationships within spatial

networks, consisting of interconnected nodes (points) and edges
(lines) that represent real-world systems like roads and utility lines.
It aims to solve spatial problems by analysing these connections.

This analysis is crucial for optimizing routes, enhancing

accessibility, managing infrastructure, and making informed
decisions across various sectors. For example, it aids urban planners
in designing efficient transportation systems, helps emergency
responders find the quickest routes, and allows logistics companies
to optimize delivery paths. Analysing spatial networks improves
operational efficiency, reduces costs, and enhances service delivery.

Network analysis has diverse applications, including:

1. Transportation Planning: Optimizing public transit

routes, traffic flow, and planning infrastructure.
2. Emergency Response: Identifying the fastest routes for
emergency vehicles and planning evacuations.
3. Logistics: Optimizing delivery routes and managing fleet
operations.
4. Utilities: Managing networks of pipelines, electrical grids,
and communication lines.
5. Urban Planning: Analysing accessibility to amenities and
planning pedestrian and bicycle paths.
6. Environmental Management: Studying water flow in
river networks and managing natural resources.
7. Healthcare: Optimizing locations of healthcare facilities
and analysing patient access.

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Key data structures for network analysis in GIS include:

 Vector Data Model: Represents networks with points

(nodes) and lines (edges), with attributes like length and
travel time.
 Raster Data Model: Uses a grid of cells to represent
networks, often for environmental networks.
 Topological Data Model: Focuses on relationships and
connectivity between network elements, maintaining
spatial relationships.

These structures are essential for building and analyzing networks in

GIS, enabling accurate analysis and optimization of spatial
networks.

5.2 Fundamentals of Network Theory

Graph theory forms the mathematical basis of network analysis,

focusing on graphs composed of nodes (or vertices) and edges (or
links). A graph is a collection of nodes connected by edges,
representing relationships between objects.

Key concepts in graph theory include:

 Graph: A structure made up of nodes connected by edges.

 Vertex (Node): The basic unit in a graph, representing
locations in the network (e.g., intersections, cities).
 Edge (Link): The connection between nodes, representing
relationships or pathways (e.g., roads, communication
lines).
 Degree: The number of edges linked to a node; in directed
graphs, this includes in-degree (incoming edges) and out-
degree (outgoing edges).
 Path: A sequence of edges that connects a series of nodes.
 Cycle: A path that begins and ends at the same node
without repeating any edges.

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Types of Networks: Directed and Undirected

Networks can be classified based on whether their edges have a

direction:

Undirected Networks:
o Edges do not have a direction; the relationship
between nodes is bidirectional.
o Typically used to represent mutual relationships,
like friendships in social networks.
Directed Networks (Digraphs):
o Edges have a direction, indicating a one-way
relationship.
o Used for systems where direction matters, such as
road networks with one-way streets or citation
networks in academic research.

Nodes, Edges, and Attributes

In network theory, nodes and edges can have various attributes that
provide additional information:

 Nodes (Vertices):

o Attributes: Characteristics such as name,

location, type, or other relevant information.
o Example: In a transportation network, nodes could
represent intersections, with attributes like
coordinates and traffic signal timings.

 Edges (Links):

o Attributes: Properties such as length, travel time,

capacity, or cost.
o Example: In a road network, edges could represent
streets, with attributes like distance, speed limit,
and number of lanes.

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Understanding these fundamental components and their attributes is
essential for analyzing and interpreting networks effectively. By
applying graph theory, user can explore various network properties,
optimize routes, and solve complex spatial problems in GIS.

5.3 Data Preparation for Network Analysis

Effective network analysis in GIS begins with gathering high-quality

spatial data from reliable sources. Government agencies offer
comprehensive datasets, such as those for road networks, utility
lines, and public transit systems. Commercial providers supply
proprietary datasets with more detailed or specialized information.
Open data platforms like OpenStreetMap provide accessible
geospatial data for free. Remote sensing techniques, including
satellite imagery and aerial photography, are also valuable sources,
while field surveys provide precise and up-to-date data through
direct collection.

After data collection, cleaning and preprocessing are crucial to

resolve any errors or inconsistencies. This includes correcting errors
by identifying and fixing issues like missing values, duplicates, and
inaccuracies. It is important to standardize all data to the same
coordinate system and projection for consistency. Data integration
involves combining datasets from different sources to ensure spatial
and attribute alignment. Finally, building topology ensures that the
network's connectivity is accurately represented, verifying that roads
and intersections are properly depicted. Proper data preparation is
essential for conducting reliable and accurate network analysis in
GIS.

5.4 Network Data Models in GIS

In GIS, network data models are essential for representing spatial

networks effectively. The vector data model utilizes points, lines,
and polygons to depict nodes, linear features (such as roads and
utility lines), and areas, respectively. Each feature can have

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associated attributes like road length or capacity, making it ideal for
detailed representations in transportation, utility, and
communication networks.

Conversely, the raster data model employs a grid structure where

each cell holds specific values, suitable for environmental networks
like hydrological modelling. It excels in handling continuous data
over large areas but may lack precision for intricate linear features
compared to the vector model.

Topological data models emphasize spatial relationships and

connectivity between network elements, ensuring accurate
representation through defined nodes, edges, and rules governing
their interaction. These models are pivotal for tasks such as route
optimization and network tracing in fields like transportation
planning and utility management, where precise connectivity is
crucial. By maintaining proper connectivity and relationships, these
models enable robust analysis and decision-making in GIS
applications.

5.5 Types of Network Analysis

5.5.1 Shortest Path Analysis

Shortest path analysis calculates the most efficient route between

two points within a network, minimizing distance or travel time
based on predefined criteria. For example, in urban planning,
determining the shortest route from residential areas to hospitals can
significantly enhance emergency response times.

5.5.2 Service area analysis

Service area analysis identifies accessible areas from a specific

location within a defined travel time or distance. It aids in
delineating coverage areas for facilities like fire stations or
distribution centers. For instance, analyzing a 15-minute service area
around a retail store can evaluate its market reach and accessibility.

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5.5.3 Closest Facility Analysis

Closest facility analysis determines the nearest facility from

specified locations, crucial for tasks such as locating the nearest fire
station to residential areas or identifying the closest warehouse for
efficient logistics operations.

5.5.4 Origin-Destination (OD) Cost Matrix

The OD cost matrix computes travel costs or distances between

multiple origin-destination pairs within a network. It offers insights
into network connectivity and is useful for analyzing traffic flow
patterns and commuter behaviours. For instance, analyzing an OD
cost matrix can reveal commuting patterns between residential areas
and job centers.

5.5.5 Route Optimization

Route optimization identifies the optimal sequence of stops or

waypoints to minimize travel time, distance, or cost. It is essential
for logistics companies optimizing delivery routes or public transit
systems refining bus schedules. For example, optimizing delivery
routes can reduce operational costs and improve service efficiency.

These forms of network analysis play a pivotal role in GIS

applications across diverse sectors, providing critical insights that
optimize resource allocation, enhance service delivery, and improve
overall operational efficiency.

5.6 Algorithms and Techniques for Network Analysis

5.6.1 Dijkstra’s Algorithm

Dijkstra's algorithm stands as a fundamental method for determining

the shortest path between nodes within a graph characterized by non-
negative edge weights. It operates by iteratively selecting the node
with the smallest known distance and updating distances to

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neighbouring nodes accordingly. This approach finds extensive use
in applications necessitating shortest path computations, including
GPS navigation systems and network routing protocols.

Let's consider a simplified example where we have a small road

network in a GIS:

 Nodes: A, B, C, D
 Edges with weights (distances): A-B (5), A-C (10), B-C (3),
B-D (9), C-D (2)

Suppose we want to find the shortest path from node A to node D

using Dijkstra's algorithm:

1. Initialization: Start at node A with a distance of 0 and set

all other nodes' distances to infinity. Place node A in the
priority queue.

2. Iteration Steps:

o Visit node A, then explore its neighbors B and C.

o Calculate tentative distances:
 From A to B: 5 (current distance from A)
+ 5 (distance from A to B) = 10
 From A to C: 5 (current distance from A)
+ 10 (distance from A to C) = 15
o Update distances: Set B's distance to 10 and C's
distance to 15. Place B and C in the priority queue.

3. Next Node: Select node B (shortest distance in the queue).

Explore its neighbor C.

o Calculate tentative distance from A to C through

B: 10 (current distance to B) + 3 (distance from B
to C) = 13
o Since 13 is less than 15, update C's distance to 13.

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 4. Continue: Continue selecting the next node with the
shortest distance until all nodes are visited. In this example,
after processing all nodes, the shortest path from A to D
would be A -> B -> D with a total distance of 19.

In GIS, Dijkstra's algorithm is extensively used for tasks such as:

 Finding the shortest route for emergency vehicles or service

vehicles in transportation planning.
 Determining optimal paths for logistics and supply chain
management.
 Analyzing accessibility to amenities or services in urban
planning.

5.6.2 A* Algorithm

The A* algorithm integrates components from Dijkstra's approach

with heuristic strategies to effectively ascertain the shortest path in
graph structures. Employing a heuristic function that estimates the
cost from the current node to the goal, A* directs its search toward
the most promising paths. This algorithm proves particularly
advantageous in scenarios where accurate heuristic estimates are
available, such as in video game pathfinding or logistical route
planning.

Consider a scenario with the following network details:

 Nodes: A, B, C, D
 Edges with weights: A-B (5), A-C (10), B-C (3), B-D (9),
C-D (2)
 Heuristic (h-value): Euclidean distance from each node-
to-node D (destination).

Let's assume Node A serves as the starting point and Node D as the
destination:

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  Initialization: Begin at Node A with an initial heuristic
estimate (h-value) towards Node D.
 Priority Queue: Start the algorithm from Node A,
calculating f-values based on g-values (actual costs) and
heuristic estimates.
 Algorithm Execution: Expand nodes in order of priority
until reaching Node D.
 Path Reconstruction: Upon reaching Node D, reconstruct
the shortest path using stored parent pointers.

In this example, the A* algorithm efficiently determines the shortest

path from Node A to Node D by utilizing both actual costs (g-values)
and heuristic estimates (h-values). This approach ensures optimized
route planning in GIS applications, such as navigation systems and
logistics optimization, by guiding the search towards the most
promising paths based on heuristic predictions.

5.6.3 Floyd-Warshall Algorithm

The Floyd-Warshall algorithm serves to compute the shortest paths

between all pairs of nodes within a weighted graph. By
systematically considering all potential intermediate nodes, this
algorithm determines optimal paths. It finds applicability in
scenarios involving dense graphs or instances requiring
comprehensive all-pairs shortest path analyses, such as network
connectivity assessments and traffic management.

In GIS, the Floyd-Warshall algorithm is applied in various scenarios,

including:

 Calculating shortest paths in transportation networks.

 Analyzing connectivity and accessibility between
locations.
 Assessing network robustness and reliability in
infrastructure planning.

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5.6.4 Heuristics and Metaheuristics

Heuristics and metaheuristics represent problem-solving

methodologies utilized to approximate solutions in complex
optimization challenges where precise algorithms may be
impracticable. Heuristics rely on intuitive strategies or rules of
thumb to expediently identify solutions, whereas metaheuristics
employ advanced strategies like simulated annealing or genetic
algorithms to effectively explore and exploit search spaces. These
techniques find application across diverse network analysis tasks,
encompassing vehicle routing problems, facility location decisions,
and the optimization of network designs.

These algorithms and methodologies serve as integral components

of network analysis in GIS, facilitating efficient pathfinding,
comprehensive connectivity evaluations, and the optimization of
spatial networks across a broad spectrum of applications.

5.7 GIS Software and Tools for Network Analysis

Network Analyst Extension in ArcGIS

ArcGIS features a specialized toolset known as Network Analyst,

tailored for advanced spatial analysis based on network structures.
This extension offers several key functionalities:

1. Routing Analysis: Users can determine optimal routes

between locations, considering criteria like shortest path,
fastest route, or avoidance of specific obstacles.
2. Service Area Analysis: This tool enables the delineation
of service areas around locations based on travel time or
distance, crucial for assessing accessibility and service
coverage.
3. Location-Allocation Analysis: Facilitates strategic
placement of facilities to meet demand, accounting for
factors such as demand distribution and service capacities.

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 4. Network Dataset Management: Provides capabilities for
creating and managing network datasets, defining
connectivity rules, attributing network elements, and
ensuring data accuracy.

Plugins and Extensions in QGIS

QGIS extends its core functionality through various plugins and

extensions, enhancing capabilities particularly relevant to network
analysis:

1. QGIS Network Analysis Library (QNEAT3): This

plugin augments QGIS with tools for tasks such as shortest
path analysis, service area computations, and OD matrix
generation.
2. PgRouting: Integrates with QGIS to offer advanced
routing and network analysis using PostgreSQL and
PostGIS databases.
3. GRASS GIS Integration: QGIS seamlessly integrates
with GRASS GIS, providing additional tools for spatial
analysis and modelling, including tasks related to network
analysis.
4. Processing Toolbox: QGIS’s Processing Toolbox includes
algorithms dedicated to network analysis, such as shortest
path calculations and other specialized functions,
customizable to specific project needs.

These tools empower GIS professionals to conduct comprehensive

network analysis across various domains, from urban planning and
transportation logistics to environmental management and
infrastructure development.

5.8 Conclusion

Network analysis in GIS is vital for understanding spatial

relationships and optimizing resource use across sectors. Using

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graph theory and algorithms, GIS professionals’ model complex
networks of nodes and edges. Vector, raster, and topological data
models enable detailed representations of real-world networks like
transportation and utilities. Effective data preparation, including
collection, cleaning, and dataset creation, ensures accurate analyses.
Various analyses like shortest path calculations and route
optimization meet specific spatial planning needs. Algorithms such
as Dijkstra’s, A*, and Floyd-Warshall enhance capabilities for
pathfinding and connectivity. With ArcGIS and QGIS software and
their specialized tools, professionals conduct precise spatial
analyses, improving efficiency and resource management.

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 Chapter 6
Map Composition
Map composition is a process of preparing maps with incorporation
of thematic layers and their attributes, showing all map elements.
The elements are shown using conventional signs and symbols for
easy interpretation of the maps. This chapter explores the essential
components of map composition, guiding principles, and best
practices to ensure that maps are both functional and aesthetically
pleasing.

6.1 Basic Map Elements

There are some basic elements of map design that aid in map
interpretation. These map elements are title, scale, reference grid,
legend, projection, north arrow etc. The figure 6.1 shows some of
the important map elements.

Figure 6.1: Common map elements

(https://docs.qgis.org/3.34/en/docs/gentle_gis_introduction/map_producti
on.html)

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6.1.1 Map Title

A title of the map introduces the user to the map content and its
specific details, such as the study area, year of study, or variable
under consideration. A map title should be concise, informative, and
reflective of the map's content.

6.1.2 North Arrow

A north arrow shows the map’s orientation, helping users understand

directionality. It is especially important in navigation maps and
should be easily visible without being intrusive.

6.1.3 Scale

Scale is the ratio between the distance between two points on a map
and the actual distance on the ground. For instance, 1 cm of a map
drawn at 1:50000 scale represents 50,000 cm (0.5 km) distance on
the ground. A ratio of distance on map to actual distance on the
ground is also known as representative fraction. A map with high a
representative fraction is called as large-scale map. Conversely, a
map with a very small representative fraction is called a small-scale
map.
A scale can be represented in various ways, including a scale bar,
verbal statement, or numeric scale. Choosing the right scale is
essential for conveying the appropriate level of detail. The use of
different types of scale is given below.

Statement Scale: 1 cm = 2 km

(Interpretation: 1 cm of map is equal to 2 km on the ground)

Numeric Scale: 1:200,000

(Interpretation: 1 unit on the map is equal to 200,000 units on the

ground)

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Graphical Scale:

(Interpretation: Each division of graphical scale represents 7.5 km

distance on the ground)

The scale of a map determines the information it carries about the

features. For instance, a large-scale map shows a small area of earth
in great detail and small-scale map shows a larger area in less detail
(Figure 6.2). For example, the figure below shows three maps at
1:100,000 (small scale), 1:50,000 (medium scale), and 1:25,000
(large scale). The small-scale map shows a larger area compared to
medium and large-scale maps, but level of detail about blue feature
is more in the large-scale map.

Figure 6.2: A representation of feature details at three different scales.

6.2 Projection

A map projection determines how three-dimensional information is

translated to a two-dimensional plane. Including information about
the projection used is vital for understanding the spatial relationships
and potential distortions present (Figure 6.3).

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Figure 6.3: World in different projections (Mollweide Equal Area
projection on the left and Plate Carree Equidistant Cylindrical projection
on the right).

6.3 Reference Grid

The inclusion of a reference grid (or graticule) on a map helps in

identifying the spatial location of different features. A grid of
latitude and longitude or alpha-numeric codes is used as a reference
grid that subdivides the map into different regions for easy
interpretation (Figure 6.4).

Figure 6.4: Reference grids dividing the entire image area into 9 sub-
regions (Source: https://desktop.arcgis.com/en/arcmap/latest/map/page-
layouts/what-are-grids-and-graticules-.htm).

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6.4 Map Legend

A map is a simplified representation of the real-world features. A

map uses symbols to represent real-world objects. The map legend
explains the symbols, colors, and patterns used on the map. It is
essential for interpreting the data accurately (Figure 6.5). Legends
should be simple, uncluttered, and positioned where they can be
easily found without obstructing the map’s main features.

Figure 6.5: Crop map of Narayanpur command area with legend shown at
bottom of image (Source: https://bhuvan.nrsc.gov.in/nhp/webgis-
irrigation/map).

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Different symbols are used to represent natural and man-made
features, including point, linear, and polygon shape features. These
are also known as primary visual variables. The size of these shapes
and color intensity are often used to depict auxiliary properties of
the variables, which are called secondary visual variables.

6.5 Conclusion

Map composition involves preparing maps with thematic layers and

attributes, using conventional signs and symbols for easy
interpretation. Essential components and best practices ensure maps
are functional and aesthetically pleasing. Basic map elements
include title, north arrow, scale, projection, reference grid, and
legend. Each element aids in map interpretation, orientation, and
understanding of spatial relationships. Scale represents the ratio of
map distance to ground distance and can be displayed in various
formats. Map projections convert 3D information to 2D which is
crucial for accurate spatial representation. Map legend explains the
symbols, colors, and patterns on the map, essential for accurate data
interpretation. Legends should be simple and unobtrusive, aiding in
the easy understanding of map features.

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 Chapter 7
FOSS4G - Open-Source Tools & Techniques

7. 1 Introduction

Geospatial technology has advanced significantly, driven by the

necessity for sophisticated tools to manage complex spatial data and
analysis. A key development in this field is the Free and Open-
Source Software for Geospatial (FOSS4G) movement, which has
played a crucial role in making powerful geospatial tools accessible
to all and fostered a collaborative community. This chapter delves
into FOSS4G, examining its core principles, the variety of tools it
offers, and the innovative techniques it supports.

FOSS4G (https://foss4g.org/) includes a wide range of freely

available and open-source geospatial software, which users can
access, modify, and distribute without financial or licensing
restrictions. This open model not only lowers costs but also enhances
transparency and fosters innovation. Users can examine the source
code, tailor functionalities to their needs, and contribute to the
software’s development. The collaborative spirit of FOSS4G
communities accelerates the evolution of geospatial tools, ensuring
they stay cutting-edge and meet the diverse requirements of users
worldwide.

This chapter provides an overview of essential FOSS4G tools such

as QGIS, GRASS GIS, GeoServer, PostGIS, GDAL, and Leaflet,
detailing their features, capabilities, and applications. Each tool
addresses different aspects of geospatial analysis, from data
integration and management to spatial modelling and web mapping.
By understanding these tools, users can conduct comprehensive
spatial analyses, create interactive maps, and manage geospatial data
effectively and efficiently.

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The chapter also explores various techniques made possible by
FOSS4G tools, including data integration, spatial analysis, web
mapping, and automation. These techniques enable users to tackle
complex spatial challenges, streamline workflows, and extract
meaningful insights from geospatial data. Through case studies and
practical examples, the chapter demonstrates how FOSS4G tools
and techniques are utilized in real-world scenarios, highlighting their
impact in fields like urban planning, environmental management,
transportation, and public health.

With the growing demand for geospatial solutions, the importance

of FOSS4G continues to increase. By adopting open-source
geospatial tools and techniques, users can fully exploit spatial data,
contribute to the advancement of geospatial technology, and drive
innovation in their respective domains. This chapter aims to provide
readers with the knowledge and skills needed to effectively use
FOSS4G, fostering a deeper understanding of its benefits and
applications in the geospatial landscape.

7.2 Key components & benefits of FOSS4G

FOSS4G features several key components that have led to its

widespread adoption and effectiveness. One of its major advantages
is its accessibility and cost-effectiveness; FOSS4G tools are free,
making them accessible to individuals, organizations, and
governments regardless of their budget. Additionally, the vibrant
global community offers extensive documentation, forums, and
development resources, making these tools easy to use and
troubleshoot. Key benefits of FOSS4G are:

1. Transparency and customizability are central to

FOSS4G's appeal. The availability of source code allows
users to view and modify the software, tailoring it to
specific needs and enhancing transparency. Organizations

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 can also develop custom plugins or extensions, improving
functionality without dependence on proprietary vendors.
2. Interoperability and standards compliance are critical
elements, with many FOSS4G tools adhering to
international standards set by organizations like the Open
Geospatial Consortium (OGC). This compliance ensures
compatibility with other software and data formats,
allowing seamless integration with other systems for
efficient data exchange and workflow integration.
3. FOSS4G provides a diverse toolset that caters to various
aspects of geospatial analysis. Tools like QGIS for desktop
GIS, GRASS GIS for advanced spatial modeling,
GeoServer for web mapping, PostGIS for spatial databases,
GDAL for data processing, and Leaflet for interactive web
maps cover all stages of geospatial data handling, from
acquisition and management to analysis and visualization.
4. The benefits of FOSS4G are extensive. One of the primary
advantages is cost savings, as FOSS4G eliminates the need
for expensive licensing fees, significantly reducing the cost
of acquiring and maintaining geospatial software. Its open-
source nature also fosters collaborative development,
distributing costs and efforts across a wider community.
5. FOSS4G promotes innovation and collaboration through
its global community of developers and users, who
contribute to continuous improvement and ensure that the
tools remain at the forefront of geospatial technology. The
open-source model allows for rapid iteration and the
inclusion of new features based on user feedback and
emerging needs.
6. Flexibility and control are additional benefits. Users can
modify and extend the software to meet their specific
requirements, offering greater control over the
functionality and performance of their geospatial tools.

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 This independence from a single vendor reduces the risk of
obsolescence and ensures long-term access and usability.
7. Community support is crucial, with an active user and
developer base providing a wealth of knowledge,
resources, and assistance, simplifying the learning and
troubleshooting processes. Open forums, user groups, and
conferences facilitate the exchange of ideas and best
practices, enhancing overall expertise within the
community.
8. Enhanced data sharing and integration are made
possible through compliance with international standards,
ensuring that FOSS4G tools can easily integrate with other
systems and promote data sharing and collaborative
projects. The widespread use of FOSS4G tools across
various industries and sectors encourages the development
of interoperable solutions and datasets.
9. Sustainability and longevity are supported by the open-
source model, which fosters sustainable software
development practices. The community can continue to
develop and maintain the software independently of
commercial interests. The availability of source code
ensures that FOSS4G tools can be maintained and updated
over the long term, even if the original developers move on.

7.3 Prominent Open-Source Geospatial Tools

7.3.1 QGIS (Quantum GIS)

QGIS is a user-friendly, open-source desktop GIS application

designed for viewing, editing, and analyzing geospatial data (Figure.
7.1). It boasts advanced analysis capabilities, plugin support, and
compatibility with various data formats. QGIS is widely used in
urban planning, environmental management, resource mapping, and
other fields. QGIS is designed with an intuitive, user-friendly
interface, making it accessible to both beginners and advanced users.

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It is compatible with multiple operating systems, including
Windows, macOS, Linux, and Android. QGIS supports a wide range
of raster and vector data formats, such as shapefiles, GeoTIFF,
PostGIS layers, and more. The software provides powerful tools for
spatial analysis, geoprocessing, and advanced cartography, enabling
users to perform buffer analysis, overlay analysis, network analysis,
and additional tasks. QGIS features a robust plugin architecture,
allowing users to extend its functionality through the QGIS Plugin
Repository, which hosts a variety of plugins for diverse geospatial
tasks. It offers comprehensive tools for managing and manipulating
geospatial data, including attribute table management, data
import/export, and database integration. Advanced cartographic
tools are included for creating high-quality maps, with options to
design and print maps featuring customized layouts, symbols, and
labels.

Figure 7.1: QGIS homepage (https://www.qgis.org/en/site/).

The software includes georeferencing tools essential for integrating

scanned maps and aerial imagery into GIS projects. QGIS provides
extensive visualization options, including thematic mapping, 3D
visualization, and temporal data animation. As open-source software

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released under the GNU General Public License, QGIS is free to use,
modify, and distribute.

A strong, active community of users and developers offers extensive

support through forums, mailing lists, and online resources. QGIS
can also be integrated with other geospatial software and tools, such
as GRASS GIS, GDAL, and SAGA GIS, enhancing its functionality.
These features make QGIS a versatile and powerful tool suitable for
a wide range of geospatial applications, from simple mapping to
complex spatial analysis and geospatial data management.

7.3.2 GRASS GIS (Geographic Resources Analysis Support

System)

GRASS GIS, acronym for Geographic Resources Analysis Support

System, stands out as a robust open-source GIS platform celebrated
for its extensive analytical capabilities spanning raster, vector, and
geospatial data (Figure. 7.2). Equipped with a comprehensive suite
of tools encompassing spatial modeling, geostatistics, and image
processing, it emerges as a pivotal resource for in-depth geospatial
analysis. Offering support for advanced functionalities like 3D
visualization, topological vector analysis, and temporal data
processing, GRASS GIS proves indispensable for sectors ranging
from environmental management and urban planning to scientific
research. Its modular architecture empowers users to execute
intricate operations seamlessly via an intuitive graphical user
interface or command line, thereby enabling a heightened level of
customization and automation. With its capacity to handle
substantial datasets and execute complex geospatial computations,
GRASS GIS emerges as a potent asset for addressing a diverse array
of spatial challenges and endeavours.

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Figure 7.2: GRASS GIS (https://grass.osgeo.org/).

7.3.3 GeoServer

GeoServer is a robust open-source server application designed to

share geospatial data and services seamlessly across the web (Figure
7.3). It provides a diverse range of functionalities and services that
support efficient data dissemination, spatial analysis, and web
mapping.

Functionality: Main functionalities of GeoServer are:

1. OGC Standards Compliance: GeoServer conforms to

Open Geospatial Consortium (OGC) standards like Web
Map Service (WMS), Web Feature Service (WFS), and
Web Coverage Service (WCS), ensuring compatibility with
other geospatial systems.
2. Data Publishing: It simplifies the publication of geospatial
data in various formats, including raster and vector data
such as Shapefile, GeoTIFF, and PostGIS, facilitating
smooth integration with GIS software.
3. Data Styling and Symbology: Users can customize the
appearance of geospatial data layers within GeoServer,
allowing the creation of visually appealing maps and

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 cartographic outputs by adjusting colors, labels, and
symbology.
4. Raster and Vector Data Processing: GeoServer provides
tools for processing raster and vector data, enabling real-
time data transformation, reprojection, and spatial analysis
to ensure geospatial data can be served in desired formats
and projections.
5. Security and Access Control: Robust security features are
available in GeoServer, allowing administrators to manage
access to data and services based on user roles and
permissions, ensuring data integrity and confidentiality in
multi-user environments.

Figure 7.3: GeoServer homepage (https://geoserver.org/).

Services: Important services provided by GeoServer are-

1. Web Mapping: As a foundation for web mapping

applications, it empowers users to visualize geospatial data
through interactive maps embedded in web pages,
facilitating the creation of dynamic, responsive maps
accessible from any web-enabled device.

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 2. Data Dissemination: GeoServer streamlines the
dissemination of geospatial data to a broad audience by
offering standardized web services for data access,
enabling users to access and download geospatial data in
various formats for offline analysis or integration into GIS
workflows.
3. Spatial Data Infrastructure (SDI) Development: Serving
as a cornerstone for Spatial Data Infrastructures (SDIs),
GeoServer supports data sharing and interoperability,
enabling organizations to publish and share geospatial data
internally and externally, fostering collaboration and
informed decision-making.
4. Geospatial Analysis: With support for OGC standards and
robust data processing capabilities, GeoServer facilitates
geospatial analysis workflows by providing access to
geospatial data layers and services, allowing users to
perform tasks like buffering, overlay analysis, and spatial
querying directly through web-based interfaces.

7.3.4 PostGIS

PostGIS is a powerful extension for the PostgreSQL database,

designed to enable the storage, management, and analysis of spatial
data. By enhancing PostgreSQL's capabilities to include geographic
objects, PostGIS allows for the direct storage of spatial features such
as points, lines, and polygons within the database. This is supported
by a range of spatial data types, including GEOMETRY and
GEOGRAPHY, accommodating different coordinate systems and
spatial reference models. A key feature of PostGIS (Figure 7.4) is its
ability to perform complex spatial queries using standard SQL,
facilitating operations such as finding intersections, calculating
distances, and querying spatial relationships. Furthermore, the use
of R-tree and GiST (Generalized Search Tree) indexing ensures
efficient spatial querying, delivering rapid performance even with
large datasets.

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In addition to basic spatial data storage and querying, PostGIS
provides a comprehensive suite of geometric functions that enable
users to conduct detailed spatial analyses, such as buffering, union,
and convex hull calculations. It also supports raster data
management, essential for handling satellite imagery and digital
elevation models (DEMs). Adherence to Open Geospatial
Consortium (OGC) standards ensures seamless integration with
other geospatial tools and applications, including QGIS, GeoServer,
and MapServer. This interoperability, combined with PostgreSQL's
scalability and robustness, makes PostGIS a formidable tool for
managing large-scale spatial databases. The extension also includes
topological functions for maintaining and analyzing spatial
relationships, support for various spatial reference systems, and
parallel processing capabilities, all of which enhance its
performance and usability for complex geospatial data management
and analysis tasks.

Figure 7.4: PostGIS homepage (https://postgis.net/).

PostGIS is an extension to the PostgreSQL database that adds

support for geographic objects. It features spatial queries, spatial
indexing, and geospatial data storage and management. PostGIS is

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used in spatial databases, geographic information systems, and web
mapping applications.

7.3.5 PostgreSQL

PostgreSQL, often referred to as Postgres, is a sophisticated open-

source relational database management system (RDBMS). Known
for its robustness, high performance, and adherence to SQL
standards, PostgreSQL (https://www.postgresql.org/) is utilized
across a wide range of applications, from web services to data
warehousing. It guarantees reliable transactions through ACID
compliance, ensuring Atomicity, Consistency, Isolation, and
Durability. PostgreSQL supports a diverse array of data types,
including JSON, XML, hstore, and arrays, and offers powerful full-
text search capabilities for advanced querying. The system allows
users to create custom functions, operators, and data types, and
employs Multi-Version Concurrency Control (MVCC) to manage
multiple transactions simultaneously without conflict. It also
features streaming replication and robust backup solutions for high
availability and data integrity, and with extensions like PostGIS, it
can handle and query spatial data.

Figure 7.5: PGADMIN of PostgreSQL.

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A major advantage of PostgreSQL is its open-source nature, making
it free to use, modify, and distribute, thus providing cost savings and
flexibility. It boasts a proven track record of stability and data
integrity, efficient query optimization, and indexing for high
performance, and SQL standards compliance for compatibility and
ease of use. PostgreSQL's extensibility is evident with its wide range
of plugins and extensions (Figure 7.5), supported by a large, active
community offering extensive documentation and support. It
operates on various systems, including Windows, macOS, and
Linux. However, PostgreSQL can be complex to configure and
optimize for peak performance, requiring advanced database
administration skills, and it has a steeper learning curve compared to
some other RDBMS, especially for new users. High-performance
applications may demand substantial hardware resources, and while
community support is robust, commercial support options are more
limited compared to proprietary databases like Oracle or Microsoft
SQL Server. Additionally, some third-party tools and applications
might offer limited support or integration relative to more widely
adopted commercial databases. In summary, PostgreSQL is a
powerful and versatile RDBMS with extensive functionality and
numerous benefits, particularly in terms of cost, performance, and
extensibility, though it requires significant resources and expertise
for effective management in high-performance environments.

7.3.6 GDAL

The Geospatial Data Abstraction Library (GDAL) is an open-source

library designed for managing geospatial data formats, offering a
broad range of functionalities and advantages for users working with
geographic information systems (GIS). GDAL supports numerous
raster and vector data formats, including GeoTIFF, JPEG, PNG,
shapefiles, KML, and PostGIS, among others. This extensive format
support allows for seamless conversion between different geospatial
data formats, promoting interoperability and data sharing across
various GIS applications. GDAL (https://gdal.org/index.html)

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provides robust tools for geospatial data processing, including image
reprojection, resampling (like nearest neighbour resampling),
mosaicking, subsetting, and layer translation. It also supports
advanced spatial operations such as raster algebra, contour
generation, and vector data rasterization. The library includes a suite
of command-line tools for data inspection, transformation, and
processing, which are highly useful for batch processing and
automation. Additionally, GDAL offers APIs for various
programming languages, including C++, Python, and Java, allowing
developers to integrate geospatial data handling capabilities into
custom applications. As an open-source library, GDAL is freely
available, reducing costs associated with proprietary geospatial data
handling solutions. Its extensive format support and data translation
capabilities ensure compatibility with almost any geospatial data
source, promoting interoperability across different systems and
applications. GDAL benefits from a large, active user and developer
community, providing extensive documentation, forums, and user-
contributed code examples. Users can tailor GDAL to meet specific
needs through its programmable APIs, making it adaptable for a
wide range of geospatial tasks. Designed for efficient data
processing, GDAL can handle large datasets and complex spatial
operations, making it suitable for both desktop and server
environments. It integrates well with other open-source GIS
software such as QGIS, GRASS GIS, and MapServer, enhancing its
utility within a broader geospatial data ecosystem. In summary,
GDAL is a powerful, versatile tool essential for geospatial data
management and processing, offering extensive functionalities and
significant advantages, particularly in terms of format support,
interoperability, and cost-effectiveness.

7.3.7 Openlayers

OpenLayers is an open-source JavaScript library widely used for

displaying maps and building interactive web mapping applications.
This versatile tool offers numerous advantages, making it a popular

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choice among developers and organizations. Being open source and
freely available under a permissive license, OpenLayers is cost-
effective. It supports a wide range of map sources, including
OpenStreetMap, Google Maps, Bing Maps, and custom tile servers,
which provides flexibility in map presentation. Its rich feature set
includes extensive tools for creating interactive maps, such as layer
management, vector drawing, and spatial analysis. The library is
highly customizable and extensible due to its modular architecture
and comprehensive API, enabling developers to tailor it to specific
requirements. Additionally, OpenLayers (Figure 7.6) works
seamlessly across major web browsers, ensuring a consistent user
experience. The active community of developers continuously
contributes to its improvement, and there is extensive documentation
and tutorials available to support users.

Figure 7.6: Openlayers home page (https://openlayers.org/).

However, OpenLayers also has its limitations. Rendering large

datasets or complex maps can lead to performance issues,
particularly in resource-constrained environments. The extensive
functionality and flexibility of OpenLayers come with a steep
learning curve, making it challenging for beginners to master
quickly. While it is usable on mobile devices, it may not perform as
well or be as responsive as some mobile-focused mapping libraries.

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Managing dependencies and ensuring compatibility with other
frameworks can sometimes be challenging, as with many JavaScript
libraries. Additionally, OpenLayers has limited native support for
3D visualization compared to some other mapping libraries like
CesiumJS, which may be a drawback for applications requiring 3D
maps. In summary, OpenLayers is a powerful tool for building
interactive web maps, offering significant advantages in terms of
being open-source, highly customizable, and compatible with
various map sources. Nonetheless, it also presents challenges such
as performance concerns, a steep learning curve, and less
optimization for mobile and 3D applications.

7.4 Conclusion

The chapter on "FOSS4G - Open-Source Tools & Techniques"

provides an in-depth examination of Free and Open-Source Software
for Geospatial (FOSS4G), elucidating its core principles,
functionalities, and advantages. It elucidates how FOSS4G plays a
pivotal role in widening access to potent geospatial tools, fostering
collaboration, and stimulating innovation within the geospatial
community. Central to the chapter are the key components of
FOSS4G, including QGIS, GRASS GIS, GeoServer, PostGIS,
GDAL, and Leaflet, each highlighted for their unique features,
capabilities, and applications across various sectors like urban
planning and environmental management. Moreover, the chapter
explores the diverse techniques enabled by FOSS4G, encompassing
data integration, spatial analysis, web mapping, and automation,
effectively demonstrating their practical utility through real-world
case studies and examples. Ultimately, the chapter underscores the
indispensable contribution of FOSS4G in propelling advancements
in geospatial technology, empowering users, and facilitating
solutions to intricate spatial challenges spanning multiple domains.

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 Chapter 8
Global Navigation Satellite System (GNSS)
8.1 Introduction

Global Navigation Satellite Systems (GNSS) are a collection of

satellite constellations that provide global coverage for positioning,
navigation, and timing (PNT) services. These systems transmit
signals from space, which GNSS receivers use to determine their
exact location (latitude, longitude, and altitude) anywhere on Earth,
at any time, and in any weather conditions. GNSS encompasses
several satellite systems operated by different countries or entities,
each contributing to the global PNT infrastructure.

GNSS technology has a multitude of uses that span across various

industries and everyday life. In transportation, GNSS is essential for
navigation, providing real-time directions and traffic updates for
drivers, as well as guiding ships and aircraft with precision. In
emergency response and disaster management, GNSS enables
efficient search and rescue operations, helps coordinate relief efforts,
and tracks the movement of emergency vehicles. The agriculture
sector utilizes GNSS for precision farming. GNSS plays a critical
role in geospatial and surveying activities, offering accurate location
data for mapping, construction, and land surveying.

8.2 Principle behind GNSS

Global Navigation Satellite Systems (GNSS) work on the principle

of trilateration (Figure 8.1), which involves determining a position
based on the distances from multiple reference points. In the context
of GNSS, these reference points are satellites in space.

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Figure 8.1: Shows trilateration from GNSS satellites.

GNSS relies on a constellation of satellites orbiting the Earth (Figure

8.1). Each satellite continuously transmits signals that include its
location and the precise time the signal was sent. A GNSS receiver
on the ground (or in the air or at sea) picks up these signals. By
calculating the time, it takes for the signals to travel from the satellite
to the receiver, the distance from each satellite to the receiver can be
determined. This is known as the time-of-flight or time-delay
measurement. The distance 𝐷 from a satellite to the receiver is
calculated using the formula:

D= 𝑐 × 𝑡

where c is the speed of light (approximately 299,792 kilometres per

second) and 𝑡 is the time delay.

To determine its exact position, a receiver needs signals from at least

four satellites. Three satellites are required for calculating the
receiver's position in three-dimensional space (latitude, longitude,

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and altitude). A fourth satellite is needed to correct the receiver’s
clock error. GNSS receivers do not have atomic clocks like the
satellites; thus, they need this additional measurement to precisely
synchronize their internal clocks with the satellite clocks.

8.3 GNSS Bands

GNSS satellites transmit signals on specific frequency bands that are

divided into multiple channels. Each band serves a unique purpose
and offers different advantages. Each band plays a crucial role in
ensuring the accuracy, reliability, and efficiency of GNSS services.
The frequency bands used by GNSS systems include L1, L2, L5, and
other higher frequencies. These bands are carefully selected to
balance factors such as signal strength, atmospheric interference,
and the need for interoperability between different GNSS systems.
Most commonly used GNSS bands are as follows:

1. L1 Band (1575.42 MHz)-

Usage: The L1 band is the primary frequency used by most GNSS

receivers. It carries the standard positioning service (SPS) signals,
which are freely available to civilian users.

Advantages: Signals in the L1 band are less affected by ionospheric

delays compared to lower frequency bands. This band is widely
supported by all major GNSS systems, including GPS, GLONASS,
Galileo, and BeiDou, ensuring broad compatibility and availability.

Applications: Commonly used in consumer-grade devices such as

smartphones, car navigation systems, and handheld GPS units.

2. L2 Band (1227.60 MHz)-

Usage: The L2 band transmits the precise positioning service (PPS)

signals, primarily intended for military and authorized users.
However, civilian signals such as L2C (a modernized civilian signal)
are also available.

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Advantages: L2 signals, when combined with L1 signals, allow for
more accurate correction of ionospheric errors. This dual-frequency
capability improves positioning accuracy.

Applications: Used in high-precision applications such as surveying,

geodesy, and scientific research.

3. L5 Band (1176.45 MHz)-

Usage: The L5 band is designated for safety-of-life applications and

is used by modernized GNSS systems to provide enhanced accuracy
and reliability.

Advantages: Signals in the L5 band have a higher power level and a

wider bandwidth, which improves signal robustness and accuracy.
They are also less prone to multipath errors and interference.

Applications: Critical for aviation, maritime navigation, and other

safety-of-life services where precision and reliability are paramount.

4. Other Bands (E1, E6, B1, B2, etc.)-

Usage: Different GNSS systems use additional frequency bands to

provide specific services and enhance overall system performance.
For example, Galileo uses E1, E5, and E6 bands, while BeiDou
employs B1, B2, and B3 bands.

Advantages: These additional bands support advanced services,

improve interoperability between systems, and offer redundancy,
ensuring continuous availability of positioning services.

Applications: Used in a variety of specialized applications, including

scientific research, high-precision surveying, and augmented GNSS
services.

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8.4 Sources of error in GNSS

Global Navigation Satellite Systems are highly reliable and accurate,

but various sources of error can affect the precision of the
positioning data they provide. Understanding these sources of error
is crucial for improving GNSS performance and developing
techniques to mitigate them. The primary sources of error in GNSS
are as follows:

1. Satellite Clock Errors

Each GNSS satellite is equipped with highly accurate atomic clocks.
However, even these clocks can experience slight deviations from
the true time, leading to errors in the signals transmitted by the
satellites. These errors can affect the accuracy of the calculated
position, as the receiver uses the time of signal transmission to
determine distance from the satellite.
2. Ephemeris Errors
Ephemeris errors, also known as orbital errors, occur due to
inaccuracies in the satellite's reported position. GNSS satellites orbit
the Earth, and their exact positions are calculated and transmitted in
the navigation message. Any errors in these calculations can lead to
incorrect positioning data.
3. Ionospheric Delays
The ionosphere is a layer of the Earth's atmosphere that is ionized
by solar radiation. GNSS signals passing through the ionosphere are
delayed due to the varying density of charged particles. This delay
is frequency-dependent, meaning signals on different frequencies
experience different delays. Dual-frequency receivers can measure
and correct for this error by comparing the delays of signals at
different frequencies.
4. Tropospheric Delays
The troposphere is the lowest layer of the Earth's atmosphere, and it
contains water vapor and other particulates. GNSS signals slow
down when passing through the troposphere, leading to delays.
Unlike ionospheric delays, tropospheric delays are not frequency-

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dependent, making them harder to correct. Models and real-time
data are used to estimate and mitigate these errors.
5. Multipath Effects
Multipath effects occur when GNSS signals reflect off surfaces such
as buildings, water bodies, and the ground before reaching the
receiver. These reflected signals can interfere with the direct signals,
causing errors in the calculated position. This is particularly
problematic in urban environments with many reflective surfaces.
6. Receiver Noise
GNSS receivers themselves can introduce errors due to internal
noise and inaccuracies in signal processing. Thermal noise,
electronic interference, and imperfections in the receiver's hardware
can all contribute to positioning errors. High-quality receivers with
advanced signal processing algorithms can reduce the impact of
receiver noise.
7. User Equivalent Range Error (UERE)
UERE encompasses several factors, including satellite clock errors,
ephemeris errors, and atmospheric delays, that contribute to the
overall error in the distance measurement between the satellite and
the receiver. It provides a comprehensive measure of the
uncertainties affecting the range measurements used to compute the
receiver's position.

8.5 Mitigation Techniques

Several techniques and technologies are employed to mitigate these

errors and improve GNSS accuracy like:

1. Differential GNSS (DGNSS): This technique uses a

network of ground-based reference stations to provide
corrections for GNSS errors. These stations calculate the
errors in the GNSS signals and broadcast correction data to
nearby GNSS receivers.
2. Satellite-Based Augmentation Systems (SBAS): Systems
such as WAAS (Wide Area Augmentation System) in the

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 U.S., EGNOS (European Geostationary Navigation
Overlay Service) in Europe, and MSAS (Multi-functional
Satellite Augmentation System) in Japan provide
additional correction signals from geostationary satellites,
enhancing GNSS accuracy and integrity.
3. Real-Time Kinematic (RTK): RTK is a high-precision
technique that uses carrier phase measurements and
corrections from a nearby base station to achieve
centimeter-level accuracy. It is widely used in surveying,
agriculture, and other applications requiring precise
positioning.
4. Advanced Receiver Technologies: Modern GNSS
receivers use advanced algorithms and signal processing
techniques to mitigate errors. These include multipath
mitigation, enhanced ionospheric and tropospheric models,
and improved clock error corrections.

8.6 Major GNSS Systems

There are four primary GNSS systems operational today:

1. GPS (Global Positioning System): It is operated by the

United States Department of Defense and GPS is the most
widely recognized and used GNSS. It became fully
operational in 1995 and consists of a constellation of at
least 24 satellites in medium Earth orbit (MEO). It uses L1,
L2 and L5 bands for operations. GPS has global coverage
and provides accurate positioning anywhere on Earth. GPS
offers a Standard Positioning Service (SPS) for civilian use
and a Precise Positioning Service (PPS) for military and
authorized users. GPS has major applications in navigation,
surveying, agriculture, aviation, and more.
2. GLONASS (Global Navigation Satellite System): It is
operated by Russian Federation Government. GLONASS
is Russia's GNSS, which became fully operational in 1996.

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 The system comprises a constellation of 24 satellites in
MEO. It uses L1, L2 and L3 bands for operations. It also
has global coverage comparable to GPS. It has better
performance in high latitude regions particularly in
northern latitudes due to its orbital design.
3. Galileo: It is operated by European Union through the
European Space Agency (ESA). Galileo is Europe's GNSS,
which aims to provide an independent high-precision
positioning system. It became operational in 2016. It uses
E1, E5a, E5b and E6 bands for operations. Galileo offers
higher precision positioning compared to GPS and
GLONASS. Provides free services for civilian use and
encrypted services for governmental applications. It is
designed to be interoperable with other GNSS systems.
4. BeiDou (BDS - BeiDou Navigation Satellite System): It
is operated by China National Space Administration
(CNSA). BeiDou, also known as BDS, is China's GNSS.
The system started regional service in 2000 and achieved
global coverage in 2020. It uses B1, B2 and B3 bands for
operations. It provides both regional (Asia-Pacific) and
global services. It has a unique feature allowing two-way
communication for short messages.

8.7 Regional GNSS Systems

Other than major GNSS systems, regionals GNSS systems that cover
a specific region on Earth are alos available. Major ones are as
follows:

1. QZSS (Quasi-Zenith Satellite System): It is operated by

Japan. It has enhanced GNSS coverage and accuracy in the
Asia-Oceania region. It complements GPS and provides
additional services tailored to regional needs.
 NavIC (Navigation with Indian Constellation): Indian
Regional Navigation Satellite System (IRNSS) or NavIC

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 (operational name) is a regional GNSS system operated by
the Indian Space Research Organisation. It provides precise
positioning services to users in India and the surrounding
region (about 1,500 km around it). Plans are to further
extent this range up to 3,000 Km. IRNSS constellation
consists of 8 satellites in space, along with two satellites on
ground as stand-by. IRNSS has “standard positioning
service” for civilian use and “restricted service” with
encryption for defence purposes and both the services use
L5 (1176.45 MHz) and S band (2492.028 MHz) for
operations. The constellation is operational in space in
since 2018.

8.8 Conclusion

Global Navigation Satellite Systems are indispensable in the modern

world due to their ability to provide accurate and reliable
positioning, navigation, and timing services. GNSS have vast
number of applications ranging from transportation, agriculture,
emergency response, telecommunications, and scientific research to
autonomous systems.

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 Chapter 9
AI Applications in Remote Sensing and GIS
Artificial Intelligence (AI) has revolutionized numerous industries,
and its impact on remote sensing and Geographic Information
Systems (GIS) is profound. By integrating AI with these
technologies, a new potential for data analysis, pattern recognition,
predictive modeling, and decision-making has been unlocked. This
chapter explores how AI enhances remote sensing and GIS
applications, focusing on various techniques, case studies, and future
trends.

9.1 AI in Remote Sensing

Remote sensing involves acquiring information about the Earth's

surface without direct contact, typically through satellites or aerial
imagery. AI algorithms enhance remote sensing by improving image
analysis, feature extraction, and image classification.

9.1.1 Image Classification

One of the primary applications of AI in remote sensing is image

classification. The machine learning algorithms such as random
forest, support vector machine, XGBoost, and neural networks are
capable enough to perform Level-1 land use and land cover tasks.
Similarly, Deep Learning based methods such as Convolutional
Neural Networks (CNNs) excel in recognizing patterns and
categorizing images. These models can classify land cover types
(forests, urban areas, water bodies) with high accuracy by analyzing
spectral signatures in satellite imagery. For instance, Figure 9.1
shows the Level-I classification of Delhi city using the Random
Forest algorithm on Sentinel-2 data. The red color represents built-
up area, blue represents water, dark green shows naturally vegetated
areas, yellow represents vacant or barren land, bright green

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represents agriculture, and light green represents grassland or
wastelands.

Figure 9.1: Level-1 Classification of Delhi city using Random Forest

Algorithm.

9.1.2 Change Detection

ML and DL methods also facilitates change detection in remote

sensing. Recurrent Neural Networks (RNNs) and Long Short-Term
Memory (LSTM) networks, including architectures like GRU
(Gated Recurrent Unit) and Bi-LSTM (Bidirectional LSTM), are
particularly effective in identifying temporal changes in sequential
data. By comparing time-series images, these models can detect
changes in land use, vegetation growth, urban expansion, and
environmental degradation.

1. Object Detection: AI-driven object detection models such

as YOLO (You Only Look Once) (Fig. 9.2), Faster R-CNN,
SSD (Single Shot MultiBox Detector), and RetinaNet are
utilized to identify and locate specific objects within remote
sensing imagery. Applications include detecting ships in
maritime surveillance, vehicles in traffic monitoring, and
buildings in urban planning.

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Figure 9. 2. Building detection in Cartosat-3 data in parts of Delhi using
Yolov8 algorithm.

9.1.3 Hyperspectral and Multispectral Analysis

Remote sensing often involves hyperspectral and multispectral

imaging, capturing data across various wavelengths. AI algorithms,
particularly deep learning models like 3D-CNNs (Three-
Dimensional Convolutional Neural Networks) and hybrid CNN-
RNN architectures, can analyze these complex datasets to identify
materials and assess their properties. This capability is vital for
applications like mineral exploration, agriculture monitoring, and
environmental assessment.

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 9.2 AI in GIS

Geographic Information Systems (GIS) involve the storage,

analysis, and visualization of spatial data. AI enhances GIS by
enabling advanced spatial data analysis, predictive modeling, and
decision support systems.

1. Spatial Data Analysis: AI techniques such as K-means

clustering, DBSCAN (Density-Based Spatial Clustering of
Applications with Noise), and Random Forest
classification help analyze spatial data more effectively.
For example, clustering algorithms can group similar
geographic features, aiding in regional planning and
resource allocation. Classification algorithms can
categorize land parcels based on usage, improving land
management practices.
2. Predictive Modeling: AI-driven predictive modeling in
GIS helps forecast future spatial patterns and trends.
Machine learning models such as Gradient Boosting
Machines (GBMs), XGBoost, and LightGBM can predict
urban growth, environmental changes, and disaster risks.
For instance, predictive models can simulate flood risks
based on historical data and climate projections, assisting
in disaster preparedness and mitigation.
3. Route Optimization: AI algorithms optimize routing and
logistics in GIS applications. Techniques like Genetic
Algorithms (GAs), Ant Colony Optimization (ACO), and
Reinforcement Learning models such as Q-Learning and
Deep Q-Networks (DQN) find the most efficient paths for
transportation and delivery services. These models
consider various factors such as traffic conditions, road
networks, and delivery constraints to minimize travel time
and costs.
4. Smart Cities and IoT Integration: In the context of smart
cities, AI and GIS integration is crucial. AI processes data

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 from Internet of Things (IoT) devices, such as sensors and
cameras, using models like Long Short-Term Memory
(LSTM) networks and Autoencoders to monitor urban
environments in real-time. Applications include traffic
management, air quality monitoring, and infrastructure
maintenance. GIS platforms visualize this data, providing
insights for urban planners and policymakers.

9.3 Case Studies

1. Precision Agriculture: AI in remote sensing and GIS plays

a pivotal role in precision agriculture. By analyzing satellite
imagery and sensor data with models such as Convolutional
Neural Networks (CNNs) and Support Vector Machines
(SVMs), AI can assess crop health, soil moisture, and pest
infestations. GIS integrates these insights with spatial data,
enabling farmers to make informed decisions about
irrigation, fertilization, and pest control. This approach
enhances crop yields and resource efficiency.
2. Disaster Management: AI and GIS are instrumental in
disaster management. During natural disasters like
hurricanes and earthquakes, AI models like UNet and
SegNet analyze satellite imagery to assess damage and
identify affected areas. GIS platforms visualize this data,
guiding emergency response teams in resource allocation
and rescue operations. Predictive models like Random
Forests and Gradient Boosting Machines (GBMs) also
forecast disaster impacts, aiding in preparedness and risk
reduction.
3. Environmental Monitoring: Environmental monitoring
benefits significantly from AI and GIS integration. AI
algorithms such as Random Forests, k-Nearest Neighbors
(k-NN), and Gradient Boosting Machines (GBMs) analyze
remote sensing data to monitor deforestation, water quality,
and pollution levels. GIS platforms map these changes,

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 providing a spatial context for environmental policies and
conservation efforts. For instance, AI-driven models can
detect illegal logging activities, enabling timely
interventions.
4. Urban Planning: Urban planning leverages AI and GIS for
sustainable development. AI models like Support Vector
Machines (SVMs), Decision Trees, and Neural Networks
analyze population growth, land use patterns, and
infrastructure needs. GIS visualizes these data layers,
supporting urban planners in designing efficient
transportation networks, green spaces, and residential
areas. This integrated approach promotes balanced urban
growth and resource management.

9.4 Conclusion

AI has significantly transformed remote sensing and GIS, offering

advanced capabilities for data analysis, predictive modeling, and
decision support. The integration of AI with these technologies
enables more accurate, efficient, and insightful analyses of spatial
data, addressing various challenges in agriculture, disaster
management, environmental monitoring, and urban planning. As AI
algorithms and big data technologies continue to evolve, the future
holds immense potential for further advancements and applications
in remote sensing and GIS, contributing to sustainable development.

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 Glossary
A I
Analysis p1, Aspect p25, Attribute Identify p8, Image p23,
p4, Attribute Table p15, Interpolation p36, Intersect p26.
Automation p71, Azimuth p26.
B J
Band p76, Buffer p18. Join p18

C K
Class p26, Contour p39, Key attributes p17, Kriging p 36
Coordinate System p20, Coverage
p6, Cylindrical Projection p56.

D L
Data Source p71, Data Model p9, Latitude p20, Layer p9, Legend
Data Type p12, Database p17, p57, Longitude p20
Datum p23, DEM p8.

E M
Ellipsoid p21. Map elements p53, Map Projection
p20.
G
Geocoding p8, Geographic N
Coordinate System (GCS) p20, Nearest-neighbor resampling p71,
Geoprocessing p63, NSDI (National Spatial Data
Georeferenced p1, GPS p80. Infrastructure) p1.

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O T
OGC (OpenGIS Consortium) p61. Topology p5.
P U
Prime meridian p20, Projected Union p32, UTM (Universal
coordinate system p24, Projection Transverse Mercator) p29.
p24.
R V
Reference system p68. Vector p5, Vector model p5.
S W
Scale bar p54, Shortest path p34, WGS 84 p22.
Slope p39, Spatial analysis p3,
Spheroid p21.
Z
Zone p29.

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 Deputy General Manager
Regional Remote Sensing Centre-North

National Remote Sensing Centre

Indian Space Research Organisation

New Delhi