B.Tech.

Project Report
On
AI BASED URBAN ASSET MANAGEMENT & MAPPING

Submitted in partial fulfillment for the award of B.Tech. Degree

By

THAKUR ADITYA PRAKASH (20211053)

SPENCER NAOREM (20211102)
SAHIL MEENA (20211114)

Under the guidance of

Dr A.K. Singh, Professor, CED

Department of Civil Engineering

Motilal Nehru National Institute of Technology,
Allahabad Prayagraj, 211004 (India)

May,2025
 Department of Civil Engineering
Motilal Nehru National Institute of Technology
Allahabad-211004(India)
www.mnnit.ac.in

UNDERTAKING
We declare that the work presented in the project entitled “AI BASED URBAN ASSET

MANAGEMENT &MAPPING” submitted to the Department of Civil Engineering, Motilal Nehru

National Institute of Technology Allahabad, Prayagraj (India) as a part of B.Tech. VIII semester course

curriculum is our original work. We neither have plagiarized any part of the present project nor submitted

the same work for the award of any other degree elsewhere.

In case this undertaking is found incorrect, the B.Tech project may be withdrawn unconditionally.

THAKUR SPENCER SAHIL

ADITYA NAOREM MEENA
PRAKASH

Place:
Date:

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 Department of Civil Engineering

Motilal Nehru National Institute of Technology Allahabad

Prayagraj-211004(India)
www.mnnit.ac.in

CERTIFICATE
This is to certify that project entitled “AI BASED URBAN ASSET MANAGEMENT

&MAPPING” submitted by THAKUR ADITYA PARKASH (20211053), SPENCER

NAOREM (20211102) & SAHIL MEENA (20211114) in partial fulfillment of the requirements for

award of the degree of Bachelor of Technology (Civil Engineering) to Motilal Nehru National Institute

of Technology Allahabad, Prayagraj has been carried out under my supervision and is an authentic record

of the student’s own work to the best of my knowledge and belief.

Date: (Dr. A.K. Singh)

Professor

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 ACKNOWLEDGEMENT

We extend our heartfelt appreciation to Dr. A.K. Singh, Professor, our mentor and Professor, for
skillfully guiding us through this project. His thoughtful guidance, valuable suggestions, and
timely instructions have been instrumental in our journey. We also express gratitude for his
unwavering motivation and encouragement throughout the project duration.

Our thanks also go to the Head of the Department of Civil Engineering for inspiring us to excel in
our work. Special appreciation is extended to Dinesh Kumar Azad Sir for his assistance and
collaboration at various stages of the project, particularly in mastering different software
applications.

Acknowledgment is also due to the project panel team for subjecting us to a rigorous learning
process, contributing to an enriched understanding of the topic. Our sincere gratitude extends to all
individuals, knowingly or unknowingly, who played a role in the successful completion of this
project.

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)
 ABSTRACT

People are struggling to manage and maintain public resources, including physical ones like
streets, energy, buildings, and green spaces, as cities grow at an astonishing rate. Conventional
asset management techniques lack real-time visibility, are time-consuming, and are prone to
human mistake. AI has the potential to transform city management and mapping, which will
impact how institutions move and analyze data.

This study looks at the potential applications of artificial intelligence (AI) tools for mapping and
managing urban assets, including computer vision, machine learning, and Geographic
Information Systems (GIS). To accurately identify, describe, and track urban assets, machine
learning skills can leverage data from IoT sensors, satellite or drone photos, and more.
Additionally, predictive analytics driven by AI helps determine when assets degrade and
optimizing maintenance schedules, thereby reducing costs and improving service delivery.

Our strategy combines the use of AI models with real-time data processing and visualization
techniques through interactive dashboards, providing the city administration with a single source
of accurate and useful information. Automating routine operations like inventory management
and condition assessments makes labor more efficient while requiring fewer workers.
Additionally, when AI is incorporated into mobile applications, it facilitates citizen engagement
by providing real-time asset issue reporting and tracking resolution timelines.

Through case studies and simulations, this paper offers evidence of the potential influence of AI-
driven solutions in attaining sustainable urban growth, optimizing resource allocation, and using
these tools to improve the quality of life in urban areas. The findings emphasize how crucial
relationships and policy frameworks are to overcome challenges in data privacy, scalability, and
implementation.

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Table of Contents
UNDERTAKING ............................................................................................................................ ii
CERTIFICATE .............................................................................................................................. iii
ACKNOWLEDGEMENT...............................................................................................................iv
ABSTRACT ..................................................................................................................................... v
Table of Contents.............................................................................................................................vi
Chapter 1 INTRODUCTION .......................................................................................................... 9
1.1 LITERATURE REVIEW ........................................................................................................ 10
1.2 OBJECTIVES .........................................................................................................................11
1.3 STUDY AREA .......................................................................................................................11
Chapter 2 METHODOLOGY ..........................................................................................................12
2.1 METHODOLOGY(Overall) .....................................................................................................12
2.2 DATA COLLECTION OF THE STUDY AREA ....................................................................13
2.3 DATA UPLOAD ON AN OPEN-SOURCE PLATFORM .......................................................13
2.4 AI BASED FEATURES EXTRACTION ................................................................................. 14
2.5 DEVELOPMENT OF GIS DATABASE ................................................................................. 15

2.6 ROADWAY ASSET MANAGEMENT AND UTILITY PLANNING USING iRAP .......... 16

CHAPTER 3 GIS .................................................................................................................17

3.1 INTRODUCTION TO GIS...................................................................................................17

3.2 GIS Applications...................................................................................................................18

3.3 Components of GIS ..............................................................................................................19
3.4 Data representation in GIS....................................................................................................20
3.4.1 Raster .............................................................................................................................20
3.4.2 Vector ............................................................................................................................21
3.4.3 Advantages Vs disadvantages of Raster and Vector ......................................................23

CHAPTER 4 SOFTWARES USED .............................................................................................25

4.1 QGIS .............................................................................................................................. 25
4.2 Mapillary ....................................................................................................................... 26
4.3 iRAP .............................................................................................................................. 28
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Chapter 5 DATABASE CREATION ............................................................................................. 29
5.1 SPATIAL DATABASE CREATION ........................................................................... 29
5.1.1 Data extraction using 360º camera ....................................................................... 29
5.1.2 Georeferencing ...................................................................................................... 29
5.1.3 Creation of Layers.................................................................................................30
5.1.4 Creation of Geo- Database .................................................................................... 30
5.1.6 Additional Digitization and Quality Checking .....................................................30
5.2 Integration of Spatial Data .............................................................................................. 31
Chapter 6 ANALYSIS OF IMPORTANT ROADWAYS USING iRAP ..................................... 32
Chapter 7 CONCLUSION..............................................................................................................46
Chapter 8 SCOPE OF FUTURE WORK ...................................................................................... 47
REFRENCES ................................................................................................................................. 48

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LIST OF FIGURES

Figure 1.1 Street view 10

Figure 1.2 Map of work area 11
Figure 2. 1 Methodology 12
Figure 2.2 Study route 13
Figure 2.3 Map of features 14
Figure 2.4 ArcGIS map 15
Figure 3.1 About GIS (https://gisgeography.com/) 18
Figure 3.2 Component of GIS 20
Figure 3.3 Raster Data (Data Representation in GIS) 21
Figure 3.4 Vector Data (Data Representation in GIS) 22
Figure 4.1 ArcGIS 26
Figure 4.2 Mapilliary map 27
Figure 4.3 Pilot Era 8k Camera 28
Figure 5.1 Conceptual database design 31
Figure 5.2 Json to shape file 31

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Chapter 1 INTRODUCTION
Around the world, urbanization is underway, with cities growing further to accommodate once-in-
a-lifetime population expansion and economic activity. This growth demands that urban asset—
roads, bridges, utilities, parks, public infrastructure, etc.—be managed efficiently so that the urban
space continues to function and citizens have a quality of life. But traditional asset management
practices are generally slow, manual, and reactive to address problems, resulting in ineffective
utilization of human resources and lag in responding to infrastructure requirements. These issues
underscore the necessity for innovative solutions that can adapt to the complexities of modern
urban environments.

But there is a revolution in the making — that of Artificial Intelligence (AI) — that offers
transformative solutions to surpass these solutions, by enabling data-driven, automated, and
proactive decision-making for the management of urban assets and systems. Through AI
technology, such as machine learning, computer vision or GIS can assist us in collecting,
processing and comprehending gigantic volumes of dataset.

But AI use in urban asset management is more than mapping and tracking. AI provides the
capability of predictive analytics to predict degradation of the assets so maintenance can be done
ahead of time to prevent catastrophic failure, sparing costly downtime. AI systems also make
decision-making easier and prioritizes the asset repairs needed through the recognition of how
asset condition, criticality and utilization relate. It assists in the efficient use of resources, providing
fewer disruptions to the public service.

Another significant AI aspect is citizen participation. AI-driven mobile apps allow citizens to
report issues on public assets such as potholes, faulty street lights, or leaky pipes. These kinds of
systems can sort and grade reported issues automatically, enabling quicker fixes and more
transparency with regard to the management of cities.

This is a report about how AI-powered urban asset and mapping can reimagine city infrastructure
management. The report examines signals for how AI applications fit within current ecosystems,
success stories, and the danger and opportunity that they pose. Through advice on some of the
main challenges such as data privacy, scalability, and cross-sector cooperation, the report
ultimately wishes to act as an in-depth playbook for cities that want to embrace AI-driven
solutions.

As urban pressures escalate, such as population expansion, aging infrastructures, and climate
change, AI technologies are no longer a choice, but a necessity. AI-powered asset management
and mapping have the potential to construct wiser, sustainable, and more resilient cities with
improved quality of life.

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 Fig 1.1 Street view
1.1 LITERATURE REVIEW
The City of Clovis is utilizing Mapillary for the first-ever traffic sign inventory. The city has only
one employee in its GIS department, with many other projects under their oversight, so this is
challenging for them. They did not have the time or money to spend on completing the inventory.
Time and money were saved since Mapillary enabled their GIS expert to collect street-level
imagery himself and build the inventory based on the automatically determined features of the
traffic signs.
Mapillary assists the city of Detroit gain a clear vision of its street and road assets in the quickly
changing city. Their GIS staff utilized a high-resolution 360-degree camera to capture 1.5 million
photos that were made public and distributed throughout the city's over 30 agencies. Mapillary's
automatic feature extraction assists city leaders in streamlining asset management tasks and
improving citizens' lives in aspects such as safety, insurance premiums, and snowplowing.

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1.2 OBJECTIVES
1. To collect 360° images along road network for various areas of Prayagraj city
2. To extract various objects from 360° images such as roads, traffic signs, markings, utilities to
develop as database
3. To provide 3D virtual visualization on urban features for improved planning and decision-
making using GIS.
4. To give star rating for the various important roads for traffic measures in terms of safety.
5. To give feedback and measures to make the roads safer.

1.3 STUDY AREA

The area of study for the project is the city of Prayagraj, situated around 25.4358° N latitude and
81.8463° E longitude. Situated in northern India, Prayagraj is historically and culturally a very old
city in the world. Prayagraj struggles with the problem of municipal solid waste management with
the rate of urbanization increasing at a very fast pace. Located roughly 700 kilometers southeast
of India's capital, New Delhi (28.6139° N, 77.2090° E), Prayagraj is an important urban hub in the
area. It is also roughly 635 kilometers northwest of the state capital, Lucknow (26.8467° N,
80.9462° E), which further underscores its strategic position. The city's distinctive features, in
addition to its increasing generation of waste with industrialization and migration, provide a
suitable site for the formulation of a custom-made AI-based urban asset mapping and management.
Fig 1.2 Map of work area

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Chapter 2 METHODOLOGY

2.1 METHODOLOGY (Overall)

To meet the above-stated objectives, the following methodology has been employed:

The initial process is to take data with a 360° camera circling a city in order to acquire panoramic images.
Next, the data is sent to an open-source platform to store, share, and more analytics and analysis. The
imagery is processed via, for instance, AI-based feature extraction in order to recognize and classify all urban
assets： road signs, utilities, and sidewalks.
Proportionate and accurate amounts of the extracted features are integrated into the GIS database that
geospatial data spatialized to generate more rich urban asset maps. Training on such maps facilitates roadway
asset management and utility planning, which translates into optimal maintenance, resource allocation and
infrastructure development towards smart urban management

Figure 2. 1 Methodology

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2.1.1 DATA COLLECTION OF THE STUDY AREA USING A 360º CAMERA

The in-depth analysis begins with the gathering of high-definition visual information of MNNIT campus
through 360° camera. The technology enables the panoramic video recording of the campus as a whole,
including roads, walkways, utility and built-up areas. With the camera rotating on site, the 360° view
records everything at once without omitting critical details. They are helpful in examining the conditions
of respective assets in a spatial manner. The video record (of the video series) is necessary to execute the
deep analysis of the campus for generating a detailed digital model (digital twin) at the next stages of data
processing and analysis of the urban asset management process.

FIG 2.2 STUDY ROUTE

2.1.2 DATA UPLOAD ON AN OPEN-SOURCE PLATFORM

After the 360° videos of Civil lines area and Teliarganj are captured, the data gets loaded into an
open-source platform named Mapillary designed especially for street-level imagery processing
and analysis. Mapillary allows the sharing and processing of geospatial data and hence it is a
great resource in making this project happen. Post these videos to Mapillary; the footage is
geolocated there, converting images into a database of geolocated imagery and spatial data. To
enable the data to become queryable for processing into a mapping workflow. Mapillary not only
makes data handling easier, processed imagery can be further processed and disseminated to
stakeholders for use in asset management and urban planning.

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2.1.3 AI BASED FEATURE EXTRACTION
Following the execution of Mapillary process from the teliarganj and civil lines 360° video data, AI-driven
feature extraction methodologies were applied to analyze the geotagged images. Mapillary smartphones are
specifically loaded with sophisticated computer vision algorithms which were capable of automatically
identifying and classifying urban features such as road signs, walkways, utilities, foliage, and infrastructure
features. These algorithms also determined the state of assets and generated warnings about possible
abnormalities, i.e., faulty road sections or obstructed pathways. Extraction of data provided rich details
about where things are located on campus and how few computers, chairs, etc., enabling us to create
accurate, more comprehensive maps of the campus. The trial of this automated procedure led to cutting
down on time and resources otherwise required to manually identify features with an increase in accuracy
and uniformity of the dataset.

FIG 2.3 MAP OF FEATURES

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2.1.4 DEVELOPMENT OF GIS DATABASE AND MAP GENARATION
OF EXTRACTED FEATURES

The features were derived through AI analysis and organized into a GIS database.
This created a detailed and organized database, combining geospatial data with extracted
features such as road signs, paths, utilities, and other campus facilities. Apart from being a
storage device, GIS can query and fetch information pertaining to particular area/asset
entered in Teliarganj and Civil lines.
Backed by GIS technology, detailed maps were generated to show the distribution,
attributes, and interconnectivity of the selected set of assets. These maps gave a clearer
picture of the area that aided infrastructure layout analyses by stakeholders
The capability to superimpose various datasets and deliver more sophisticated GIS
capabilities enabled comprehensive studies on data to be carried out such as asset
relationships, usage patterns, and resource distribution. Thus, these maps also facilitated
the viewer to easily interpret the data through visualization, enabling actionable insight
into campus development and management decisions. The second stage of the project
transformed the raw 360° camera data into significant geo-spatial form to make the data
consumable, sorted and ready for advanced analysis and utility planning. The creation of a
GIS database and the derivation of maps was a key step to completing the loop between
data capture and strategic asset management of the region.

Fig 2.4 QGIS map

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2.1.5 ROADWAY ASSET MANAGEMENT AND UTILITY PLANNING USING iRAP
The roadway assets of the MNNIT campus were maintained for condition assessment, repair
prioritization and safety enhancement utilizing the GIS database and maps produced. Some items
were qualified for maintenance, like damaged sections or worn-out markings, and prioritized so
that resources could be effectively allocated.

The geospatial information was applied to utility planning in order to create the optimal
configuration for water supply, electricity, and drainage facilities. The intersection mapping of
the roadway and utility was helpful in averting maintenance triggered conflicts and reduced
service disruption in busy routes.

Such a data enablement may be extended to further counsel the governance of campus
infrastructure: right decision taken at right time for sustainable future planning Geospatial
insights married with the process enabled streamlined resources and immediate needs while also
enabling continued transformation, allowing for a well-managed and usable campus
environment.

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CHAPTER 3: GIS

3.1 INTRODUCTION TO GIS

GIS has been defined by many ways, by many people:

“A Geographic Information System is a facility for preparing, presenting, and interpreting facts
that pertain to the surface of the earth. This is a broad definition, a considerably narrower
definition, however, is more often employed. In common parlance, a geographic information
system or GIS is a configuration of computer hardware and software specifically designed for the
acquisition, maintenance, and use of cartographic data.” [Tomlin, 1990]

“A geographic information system (GIS) is an information system that is designed to work with
data referenced by spatial or geographic coordinates. In other words, a GIS is both a database
system with specific capabilities for spatially-referenced data, as well as a set of operations for
working with data. In a sense, a GIS may be thought of as a higher order map.” [Star, et at, 1990]
GIS is, “An organized collection of computer hardware, software, geographic data, and personnel
designed to efficiently capture, store, update, manipulate, analyze, and display all forms of
geographically referenced information.” [Foote, et al, 1990]

Thus, “GIS is a systematic integration of Computer Hardware, Software and Spatial Data, for
capturing, storing, displaying, updating, manipulating and analyzing, in order to solve complex
management problems.”

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3.2 GIS APPLICATIONS

Several related disciplines have witnessed the concurrent evolution of computerized mapping
and spatial analysis. It is only through intensive cooperation among several disciplines, such as
utility networks, cadastral mapping, topographic mapping, thematic cartography, remote sensing,
photogrammetry, surveying, image processing, computer science, rural and urban planning,
geography, and earth science, that the current status is achievable.
GIS technology is rapidly emerging as a normative tool for natural resource management. There
is a need to have an efficient geographic handling and processing system in order to transform
huge amounts of spatial data into usable information.
By showing a range of possibilities when planning for conservation and development and by
modeling the likely outcomes of various scenarios, GIS technology assists decision-makers. It
should be kept in mind that the true world is the place where each task begins and ends. The true
world is the object of data collection. The end result is necessarily an abstraction because dealing
with every last detail is not possible or desirable. This knowledge is followed by information
compilation for decision-makers. Actions and plans are implemented in the real world on the
basis of this information. [development of GIS]

FIG 3.1 About GIS(https://gisgeography.com/)

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 Principal application domains

GIS finds use in the following fields:

● Various planning streams

It is utilized for landscape design, architectural preservation, urban planning, housing, and transit
planning.
● Street network-based software
It is a matching application that addresses catastrophe preparation, site selection, vehicle
scheduling, and location.
● Natural resource-based software
It is employed in flood plains, wetlands, aquifers, forests, wildlife, and wild and scenic
recreational resources for management and environmental impact studies.
● Analysis of viewsheds
GIS is highly helpful in planning migration routes, studying wildlife habitat, simulating
groundwater, and locating dangerous or toxic factories.
• Land parcel-based
Among other things, GIS is used for zoning, subdivision plan reviews, land acquisition,
environmental impact analyses, and nature quality management and upkeep.
• Facilities administration
For planning, maintenance, and monitoring energy consumption, it can identify subterranean
wires and pipes.

3.3 ASPECTS OF GIS

Five essential components (Figure 3.2) are integrated into GIS [Westminster]:

People, Data, Software, Hardware, and Method.

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 FIG3. 2 Component of GIS

3.3 DATA REPRESENTATION IN GIS

GIS data employs digital data to represent real-world phenomena such as highways, land use, and
elevation. Discrete objects, such as a house, and continuous fields, such as elevation or rainfall,
are two types of abstractions that may be employed to characterize real-world objects. Both of
these abstractions are stored in a GIS using raster and vector as the two primary means of storage.
3.4.1 A raster
One value is held in each of the rows and columns of cells that constitute raster data types. Pictures
with a color value in every pixel (or cell) are referred to as raster data. Besides, each cell can
contain a null value when there is no data, a continuous value such as temperature, or a discrete
value such as land use (Figure 3.2). Though a raster cell stores a single value, it may be increased
by associating RGB (red, green, and blue) colors with raster bands, constructing an extended
attribute table having a single row per unique cell value, or building color maps. The cell width in
ground units of the raster data set signifies its resolution.
Essentially, any digital picture expressed in grids is a raster data type. To display an inclusive
picture on a map or to digitize, aerial photographs are one kind of raster data that are often used.
Elevation, a DEM, or reflectance of a specific light wavelength, LANDSAT, will be part of other
raster datasets. It is viable to store raster data in all forms, from the default file-based form of TIF,
JPEG, etc. to binary large object (BLOB) data stored natively in a relational database management
system contrasted with other RDBMSs (vector-based feature classes). While having millions of
big data files stored within a database may be required, properly indexed data usually enables
quicker retrieval of raster data.

FIG3.3 Raster Data (Data Representation in GIS)

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3.4.1 Vector
By applying each vector element—points for wells, lines for rivers, and a polygon for the lake—
a simple vector map is produced (Figure 3.3). Since they are geometrical objects, geographical
objects in a GIS are often portrayed as vectors. Different types of geometry represent different
geographical features:
Points
Geographical attributes that are best defined by a single point location—that is, straightforward
location—are depicted by zero-dimensional points. Trailheads, summit elevations, well
positions, and objects of interest are some examples. Among all the file formats mentioned
above, points convey the lowest amount of data. Points may also be employed to represent areas
in small-scale displays. Cities, for example, would appear as points instead of polygons on a
world map. Point features cannot be measured.

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1. Lines or polylines

Linear elements such as rivers, roads, railroads, trails, and topographic lines are symbolized by
single-dimensional lines, also referred to as polylines. At a small scale, linear features will be
displayed as points of linear features rather than a polygon, similar to point features. Distance is
measurable from line features.
FIG 3.4 Vector Data (Data Representation in GIS)

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 2. Polygons

When defining geographical characteristics that cover a certain area of the earth's surface, two-
dimensional polygons are used. Characteristics such as lakes, park boundaries, buildings, city
boundaries, or usage of land are just some examples of this kind of characteristic. Polygons
provide more information than any other type of file. Polygon features can be used to calculate
area and perimeter.
Each of these geometries is assigned a row in a database that defines its attributes. The depth of a
lake, the quality of the water, and the level of pollution, for example, could be fields in a
database that defines lakes. This information may be used to build a map describing a particular
dataset attribute. Lakes could become color-coded depending on pollution levels, for example
Topology requirements, like "polygons should not overlap," can be imposed on vector features to
make them obey spatial integrity. Vector data can also be used to represent constantly changing
phenomena. Contour lines and triangulated irregular networks (TIN) are used to represent
elevation or other continuously changing data. Data are noted at point locations in TINs, which
are connected by lines to form an asymmetrical triangle mesh. The surface of the terrain is
modeled by the triangles' faces.

3.4.2 Advantages Vs disadvantages of Raster and Vector

Raster or vector data models are advantageous and disadvantageous in the representation of reality.
A raster data set can occupy more storage space than a vector mode, which only stores data where
it is required, since it captures a value for each point in the area covered. Moreover, raster data
facilitates overlay operations to be simpler to perform compared to vector data, making them more
difficult.
Whereas raster data will be displayed as an image that could either be or not be blocky in
appearance for object boundaries depending on the resolution of the raster file, vector data can be
displayed as vector graphics utilized on traditional maps. Scaling, re-projecting, and registering
the vector data can be easier. Vector data typically boasts a much lower file size for distribution
and storage compared to raster data.
Depending on the resolution, raster or image data may be 10–100 times greater than vector data.
Vector data also has the advantage of being easy to update and keep current. For example, a new
road is built. The vector data, "roads," can be easily modified by adding the new road segment, but
the raster image will need to be recreated completely.

In addition, vector data provides enormous analytical capability, especially for "networks" such as
telecommunications, electricity, rail, and roads. For example, the analyst can query for the best

23
route or means of travel with vector data related to road, port, and airfield feature. With an airstrip
within 60 miles and a connecting road which is at least a two-lane highway, the analyst can query
the vector data to identify the largest port. Not every feature shown in raster data will have all
characteristics.

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CHAPTER 4: SOFTWARE USED
The following software are used:

4.1 QGIS

QGIS (Quantum Geographic Information System) is a powerful, open-source Geographic

Information System that offers an open platform for geospatial mapping, analysis, and data
visualization. It supports users working with many spatial data formats, such as vector, raster, and
database layers, making it viable for different applications in geography, urban planning,
environmental monitoring, and disaster management. QGIS has built-in support for sophisticated
operations like spatial querying, geoprocessing, terrain analysis, and map assembly, as well as
being able to directly import data from GPS devices, satellite images, and web mapping services
such as WMS and WFS.

One of QGIS' major strengths is its customizability and flexibility, backed by a very active global
community and an enormous library of plugins expanding its capabilities. Its ease of use, periodic
updates, and capacity to process sophisticated spatial tasks without the expense of proprietary
software has rendered it a choice both for academic research work and professional geospatial
processes. As a spatial decision-making tool, QGIS equips users to produce reliable, data-driven
maps and undertake useful spatial analysis to solve actual issues.

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 FIG 4.1 QGIS MAPPING

4.2 MAPILLARY

Genomics European Molecular Biology Laboratory Building, German has produced a range of
online tools to make use of georeferenced street-level imagery. Mapillary is an open-source
platform used to gather, analyse, and share street-level imagery. Users can utilize tools like 360°
cameras, smartphones, and drones to capture images of urban areas. The images are thus
processed and hosted on the platform, forming a worldwide repository of geospatial data that
could be used for asset management, urban planning, and mapping, among other applications.

Mapillary boasts one of the finest crowdsourcing methods available, whereby anyone ranging
from ordinary citizens to companies and municipal government can become both consumers and
producers of data. The website employs advanced computer vision and machine learning
technologies to identify automatically, classify and map all of the elements of the city's structure,
such as road signs, sidewalks, building facades and utilities. It eliminates the need for hand-
mapping
which leads to quicker and more affordable solution with increased data accuracy.

Mapillary can be integrated with any GIS application like ArcGIS, it then becomes an easily
accessible source of urban asset management. Its real-time high-resolution imagery may be used
in supporting the development of high-precision maps as well as monitoring the condition of
infrastructure. In other places, the platform is also capable of supporting live data updates,
enabling cities to monitor changes within their surroundings.
Mapillary therefore assists in more informed decision-making by allowing visual and spatial
26
intelligence in urban management situations. In road preservation, traffic management or urban
infrastructure planning, Mapillary provides planners and administrators with the means to
approach urban challenges proactively. Cities can offer up to sustainable, efficient asset
management based on its scalable and collaborative strategy.

FIG 4.2 MAPILLARY MAP

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4.3 iRAP
The International Road Assessment Programme (iRAP) is a worldwide not-for-profit initiative
that collaborates with governments, development agencies, research organizations, and other
stakeholders to enhance road safety infrastructure and minimize road traffic fatalities and
injuries. As one of the initiatives worldwide aimed at addressing the public health emergency
brought about by road crashes, iRAP takes a leading role in promoting safer roads through
evidence-informed assessments and focused infrastructure enhancement.

iRAP works on the assumption that properly designed roads can have a major impact on
reducing the likelihood and severity of crashes. To substantiate this, the organization has
established a standardized method for rating roads, the iRAP Star Rating System. The system
rates roads according to their intrinsic safety elements for various road users—motorists,
motorcyclists, pedestrians, and cyclists—and gives them a safety rating of 1 star (least safe) to 5
stars (safest). These scores are calculated from comprehensive data collection and analysis of
road features like lane width, road curvature, roadside obstructions, pedestrian accommodations,
and intersection design.

Along with road inspections, iRAP produces Safer Roads Investment Plans (SRIPs)—evidence-
based recommendations that focus on low-cost, high-impact engineering interventions. These
can include upgrades such as the addition of guardrails, pedestrian crossings, road signs, traffic
calming, and other interventions that have been shown to lower fatalities and serious injuries.
The plans are customized to local contexts and budgets, assisting governments and funding
agencies in making informed investment choices.

One of iRAP's most important contributions is that it supports the United Nations Sustainable
Development Goals (SDGs), particularly SDG Target 3.6, to halve road traffic fatalities and
injuries by 2030. iRAP's methodologies and tools are extensively used by national transport
authorities, development banks, and local governments to track road safety performance and
monitor improvement over time.

iRAP also assists countries with its capacity-building programs, offering technical training,
software tools such as VIDS (ViDA platform), and materials to enable local teams to conduct
road safety assessments and apply safety upgrades independently. Its open-access model and
global best practices have established it as a foundation of contemporary road safety
management strategies.

Since road traffic injuries continue to be a major cause of death, especially among youth in low-
and middle-income nations, iRAP's contribution is essential in changing the focus. This change
is from reactive response to crashes to proactive road design and prevention.

28
 Chapter 5: DATABASE CREATION

5.1 SPATIAL DATABASE CREATION

5.1.1 Data Extraction using 360º camera

The SOI map, Prayagraj Municipal Corporation obtained map, and satellite images form the
project base maps. There is a requirement to scan the maps and make them available in different
formats like PBF and OSM.
Spatial reference information, either as a separate file or embedded within the file, is usually
missing from scanned map files. Occasionally, the local data derived from satellite imagery and
aerial photography is not enough, and the data does not correspond to our other data sources
properly.
5.1.2 Georeferencing
Due to this, we have to geo-reference, or align, some raster datasets to a coordinate map system
in order to be able to use them with other spatial data. Raster data that is georeferenced can then
be seen, queried, and analyzed by other people.
The following are the standard steps used to georeference a raster dataset:
The structure of the file which we have received is of the type of the pbf and it is required to be
converted to the osm. Utilizing the pbf to osm converter the following was executed(Osm
converter). Now using the database manager of the Qgis and connecting it with the Postgis and
postgresql a table of the osm_id of different points available in the layers was created.
Osm_id has the longitude and latitude of a location thus the georeferencing is accomplished

The Coordinate System Definition

Coordinate systems allow for the merging of geographic datasets based on common locations.
Coordinate systems are systems of reference that are employed to express, within a common
geographic framework, the positions of geographic features, imagery, and observations, such as
GPS locations. WGS-84 was used in this case.

29
 Types of Coordinate Systems

In GIS, there are two widely used types of coordinate systems:

• Geographic Coordinate Systems: These reference systems determine the locations of points
on the surface of a spheroid or sphere in terms of latitude and longitude. A datum, prime
meridian, and angular unit are all part of the definition of a geographic coordinate system.
• The Projected Coordinate System is grounded in a map projection, e.g., Robinson, Albers
equal area, or transverse Mercator, among many others, that provides various means to
project maps of the earth's spherical surface onto a two-dimensional Cartesian coordinate
plane. One name for projected coordinate systems is "map projections.
5.1.3 Creation of Layers
Shape files are employed to create numerous layers, such as roads and administrative boundaries.
Before these layers (digitizing) are created in QGis, the shape files with line, polygon, or point
features are initially prepared with the Hot Export tool. The geodatabases are then populated with
these digital layers.
5.1.4 The establishment of a geodatabase
Geodatabase in QGIS refers to a collection of various types of geographic data sets that are stored
in a relational database or file folder. Editing and automation of data in QGIS are accomplished
using this native data base.
5.1.5 Creation of Geodatabase

The geodatabase is a group of various geographic datasets of different types used in QGIS and
stored in either a file folder or a database. It is the native QGIS data source and is utilized for data
automation and editing within QGIS.
5.1.6 Additional Digitization and Quality Checking
Additional digitization was then conducted after acquisition of the new image. In this process, the
new information is brought in by revising additional roads and boundaries. Experts and a registered
organization aid in evaluating the spatial quality of the new content.

30
5.2 INTEGRATION OF SPATIAL DATA

Spatial data are integrated in the GIS environment.

FIG 5.1 CONCEPTUAL DATABASE DESIGN

FIG 5.2 JSON TO SHAPE FILE

31
 Chapter 6 ANALYSIS OF IMPORTANT ROADWAYS USING iRAP

The International Road Assessment Programme (iRAP) uses a structured and evidence-based
approach to evaluate road safety and suggest targeted improvements to infrastructure. Its
methodology targets the examination of road design elements and their contribution to the risk of
road crashes, as opposed to using past crash data alone. The process involves a number of
important steps:

1. Road Survey and Data Collection

Step one in an iRAP assessment involves collecting detailed data on road infrastructure. This is
usually done using vehicle-mounted survey equipment capturing high-resolution video imagery
and other geospatial data along the road network. Survey crews capture different attributes
including:
• Road width and number of lanes
• Road markings and signage presence and quality
• Intersection types and configurations
• Roadside hazards (e.g., poles, trees, ditches)
• Pedestrian and cyclist facilities
• Speed limits and traffic flow characteristics
2. Coding Road Attributes
After the data is gathered, it is coded and analyzed by iRAP's software tools. Experienced analysts
scrutinize the survey imagery and allocate standardized codes to more than 50 various road
features. The codes quantify the presence and quality of safety aspects, including shoulder type,
median protection, pedestrian crossings, and delineation.
3. Risk Mapping and Star Rating
With the encoded data, iRAP runs its Star Rating Algorithm to assess the safety of every 100-
meter road section for four road users:
• Vehicle occupants
• Motorcyclists
• Pedestrians
• Cyclists

32
 Each section is assigned a rating of 1 to 5 stars, depending on the degree to which the road design
keeps users safe in case of a crash. The ratings are represented graphically on risk maps, which
enable stakeholders to easily spot risky areas and allocate interventions accordingly.

4. Safer Roads Investment Plan (SRIP)

According to the results of the assessment, iRAP produces a Safer Roads Investment Plan (SRIP).
The plan determines precise engineering countermeasures that can be done to enhance safety, for
example:
• Fitting guardrails or safety barriers
• Inserting pedestrian crossings or overpasses
• Increasing road signage and visibility
• Developing segregated bike lanes
• Enhancing intersection design

Each of these proposals comes with a cost-benefit analysis so governments and funding authorities
can spend resources effectively and prioritize the most effective safety upgrades.

5. Monitoring and Reporting

iRAP also includes tools to measure changes with the passage of time. Periodic reassessments can
be done to analyze the effectiveness of interventions and monitor progress towards safety targets.
The findings are usually incorporated into national or regional road safety plans and used in
decision-making about policy.

6. Capacity Building and Local Empowerment

In order to bring about long-term impact, iRAP invests in capacity development and training.
Government agencies and local engineers and planners are equipped to apply the iRAP method
and tools such as the ViDA platform—a cloud-based database system for the storage, analysis, and
visualizing of results from road assessment. This ensures sustainable road safety management and
helps empower countries to be responsible for their infrastructure improvement.

33
 1 ANALYSIS OF THE DEAN ACADEMICS INTERSECTION USING iRAP:

Existing Star Ratings:

• Car occupants: 2-star

• Motorcyclists: 2-star

• Pedestrians: 4-star

• Cyclists: 3-star

These ratings show that motorized users are much more at risk, especially in crash situations
such as run-offs, head-on collisions, or intersections that are not adequately delineated.
Vulnerable road users (VRUs), though slightly better rated, are still exposed to risks due to non-
uniform infrastructure protection.

Suggested Infrastructure Upgrades to Enhance Star Ratings

To upgrade the star rating of roads in the study area—particularly for vehicle occupants and
motorcyclists—the following specified infrastructure upgrades are suggested:

For Vehicle Occupants and Motorcyclists (From 2-star to 4-5 star):

• Install roadside barriers (e.g., guardrails, crash cushions) to minimize run-off severity.

• Install median separation (e.g., raised medians or flexible bollards) to avoid head-on collisions.

34
 • Improve road markings and supply delineation at intersections and curves to direct vehicles more
effectively.

• Implement rumble strips to alert careless drivers.

• Enforce speed management measures, such as automatic enforcement or traffic calming areas.

For Pedestrians (From 4-star to 5-star):

• Provide continuous and expansive footpaths on either side of the road.

• Introduce zebra crossings or signal-controlled pedestrian crossings at all key intersections and mid-block
points.

• Install pedestrian refuges or islands on wider roads to facilitate safe crossing.

• Enhance street lighting to improve visibility at night.

For Cyclists (3-star to 5-star):

• Establish dedicated and protected cycle lanes, preferably physically separated from road traffic.

• Install innovative stop lines at intersections.

• Enhance intersection design with cyclist priority signals.

• Utilize traffic-calming designs in shared space.

Crash Type and Speed Analysis Insights:

The crash-type data presented here indicates that "run-off" and "head-on" crashes are major risk
contributors for motor vehicles and motorcycles. For cyclists and pedestrians, the most
hazardous situations are crossing the road and traveling along the edge with no physical
protection.

Demonstrator speed graphs also demonstrate that higher speed is associated with a much-reduced
level of safety (STAR units) for all road users—particularly pedestrians and cyclists—
emphasizing speed reduction as a main safety measure.

35
36
ANALYSIS OF THE ROAD FROM SVBH TO DEAN ACADEMICS INTERSECTION:

1. Star Rating by Road User Type

According to iRAP assessments, the star ratings show different levels of safety by different road users:
• Car Occupants: 3.7stars
• Motorcyclists: 3.7 stars
• Pedestrians: 3.3 stars
• Cyclists: 3.9 stars

2. Crash Type Insights:

Car/Motorcycle:
• Run-off (driver and passenger side) — because of absence of barriers and shoulder protection
• Head-on collisions — particularly at curves or badly divided roads

Pedestrians:
• Street crossing through traffic — is responsible for the overwhelming majority of pedestrian danger
• Absence of controlled crossings makes unpredictability and vulnerability greater

Cyclists:
• On-road collisions — because of shared space with motor traffic
• At-intersection collisions — brought about by turns of motor vehicles and absence of specific cyclist
phases
• Run-off events — from irregular surfaces or bad road edge conditions

37
3. Speed vs Risk (Star Rating) Analysis
Speed is a significant contributor to road safety. The graphs indicate:
As speed rises, the star rating reduces significantly across all user types.
At speeds above 80 km/h, the star ratings drop to 1 star for pedestrians and cyclists, and 2 stars
for cars.
The optimum safety speed for mixed-use urban roads is 30–50 km/h, above which chances of
fatal accidents increase manifold.

Infrastructure Suggestions for Enhancing Safety and Star Ratings

For Pedestrians
• Construct elevated pedestrian crossings and islands at regular distances.
• Provide continuous footpaths with physical segregation from motor traffic.
• Provide signalized crossings at all important intersections.
• Provide speed reduction zones along pedestrian routes.

For Cyclists
• Provide segregated bike lanes, preferably protected by curbs or bollards.
• Use bike boxes and advance stop zones at intersections.
• Offer bicycle-specific traffic signals to minimize turn conflicts.
• Enhance surface quality and place clear cycle lane markings.

For Car Occupants and Motorcyclists

• Implement median dividers and guardrails on curves and open roads.
• Enhance road markings, lane width, and shoulder design.
• Enhance intersection geometry and signage.
• Implement intelligent speed monitoring and enforcement systems.

38
39
ANALYSIS OF THE ROAD FROM DEAN ACADEMICS INTERSECTION TO THE
CENTRAL LIBRARY:

Star Ratings by Road User Type:

• Car Occupants: 3 Stars
• Motorcyclists: 3 Stars
• Pedestrians: 2 Stars
• Bicyclists: 4 Stars

Speed Analysis:
Car Occupants & Motorcyclists: Star rating diminishes considerably as speed rises above 60 km/h.
Between 90–120 km/h, risk surges very quickly into 1- and 2-star areas.

Pedestrians & Bicyclists: Safety worsens considerably even at moderate speeds (50–80 km/h),
particularly for pedestrians.

Proposed Infrastructural Enhancements:

For Car Occupants & Motorcyclists:
• Use crash barriers (guardrails) to mitigate run-off severity.
• Add rumble strips and reflective lane markings to help prevent lane departure.
• Use speed-calming devices such as speed cushions or warning signs prior to curves.
40
For Pedestrians:
• Provide pedestrian crossings with refuge islands to reduce high "crossing through" crash
risk.
• Provide footpaths separated from the carriageway by green strips or curbs.
• Increase street lighting to enhance night visibility.

For Bicyclists:
• Create exclusive cycling lanes, preferably protected or at least visibly marked.
• Provide cyclist warning signs for motorized traffic.
• Reduce the speed limits on shared-use roads to minimize conflict severity.

41
42
 ANALYSIS OF THE ROAD FROM DEAN ACADEMICS INTERSECTION TO GANGA
GATE:

1. Star Rating:

• Vehicle Occupants: 4
• Motorcyclists: 3.9
• Pedestrians: 4
• Cyclists: 4

2. Crash Risk Analysis

Vehicle Occupants and Motorcyclists

• Run-off Collisions: Driver-side and passenger-side run-off hazards are equally high (2.430 units each),
indicative of a lack of inadequate roadside barriers.

• Entry Points: Limited but significant crash hazard from roadside entry points (0.047 for cars, 0.061 for
motorcycles) indicates turning or merging threats.

Pedestrians

• Walking Through Traffic: The primary source of pedestrian risk (15.894 units) is due to unsafe crossing
over vehicular traffic.

• Crossing Side Movement: Extra risk (0.053 units) is posed by lateral pedestrian movements on the road.

• Walking Along the Road: Pedestrian exposure while walking alongside the road adds 0.039 units to crash
risk.

43
Cyclists

• Riding Along the Road: A high-risk value (9.703 units) indicates inadequate protection for cyclists traveling
with traffic.

• Intersections and Run-off: Lower but still significant risks are caused by intersections (0.002 units) and run-
off accidents (0.007 units).

3. Suggested Infrastructural Upgrades

A. Installation of Barriers

• Implement crash barriers on either side to reduce severity of run-off crashes.

• Utilize motorcycle-friendly safety barriers to limit secondary injuries among two-wheeler riders.

B. Pedestrian Crossings

• Provide grade-separated crossings (underpasses or footbridges) where pedestrian flow meets high-speed
roads.

• Where necessary, provide signalized pedestrian crossings with central refuge islands.

C. Bicycle Infrastructure

• Create separated bicycle lanes with curbs or bollards to shield them from motor traffic.

• Provide continuity of bike lanes with unambiguous surface markings and signage.

D. Access Management

• Prevent informal roadside access by fencing and controlled entrances.

• Design suitable merging areas with exclusive deceleration and acceleration lanes where access is required.

E. Speed Management

• Enforce and enforce lower speed limits in pedestrian and cyclist-dense zones.

• Apply visual speed control devices such as rumble strips or optical lane narrowing.

F. Visibility and Illumination

• Use high-visibility lane markings particularly around pedestrian and cyclist routes.
• Fit road lighting to make the roads more secure at night for all drivers.
44
45
Chapter 7 CONCLUSION

The project "AI-Based Urban Asset Management & Mapping" is an exemplary success in
showcasing the revolution that can be brought about by combining Artificial Intelligence (AI) and
Geographic Information Systems (GIS) for effective urban infrastructure management. Using 360°
camera technology, open-source platforms such as Mapillary, and sophisticated AI tools, the project
was able to meet its goals of extracting, mapping, and analyzing urban assets like roads, traffic signs,
and utilities in the Prayagraj city. Major achievements are:

1. Automated Asset Extraction: Feature extraction using AI drastically minimized human effort,
enhanced accuracy, and allowed real-time tracking of urban assets.
2. GIS Database Development: Development of a robust GIS database enabled spatial analysis,
visualization, and informed decision-making for infrastructure planning.
3. Road Safety Improvements: Based on iRAP methods, key road sections were assessed, and
practical recommendations (e.g., guardrails, pedestrian crossings, speed management) were made to
enhance safety scores for all road users.
4. Scalability and Future Integration: The framework of the project is scalable, with future
possibilities for use in larger urban zones and integration with Prayagraj's Smart City projects.

The project showcases how GIS and AI can transform urban asset management through proactive
maintenance, resource optimization, and increased citizen participation. It is an exemplar of data-
driven, sustainable urban development to counter population expansion and aging infrastructure.

In summary, the convergence of AI and geospatial technologies presents a strong path toward
smarter, safer, and more resilient cities.

46
 CHAPTER 8 SCOPE OF FUTURE WORK

Collaboration with Municipal Authorities for Infrastructure Development

One of the main future goals is to form a partnership with the Prayagraj Municipal Corporation
(PMC) and other concerned urban governance agencies. Through this partnership, the compiled
urban asset and road safety data can be incorporated into city-level infrastructure planning and smart
city development initiatives. By integrating the dataset into the PMC's planning mechanisms, the
dataset can be used to make evidence-based decisions in prioritization of road maintenance, traffic
management, and the deployment of safety enhancements in high-risk areas.

Integration with Live Traffic and Accident Databases

In order to design a more predictive and responsive road safety system, the future will involve the
integration of this road image database with real-time traffic flow data and accident occurrence. This
will enable the dynamic iRAP star rating generation, which adapts with real-time road conditions
and usage trends. This way, municipal authorities can send proactive safety warnings and schedule
interventions ahead of accidents, designing a more adaptive and preventive road safety system.

Development of a GIS-Based Urban Asset Management Dashboard

Another critical element of future work is the creation of an easy-to-use Geographic Information
System (GIS)-based web or mobile dashboard. This tool would enable municipal engineers,
planners, and policy-makers to visualize, update, and manage road and asset information in real
time. The dashboard might include overlays of satellite imagery, road segment star ratings, incident
reports, and maintenance records—simplifying decision-making and facilitating targeted
investments in road safety and urban infrastructure upgrades.

Through these paths, the project may develop from a tool for collecting and comparing data to a full-
fledged, real-time, decision-support system directly adding value to the creation of safer, smarter, and
more sustainable cities.

47
REFERENCES

1. Jay Dahlstrom, Christian Matthews, Tommy Nguyen,August, 2017. Enhancing Collaborative Data
Collection Using Mapillary
2. Sagar Kolekar,2017. Mapping of the assets and utilities: a vision for the development smart cities
in India.
3. Application of Remote sensing and GIS Techniques in Urban Planning, Development and
Management. (A case study of Allahabad District, India.)
https://www.academia.edu/39734778/Application_of_Remote_sensing_and_GIS_Techniques_in
_Urban_Planning_Development_and_Management_A_case_study_of_Allahabad_District India.
4. Wikipedia
5. Mapillary.
https://www.mapillary.com/app/?lat=25.492479164563605&lng=81.86720133348126&z=16.28
748040414941&x=0.9908003852323914&y=0.4600003045149714&zoom=0&pKey=55902401
3448778&focus=map&mapStyle=OpenStreetMap
6. Dorota Kamrowska-Załuska, November 2021. AI and Big Data Analytics for Indian Urban
Development. https://www.mdpi.com/2073-445X/10/11/1209
7. Laura Rodriguez, Ahmed El-Masry, and John T. Johnson, July 2022. GIS and AI Integration for
Smart City Development
8. Jessica Page, Yoonshin Kwak, Brian Deal, Zahra Kalantari, August 1, 2024. AI Analytics for
Carbon-Neutral City Planning: A Systematic Review of Applications.
https://www.mdpi.com/2413-8851/8/3/104
9. Dexter Slusarski,April 2019, Creating the first comprehensive digital map of the largest city in
Michigan https://drive.google.com/file/d/11x7xcG9vS_oTiOsfEzkwCQWNbNV5MB5T/view
10. GIS https://gisgeography.com/

48
PROJECT
ORIGINALITY REPORT

9 %
SIMILARITY INDEX
8%
INTERNET SOURCES
5%
PUBLICATIONS
7%
STUDENT PAPERS

PRIMARY SOURCES

1
Submitted to Motilal Nehru National Institute
of Technology
4%
Student Paper

2
m.moam.info
Internet Source 3%
3
Submitted to CSU, San Jose State University
Student Paper <1%
4
gecgudlavalleru.ac.in
Internet Source <1%
5
cdn.jsdelivr.net
Internet Source <1%
6
eprints.kname.edu.ua
Internet Source <1%
7
digitallibrary.mes.ac.in
Internet Source <1%
8
Submitted to Capital University of Economics
and Business, Overseas Chinese College
<1%
Student Paper

9
en.wikipedia.org
Internet Source <1%
10
Qingfeng Li, James Bradford, Abdulgafoor M.
Bachani. "Statistical estimation of fatal and
<1%
serious injuries saved by iRAP protocols in 74
countries", PLOS ONE, 2024
Publication
11
Submitted to Universiti Teknologi Malaysia
Student Paper <1%
12
www.bio-conferences.org
Internet Source <1%

Exclude quotes On Exclude matches < 10 words

Exclude bibliography On
 Thakur Aditya Prakash
# thakuradityaprakash@gmail.com ï linkedin.com/in/Thakur Aditya Prakash
Education
Motilal Nehru National Institute Of Technology 2021 – Present
Bachelor of Technology in Civil Engineering CGPA: 7.46
Ramakrishna Mission Vidhyapith 2019 – 2020
Central Board of Intermediate Education Percentage: 96
Chinmaya Vidyalaya 2017 – 2018
Central Board of Secondary Education Percentage: 92.2
Projects
Topographical Mapping of the MNNIT Academic Campus using Total Station and Plane Table Dec 2023
• Mapped the MNNIT Academic Area using Total Station and Plane Table and digitalized the map using
QGIS.
• Performed Profile Levelling using Auto Level.
• Conducted a GPS survey of the MNNIT Academic Area using Trimble Juno 3B and created a GPS layout in
QGIS.
STAAD.Pro Analysis and Design of RCC Building May 2024
• Performed comprehensive analysis and design of RCC building using STAAD.Pro ensuring compliance
with relevant codes.
• Conducted static and dynamic load analysis including dead load,live load,seismic load and wind load.
• Developed detailed 3D model of RCC structures, including beams,columns,slabs.

Skills
Tools: Matlab, AutoCAD, STAAD.Pro(Basics), QGIS
Other: MS Excel, Ms PowerPoint
Areas of Interest
Geotechnical Engineering
Transportation Engineering : Highway and Pavement Design

Leadership / Extracurricular
E-cell 2022 – Present
Marketing Team Lead Motilal Nehru National Institute Of Technology
• Successfully organized and managed Renaissance Fest 2024, securing sponsorships to support event logistics and

activities.
• Collaborated with various departments to ensure smooth execution of the fest, handling budgeting, scheduling, and

vendor coordination.
• Established partnerships with local businesses, resulting in the acquisition of in-kind donations and discounts for

event participants.
Senior Mentorship Program (SMP) 2022 – Present
Design Team Lead Motilal Nehru National Institute Of Technology
• Managed SMP activities and organized the Instagram page of @MNNIT TIMES, increasing social media engagement

by 30%.
• Acted as a liaison between juniors and seniors, providing guidance on academic and extracurricular opportunities.
• Developed infographics and visual content to simplify and communicate complex information, improving

engagement and understanding among participants.

Achievements
• Achieved a distinguished ranking in prestigious international competitions like the International
Mathematics Olympiad (IMO), International Science Olympiad (ISO),
• Top 10 in Nirmaan - Event conducted under Avishkaar
• 2nd Runner up in Navachar
• 2nd and 3rd Position in Constrengtho: Mix Design and Strength Testing Event in Avishkar 2021 and 2022
• 2nd Position in Crack the Case: Case Study Event in Avishkar 2022
 SPENCER NAOREM +91-6909863045
Registration No.: 20211102 spencernaorem27@gmail.com
Bachelor of Technology (2025) spencer.20211102@mnnit.ac.in
Civil Engineering
Motilal Nehru National Institute of Technology Allahabad, Prayagraj

EDUCATION
• Motilal Nehru National Institute of Technology
Allahabad, Prayagraj Year: 2025 Bachelor
Current CPI: 6.63
of Technology in Civil Engineering
• 12th: - Passing Year: 2019
Pioneer Academy
Palace compound
Imphal East, Manipur Obtained Marks (%): 86.7
• 10th: - Passing Year: 2017
Martin Grammar School
Kakching, Manipur Obtained Marks (%): 83.8

EXPERIECE
• Interned at NPCC North East Zone (June-July,2023)
- Project management and Infrastructure development

SKILLS
Tools: MS-Excel, SQL, Power BI, MS-Power Point, AutoCAD
Others: Sketch-up, Video-editing
Soft skills: Communication, Writing, Poetry, English

AREAS OF INTEREST
Consulting, Project Management, Infrastructural development

POSITIONS of RESPONSIBILITIES
• President of the North-East society (not registered)
• Event organizing committee member of Swagat 2022.
• Member of the Football Team.

ACHIEVEMENTS
• 3rd position in Constrengtho(team event) under annual tech fest, Avishkar 2023
• 4th position in Terraquiz (team event) under annual technical fest, Avishkar 2023
• Special Mention in Model Indian Parliament under the annual literary Fest, Elloquence 2023
• Special Mention in Poetry Slam under Elloquence 2022
• Special Mention in Poetry Slam under Elloquence 2021

HOBBIES
Gardening, Football, Reading, Trekking
 SAHIL MEENA +91-9660043221
Registration No.: 20211114
Bachelor of Technology (2025) sahil.20211114@mnnit.ac.in
Civil Engineering
Motilal Nehru National Institute of Technology Allahabad, Prayagraj

EDUCATION
• Motilal Nehru National Institute of Technology
Allahabad, Prayagraj Year: 2025 Bachelor
Current CPI: 5.9
of Technology in Civil Engineering
• 12th: - Passing Year: 2020
Pragati Public Sr Sec
School
Kota, Rajhasthan Obtained Marks (%): 74.2
• 10th: - Passing Year: 2017
Kendriya Vidyalaya
PGNS, Wear Bengal Obtained Marks (%): 72.4

SKILLS
Tools: MS-Excel, SQL, Power BI, MS-Power Point, AutoCAD
Others: Sketch-up, Video-editing
Soft skills: Communication, Writing, Poetry, English

AREAS OF INTEREST
Consulting, Project Management, Infrastructural development

POSITIONS of RESPONSIBILITIES
• Event organizing committee member of Swagat 2022.
• Member of the Football Team.

ACHIEVEMENTS
• 3rd position in Constrengtho(team event) under annual tech fest, Avishkar 2023
• 4th position in Terraquiz (team event) under annual technical fest, Avishkar 2023
• Special Mention in Model Indian Parliament under the annual literary Fest, Elloquence 2023
• Special Mention in Poetry Slam under Elloquence 2022
• Special Mention in Poetry Slam under Elloquence 2021

HOBBIES
Gardening, Football, Reading, Trekking