GIS Engineer specializing in environmental change detection, flood risk assessment, and automated geospatial workflows. I bridge the gap between Civil Engineering and Data Science.
I am a GIS Engineer with a strong foundation in Civil Engineering (M.Tech, IIT Guwahati). My passion lies in applying geospatial intelligence to solve real-world environmental and engineering challenges.
Currently at SWANSAT, I develop automated workflows for environmental monitoring, utilizing multi-temporal satellite data (SAR, Optical, Thermal). My work directly supports risk assessment models for landslides and agricultural yield forecasting.
I have expertise in 1D/2D Hydraulic Modelling, Flood Risk Assessment, and Hydrology, combining traditional engineering principles with modern Python-based geospatial tools.
Decoding the earth through satellite imagery and data science.
Innovations in Flood Risk Assessment and Geospatial Automation.
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Full-stack geospatial web application using Streamlit and GEE API. Implemented backend algorithms for real-time Vegetation Health Index (VHI) monitoring, Soil moisture retrieval, and Flood Mapping using ML/DL tools.
Serverless geospatial ETL web application to automate conversion of vector datasets (Shapefile/GeoJSON/KML) and solve CRS reprojection issues for interoperability.
Utilizes satellite data to support sustainable water conservation. Integrated open-source DEM, SAR, and Optical datasets to identify optimal locations for rainwater harvesting structures.
Interactive geospatial web module to visualize custom datasets and layers. Designed a lightweight, responsive HTML map interface ensuring seamless user experience.
Automated quantification of desiccation crack patterns in clayey soils using advanced image processing techniques.
Comprehensive utility for editing, converting, and managing Images, PDFs, and PowerPoint presentations.
SWANSAT (OPC) Pvt Ltd. | Mumbai, India
Indian Institute of Technology Guwahati
Rajkiya Engineering College
Kumar, N., Ahmad, A., Pal, A. K., Nair, A. M.
Used Sentinel-2 imagery to detect anomalies employing a fuzzy set-based approach to identify potential oil and gas reservoirs.
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