A focused spatial analysis tool developed in Python using GeoPandas and Rasterio. This project processes Land Surface Temperature (LST) raster data to quantify the Urban Heat Island (UHI) effect. It accurately calculates the UHI magnitude by comparing urban and surrounding rural temperatures, providing critical insights for urban planning and climate resilience.

Access the Python scripts and methodology documentation on my GitHub.

A dynamic web application built with Flask and Leaflet that empowers users to assess flood risk at specific locations. It integrates a scikit-learn AI model to provide real-time risk scores (0-100) based on geographical inputs, visualizing high-risk zones directly on an interactive map. This project demonstrates proficiency in full-stack geospatial application development and AI-driven predictive analytics.

Find the complete source code and implementation details on my GitHub.

A functional  dashboard developed with Flask and Leaflet. This application fetches and visualizes real-time Air Quality Index (AQI) data from monitoring stations. Markers are color-coded according to health risk levels, providing users with an immediate, intuitive understanding of local air quality. It showcases skills in API integration, geospatial data handling, and dynamic web visualization.

The complete setup and source files are available on my GitHub.

A powerful machine learning model utilizing TensorFlow/Keras (CNN) for automated land use classification from satellite imagery. Trained to categorize areas into distinct classes like Urban, Forest, Water, and Agriculture, this project highlights expertise in deep learning for remote sensing. It provides highly accurate and scalable inference for large-scale environmental monitoring tasks.

Explore the model architecture and Python code on my GitHub.