RudraxDave/UrbanizationDetection_RoadnBuilding
Building and Road Extraction for Urban and Rural Development and Annotations of Imagery -Jan 2021 - Jun 2021 Associated with Bhaskaracharya Institute For Space Applications and Geo-Informatics -Utilizing Open-Source Datasets from Google Earth Engine & NASA USGS (Sentinel, Landsat-8) of 2 certain timestamps, equalizing tiff files by Histogram Eq. Method, Clustering data by PCA + K-means Methodology, trained and segmented Data by Deep Learning Algorithms with U-NET Architecture, computed results by confusion matrix and attaining accuracy 89 percentage. • For mapping from high resolution imagery or GIS database construction and its update, automatic object-based image analysis, also animated change- after Change Detection Model so users come to know how urbanization occurs or growth happens over a decade.
This project helps urban planners, geographers, and environmental scientists monitor urban growth by analyzing satellite imagery. It takes satellite data from sources like Google Earth Engine, Sentinel, and Landsat-8, processes it to identify roads and buildings, and outputs maps showing urbanization changes over time. The primary user would be someone involved in regional development, land-use planning, or environmental impact assessment.
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Use this if you need to automatically identify and map urban features like roads and buildings from satellite images to track development and land use changes.
Not ideal if you require real-time analysis or high-resolution imagery for very granular, street-level detail, as this focuses on broader regional urbanization patterns.
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May 05, 2021
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