Geospatial Data Scientist · Remote Sensing · Climate Analytics
Turning satellite imagery into climate-action decisions. 3.5+ years building end-to-end spatial AI pipelines for Tamil Nadu's most ambitious land-use and sustainability initiatives.
I'm a Geospatial Data Scientist based in India, specialising in turning raw earth observation data into tools that governments and planners actually use. My work sits at the intersection of remote sensing, machine learning, and climate policy.
With a background in Chemical Engineering and Petroleum Refining, I bring a systems-thinking mindset to every spatial problem — from modelling watershed hydrology to classifying land cover at regional scale using Sentinel-2 imagery.
Over 3.5 years at Auroville Consulting, I've delivered end-to-end geospatial workflows for Tamil Nadu State Planning Commission, building frameworks that directly inform land-use policy and climate adaptation strategy.
Anna University · 2019–2021
Arunai Engineering College · 2015–2019
LULC classification · Watershed modelling · Supervised & unsupervised ML · CNN-based raster segmentation
Policy-ready maps and reports embedded into Tamil Nadu's spatial planning framework
Auroville Consulting × TN State Planning Commission
Regional-scale geospatial framework mapping Blue-Green Networks across Tamil Nadu — forests, wetlands, rivers — for flood regulation, carbon storage, and biodiversity.
Auroville Consulting
Land suitability assessment for distributed solar energy across Villupuram district. Policy advisory analytics supporting Tamil Nadu's transition to a net-zero energy future.
TN State Planning Commission
Multi-use suitability mapping of unused lands for forestation, agriculture, water harvesting, housing, industry, and ground-mounted solar across Kallakurichi district.
Personal · Open Source
Production-ready geospatial web platform for CRS detection and reprojection of raster and vector datasets. Built with FastAPI + Streamlit, containerised with Docker, deployed with HTTPS.
Personal · Open Source
Automated ML pipeline for mapping built-up areas from Sentinel-2 imagery using spectral indices, Random Forest classification, and Microsoft Planetary Computer STAC API.
Personal · Open Source
Deep learning pipeline for built-up area extraction using UNet semantic segmentation on multi-spectral Sentinel-2 imagery. Applied to Chennai for urban heat island analysis.
Available for geospatial AI projects, climate analytics research, and sustainability-focused data science work. Based in India, open to remote collaboration worldwide.