BACHELOR OF TECHNOLOGY
IN
COMPUTER SCIENCE ENGINEERING
6th Semester
SYNOPSIS
ON
Margdrishti
Submitted to:
Submitted by:
1
INDEX
Contents Page No.
1. Introduction 3
2. Objective 4
3. Tools and Platform 5
4. Hardware and Software Requirement 6
5. Data Flow Diagram 7
6. Modules 8
7. Future Scope 9
8. Reference 10
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INTRODUCTION
Margdrishti is a state-of-the-art system designed to automatically assess
road quality using Street View images (e.g., Google Street View) and
Satellite images. As infrastructure in cities and rural areas continues to
age, monitoring and maintaining roads has become an increasingly
important task for urban planners, local authorities, and government
agencies. Traditionally, assessing road conditions is a manual process,
which is both time-consuming and costly. By harnessing the power of
Artificial Intelligence (AI) and Machine Learning (ML), this system
automates the process of road inspection and damage detection.
Using Street View and Satellite images, this system aims to detect defects
such as potholes, cracks, wear and tear, surface irregularities, and other
road damages that compromise road safety. Through image processing
and deep learning algorithms, the system can analyze the quality of road
surfaces in real-time and provide insights that enable faster decision-
making for road repairs. By integrating these diverse data sources (Street
View and Satellite), it provides a holistic and comprehensive view of road
conditions, making it easier to prioritize maintenance and improve overall
infrastructure management.
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OBJECTIVE
1. Automate Road Condition Detection: Create a system capable of automatically
detecting various road defects, such as potholes, cracks, and surface irregularities
from both street and satellite images.
2. Data Fusion from Multiple Image Sources: Combine street-level imagery (e.g.,
Google Street View) with satellite images to provide a more comprehensive
assessment of the road conditions.
3. Image Preprocessing and Feature Extraction: Enhance and preprocess images to
ensure that relevant features (such as road surface cracks or potholes) are visible and
can be accurately detected by machine learning models.
4. Develop and Train Machine Learning Models: Implement deep learning
techniques, specifically convolutional neural networks (CNNs), to identify patterns
and classify road conditions from the images.
5. Provide Actionable Insights: Develop a user interface (UI) that allows stakeholders
(e.g., local authorities, urban planners) to view the analysis results and make data-
driven decisions for road maintenance.
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TOOLS/PLATFORM
Margdrishti is developed in Python, leveraging its powerful ecosystem for image processing,
machine learning, and geospatial analysis. OpenCV is used for image preprocessing tasks
like noise reduction and edge detection, while TensorFlow or PyTorch powers the deep
learning models for road condition classification. Geopandas and Folium assist in mapping
and visualizing road conditions. Additionally, Google Earth Engine API or Mapbox API
enables access to high-resolution satellite and street view images, making large-scale road
analysis feasible.
The project is designed to run on Windows, ensuring compatibility with AI frameworks, GPU
acceleration, and development tools. Anaconda provides an isolated environment for
dependency management, preventing conflicts between libraries. Development environments
like PyCharm or VS Code streamline coding with debugging and version control. For
hardware acceleration, CUDA-enabled GPUs can be used to speed up deep learning model
training and inference, significantly improving performance.
For data management, SQLite, PostgreSQL, or Firebase can store processed images, model
predictions, and user inputs. Flask or Django can be used for building a web-based interface,
allowing users to interact with the system easily. With these tools and platforms, the project
ensures efficient image processing, accurate road condition classification, and user-friendly
interaction.
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HARDWARE & SOFTWARE REQUIREMENTS
HARDWRE:-
DESCRIPTION CAPACITY
MODEL HP
PROCESSOR AMD Ryzen 3 3250U with Radeon
Graphics MEMORY (RAM) 8.00 GB
HARD DISK 512GB
KEYBOARD 115 KEY
MOUSE OPTICAL
MONITOR CRT
USB Flash usb port
SOFTWARE:-
The language which we shall be use to make this project is “Python” language.
We can use the Microsoft office.
We have used window 11 operating system.
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DATA FLOW DIAGRAM
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MODULES
Margdrishti is developed in Python, leveraging its powerful ecosystem for image processing,
machine learning, and geospatial analysis. OpenCV is used for image preprocessing tasks like
noise reduction and edge detection, while TensorFlow or PyTorch powers the deep learning
models for road condition classification. Geopandas and Folium assist in mapping and
visualizing road conditions. Additionally, Google Earth Engine API or Mapbox API enables
access to high-resolution satellite and street view images, making large-scale road analysis
feasible.
The project is designed to run on Windows, ensuring compatibility with AI frameworks, GPU
acceleration, and development tools. Anaconda provides an isolated environment for
dependency management, preventing conflicts between libraries. Development environments
like PyCharm or VS Code streamline coding with debugging and version control. For
hardware acceleration, CUDA-enabled GPUs can be used to speed up deep learning model
training and inference, significantly improving performance.
For data management, SQLite, PostgreSQL, or Firebase can store processed images, model
predictions, and user inputs. Flask or Django can be used for building a web-based interface,
allowing users to interact with the system easily. With these tools and platforms, the project
ensures efficient image processing, accurate road condition classification, and user-friendly
interaction.
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FUTURE SCOPE
The AI-powered Road Conditions Analyzer has significant potential for expansion. Future
improvements could include real-time analysis, better accuracy with advanced deep learning
models, and integration with smart city infrastructure. Enhanced geospatial analytics and
multi-source data fusion can further improve road monitoring and maintenance efficiency.
Potential Enhancements
• Real-time road condition monitoring using live traffic cameras and drone feeds.
• Improved accuracy with transformers and self-supervised learning models.
• Integration with IoT sensors for on-ground condition validation.
• Cloud-based deployment for scalable processing and global accessibility.
• Automated report generation with predictive maintenance recommendations.
Extended Applications
• Smart city planning and infrastructure optimization.
• Road safety improvements by identifying high-risk areas.
• Assistance for autonomous vehicles in navigation and obstacle detection.
• Government and municipal use for proactive road maintenance.
• Insurance and logistics industries for route optimization and risk assessment.
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REFERENCES
• www.kaggle.com
• docs.python.org
• flask.palletprojects.com
• www.wikipedia.org
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