Remote Sensing

An Open-Source project for monitoring field health and growth stage development. I used real-time Sentinel-2 imagery, Python, Streamlit, GeoPandas, and JavaScript to create a web app demo to showcase the product. The web app takes a GeoJSON file as input and returns a map with the field boundaries and the NDVI, LAI, and True Color images for each field.

This study aims to bridge this research gap through the development of a robust methodology that leverages remote sensing imagery and machine learning classification. We present a comprehensive model, which we employed to derive estimates of cultivated agricultural land in Gezira state, spanning the timeframe from 2019 to 2023.