Nirixan
Industry
Geospatial Technology
Category
MLUX
Roles
Model Testing, Research, Design
Team
Anmol Gupta
Kunal Bhargava
Shashwat Verma
Adithi Reddy
Tools Used
Figma, Python,
TensorFlow & Keras,
OpenCV, QGIS
Building Footprint Detection Using AI and Geospatial Data
Harness the power of machine learning and satellite imagery to tackle environmental and urban planning challenges. This project utilizes cutting-edge image processing and deep learning to automate building footprint extraction, revolutionizing urban expansion monitoring and its environmental impacts. At the core of technology and sustainability, it merges geospatial analytics with AI for smarter urban development, conservation, and disaster management decisions. Driven by a commitment to improving geographical information systems, it's essential for planners, conservationists, and emergency responders. Featuring Convolutional Neural Networks (CNN), this initiative redefines remote sensing, pushing the limits of digital mapping and analysis. It presents a scalable solution, poised to transform our interaction with urban and natural landscapes, and spearheads sustainable, resilient community building.
Overview
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The Problem
Rapid urbanization and environmental changes present significant hurdles for sustainable development. Traditional methods for mapping and monitoring urban expansion are labor-intensive and often outdated, struggling to keep pace with the fast-evolving landscapes.
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Goals & Objectives
High Precision Mapping: Develop a machine learning model for accurate, real-time mapping of building footprints, distinguishing between buildings and other urban features, and reflecting urban changes promptly.
User-Centric Design: Design an application for a broad audience, featuring easy navigation, clear data visualizations, and interactive elements for detailed analysis.
Scalability and Integration: Ensure the system can handle growing data volumes, apply to various locations effortlessly, and integrate smoothly with existing urban planning tools for enhanced utility in sustainable development.
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Project Outline
Literature Review: Identifying Gaps in Geographic Data Collection
User Research: Understanding Stakeholder Needs and Challenges
Development Planning: Integrating Machine Learning with Satellite Imagery
Development: Building the Machine Learning Model for Footprint Detection
Design: Crafting a User-Friendly Application Interface
Bridging Technological Innovation and Urban Sustainability: A Foundational Research Overview
Our project began with an in-depth analysis of environmental sustainability, urban development, and technological innovation. We identified a critical need for real-time, accurate geographic data due to the slow pace of traditional data collection, especially in urban planning and environmental monitoring. Our review covered advancements in remote sensing, machine learning, and satellite imagery analysis, revealing a gap in applying these technologies together for environmental and urban challenges.
Key findings
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Interdisciplinary Gap Identification: Initial research highlighted a critical need for real-time, accurate geographic data amidst slow traditional collection methods, especially in urban planning and environmental monitoring.
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Technological Advancements: Exploration into remote sensing, machine learning, and satellite imagery analysis showed significant progress in individual fields but a lack of integration for environmental and urban planning solutions.
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Deep Learning's Potential: Studies on Convolutional Neural Networks (CNNs) demonstrated their effectiveness in image recognition and segmentation, suggesting a high potential for processing complex visual data.
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Satellite Imagery Successes: Case studies in environmental conservation using satellite imagery (e.g., deforestation tracking, wildlife monitoring) highlighted the importance of high-resolution geospatial data.
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Urban Infrastructure Challenges: Identifying detailed urban infrastructure, like building footprints, from satellite imagery faced hurdles such as variable image resolutions and architectural diversity.
Multidisciplinary Approach: The project's foundation was built on insights from environmental science, urban planning, computer science, and data analytics, aiming to create a technologically advanced tool responsive to real-world urban and environmental planning needs.
User Research
The objective of this user research was to gather insights from industry professionals in the fields of urban planning, environmental sustainability, GIS, and related domains regarding their perspectives, needs, challenges, and expectations regarding a tool for extracting detailed urban infrastructure information from satellite imagery.
Methodology
Semi-structured interviews were conducted with 15 industry professionals and survey were taken of 30 industry professionals remotely via video conferencing or phone calls. The interviews lasted approximately 30-45 minutes each and covered predefined questions, allowing flexibility for participants to share additional insights and experiences.
Current Practices
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The majority of participants highlighted the reliance on traditional methods for collecting and analyzing geographic data, including manual surveys and aerial photography.
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Some participants mentioned using GIS software for basic analysis but expressed frustration with the limitations of these tools in handling large-scale data processing.
Challenges
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The main challenges identified included the time-consuming nature of manual data collection, the limited availability of high-resolution satellite imagery, and difficulties in accurately interpreting complex urban landscapes.
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Participants also cited issues related to data compatibility, such as integrating data from different sources and formats.
Expectations
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Participants expressed a strong interest in a tool that could automate the extraction of building footprints and other urban features from satellite imagery.
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Key expectations included high accuracy and precision in feature extraction, user-friendly interface design, and seamless integration with existing GIS workflows.
Data Collection:
Sourced high-resolution satellite images from diverse geographical regions.
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Preprocessing:
Cleaned and normalized images; enhanced structural features.
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Model Development:
Implemented a deep learning model, focusing on Convolutional Neural Networks (CNNs) for image segmentation.
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Training and Validation:
Utilized a split dataset approach for model training and validation to ensure accuracy and generalizability.
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System Design:
Developed a user-friendly interface for easy upload and processing of satellite images.
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Feedback Integration:
Incorporated user and stakeholder feedback for continuous improvement.
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Developement Methodology
From Satellite Imagery to Precise Maps: Unlocking Urban Patterns
Gathering and Refining Data for Clarity
Sourced diverse high-resolution satellite imagery, standardized image sizes, normalized pixel values, and applied data augmentation to build a robust dataset.
Crafting the Model: Choosing the Best Architectural Fit
Selected the U-Net model for its precision in semantic segmentation, outperforming others in accuracy and efficiency for complex urban landscapes.
Strategic Model Training for Optimal Results
Implemented a structured training approach with a 70-15-15 data split, adaptive learning rates, and early stopping to hone model effectiveness.
Fine-Tuning for Peak Performance
Employed grid search for hyperparameter optimization, focusing on improving the Intersection over Union (IoU) metric for better accuracy.
Rigorous Evaluation: Testing for Reliability and Accuracy
Conducted extensive validation and testing, demonstrating the model's high precision in building footprint detection with an IoU exceeding 80%.
Design & Implementation
We designed a user-friendly interface that allows users to upload satellite images and receive processed images with building footprints highlighted. The system was implemented using Python for the backend processing, with TensorFlow and Keras for model development, and a web-based frontend for user interaction.
Wireframes
Nirikshan's wireframes provide a visual roadmap for the platform's interface design. They outline the layout and functionality of key features such as data upload, analysis tools, results display, and user profiles. These wireframes serve as a collaborative tool, ensuring that Nirikshan's interface meets the specific needs of users in urban planning and environmental sustainability
User Interface
We designed a web based user interface using HTML, CSS and JavaScript that allows users to upload satellite images and receive processed images with building footprints highlighted.
Conclusion and Future Work
The successful implementation of our building footprint detection system represents a significant stride forward in improving environmental monitoring and urban planning. Despite constraints limiting our user reach, we recognize the immense potential for further refinement through more extensive user research.
A more detailed user research effort holds the key to unlocking greater insights and ensuring the continued evolution of our system. By engaging a broader spectrum of users, we can gain a deeper understanding of diverse perspectives, preferences, and pain points. This comprehensive approach empowers us to tailor our system precisely to the needs of our user base, enhancing its usability, effectiveness, and overall impact.