TESSERA is a foundation model that can process time-series satellite imagery for applications such as land classification and canopy height prediction. Developed at the University of Cambridge, it enables efficient extraction of temporal patterns from Earth observation

Server-Side GeoTIFF Rendering and Integration for Tile-Based Maps with ASP.NET Core

Semantic segmentation of road networks in high-resolution satellite images using a deep neural network architecture.

Nasa Space App Challange 24 - Champion @dhaka. Landsat Pulse is a web application that simplifies access to real-time Landsat satellite data, enabling users to monitor environmental changes and receive notifications for satellite passes and new data availability without needing technical expertise.

SAR image classification with optical domain transfer

Machine learning for pre and post-harvest crop stages in Ukraine via satellite Imagery

This is the code used in the can-sized satellite for team Idealite in the Canadian Satellite Design Challenge (CSDC).

Final Semester Dissertation Project – MCA, VIT | Deep Learning-based Water Body Detection from Satellite Images and Trend Analysis of Vellore's Water Spread

Code for our submission to Solafune: Identifying Deforestation Drivers ML competition

A Geo-AI engine for automated ESG and supply chain monitoring. This project uses a ResNet-101 model, fine-tuned on the EuroSAT satellite dataset, to classify land use and detect environmental risks like deforestation, helping enterprises meet regulatory requirements and enhance transparency.

Taking nasa orbital sattelite data and making a user friendly interface to view data

GeoShield is an advanced AI-driven platform designed for real-time landslide risk assessment using interactive satellite imagery and deep learning

Estimation of poverty rates in African countries using daytime satellite images