AGENTIC AI P.01

Agentic Climate Risk Intelligence System

Multi-agent RAG pipeline ingesting 100k+ climate datasets (ERA5, CMIP6, Sentinel-2 LULC) into a vector store, enabling natural language querying of physical risk scores and emissions trajectories. A Bayesian inference layer handles uncertainty quantification across asset classes; LLM-based agents run autonomous ESG data validation; ReAct-style agentic loops drive TCFD/EU Taxonomy compliance checks; Monte Carlo stress testing supports derivatives desk positioning.

Multi-Agent RAGLLM AgentsReAct LoopERA5 / CMIP6Bayesian InferenceMonte CarloPythonGDAL / GEE
GEOSPATIAL ML P.02

Satellite-Based Exploration Platform

In-house geospatial application built for a geo-exploration division, replacing drone/on-site surveys with open-source satellite data to track vegetation regrowth and oil slicks. Vision Transformer models achieving 92%+ accuracy on multi-spectral imagery across 400+ reclamation sites.

Vision TransformersSentinel-2Landsat-9MaxarPostGISAWS S3
REMOTE SENSING P.03

Photogrammetric Species Identification

Photogrammetric species identification pipeline using Sentinel-1 C-band SAR and Sentinel-2 optical data to classify 17+ tree species in Indian forests. GDAL with Python for band-specific data fusion, Random Forest and SVM for refined multi-spectral classification.

Sentinel-1/2Random ForestSVMGDALPandas / NumPy
RESEARCH P.04

Deforestation Detection — M.Tech Thesis

Explored the potential of Sentinel-1 SAR and Sentinel-2 optical satellites for large-scale deforestation detection at IIT Indore's Earth Observation Lab. Multi-temporal analysis combining radar backscatter and spectral indices for change detection in tropical forest cover.

Sentinel-1/2SAR AnalysisChange DetectionEarth Observation
GIS AUTOMATION P.05

Automated GIS Pipeline — NYU Stern / Project Mumbai

Automated GIS pipeline using Python, GDAL, and PostGIS for NYU Stern's Project Mumbai, cutting 1,200+ manual hours/year. GANs enhance legacy satellite imagery from 1997+ for remote road detection. Cloud-native flood risk analysis with Monte Carlo simulations on GCP.

PostGISGDAL / GeoPandasGANs (ESRGAN)Monte CarloGCP
URBAN ANALYTICS P.06

Urban Analytics & COVID-19 Economic Impact

Quantified COVID-19's economic impact via nightlight drop analysis using NASA VIIRS data (−37.2% in Delhi). SAR-based urban expansion models across 1,000+ Sub-Saharan towns using PyQGIS/C++, with optimized PostGIS queries for high-throughput spatial analysis.

NASA VIIRS / MODISSARPyQGIS / C++PostGIS