Geospatial Data Analysis for detecting and analyzing coal mines.
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Updated
Nov 30, 2021 - Jupyter Notebook
Geospatial Data Analysis for detecting and analyzing coal mines.
AI-powered urban growth monitoring using Sentinel-2, Google Earth Engine, NDBI, NDVI, and Gemini AI for automated urban assessment.
An AI-powered geospatial intelligence platform that enables satellite data analysis through natural language using Google Earth Engine, LLMs, and interactive visualizations. Users can perform vegetation, flood, land cover, urban heat, and environmental analysis without writing geospatial code.
Remote sensing index computation and visualization using Google Earth Engine, including spectral indices for land cover and environmental analysis
Interactive building density heatmap for Algiers using OSM footprints, Sentinel-2 NDBI, and Streamlit + Folium
This script processes Sentinel-2 satellite imagery to calculate various indices (NDVI, MNDWI, SAVI, NDBI).
🛰️ Integrated Google Earth Engine & Python workflow for Landsat 8 NDVI, NDBI and LST spatiotemporal analysis in Denpasar, Bali (2013–2025).
Remote sensing Beirut's urban change from 1985 to 2011
Satellite-based mapping of high-confidence urban expansion across Delhi NCR from 2015–2025 using Landsat 8, spectral indices, Random Forest classification, and Google Earth Engine.
IndexRidge Sentinel-derived GeoTIFF workflow samples — free mini sample, bundles, and QGIS/rasterio downloads
Cloud-aware Sentinel-2 imagery enhancement and spectral analysis using Google Earth Engine.
NDBI (built-up index) analysis of Delhi from Sentinel-2 imagery using GDAL
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