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Geospatial Data Scientist, Remote Sensing & GIS consultant, Deep Learning, ML, AI prompt Engineering PhD, Research Scholar, University of Delhi
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Delhi School of Economics, University of Delhi
Geospatial Data Scientist
June 12, 2026 – Present
apis_Agent_1_CV_Phraser
February 10, 2026 – Present
apis_Agent_1_CV_Phraser — GitHub repository
View ProjectDSHAT-Code-of-Computer-Vision-for-DEM-Super-resolution
October 21, 2025 – October 21, 2025
The DS-HAT framework is dedicated to the Super-Resolution (SR) stage of the pipeline, which effectively enhances the spatial resolution of globally available low-resolution DEMs.
View ProjectCRPAG-Code-of-Computer-Vision-for-DEM-void-filling
September 25, 2025 – September 25, 2025
The Conditional Residual Pyramid Attentional Generator (CRPAG) is a specialized deep learning framework designed to reconstruct voids in Digital Elevation Models (DEMs) with high accuracy and terrain fidelity. Unlike conventional methods (e.g., interpolation, U-Nets, CNNs), CRPAG leverages multi-modal fusion, to deliver state-of-the-art performance
View ProjectNASA-IMERG-Data-Extraction-Pipeline-and-Cloudburst-Detection
August 24, 2025 – August 24, 2025
Detailed Tutorial with complete visualization of how hourly NASA IMERG rainfall (NetCDF format) was extracted for a local area, analyzed at the grid-cell level, flagged for cloudbursts (≥ 100 mm/day), and published as an interactive Plotly/Streamlit dashboard.
View ProjectDeep-Ensemble-Learning-model-for-Drought-Prediction
July 3, 2025 – July 3, 2025
Ensemble Deep Learning is a highly efficient method in ML and Deep Learning (DL) that leverages this combined knowledge of multiple DL models to enhance the accuracy of predictions. It combines several classifiers to address biases, limit overfitting, and improve robustness.
View ProjectERA5_data_extraction_and_post_processing
June 12, 2025 – June 12, 2025
This repository provides a comprehensive Python script for extracting, processing, and analyzing ERA5 hourly reanalysis data and how to load GRIB files, filter specific meteorological variables, perform necessary unit conversions, merge datasets, visualize time series, and efficiently save the processed data into the cloud-optimized Zarr format.
View ProjectFlood-Prediction-using-Random-Forest-Algorithm
June 8, 2025 – June 8, 2025
This code demonstrates a streamlined workflow for building and deploying machine learning models for geospatial predictions, moving seamlessly from vector data input to raster output.
View ProjectGeospatial-Data-Balancing-for-Machine-Learning-A-Spatially-Stratified-Sampling-Approach
June 7, 2025 – June 7, 2025
This repository presents a novel and scientifically robust methodology for addressing class imbalance in geospatial machine learning datasets, particularly those where rare events (minority class) are significantly outnumbered by non-events (majority class). The approach integrates spatial buffering and K-Means clustering.if
View Project-9999-GIS-Data-Cleaner
May 18, 2025 – May 18, 2025
A GIS data cleaning tool for replacing -9999 values and visualizing missing data.
View ProjectGEOAI-Driven-Rainfall-Time-Series-Extraction-and-Zarr-Based-Geospatial-Stacking
April 30, 2025 – June 5, 2025
Step by step GIS execution through Python
View ProjectCultural Fit Analysis
The candidate's projects are highly aligned with the 'Geospatial Data Scientist' target role, demonstrating a deep interest and practical experience in the field. The diversity of projects, ranging from computer vision for DEMs to climate data analysis and flood prediction, indicates a broad understanding of geospatial challenges. However, all listed projects are personal, and there is only one current professional experience entry with a future start date, which limits the assessment of collaborative work environments or broader organizational fit. The lack of diverse technologies beyond Jupyter Notebook and Python might indicate a narrower toolset, potentially impacting adaptability to different team tech stacks.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong focus on problem-solving within the geospatial domain. The nature of the projects suggests an ability to work independently on complex technical challenges. However, without direct assessment data, specific soft skills like teamwork, leadership, or stress handling cannot be definitively evaluated.