A geospatial raster processing library for machine learning

A scalable implimentation of HANTS for time sereis reconstruction in remote sensing on Google Earth Engine platform

Python coding that takes images acquired using a Near-Infrared (NIR) converted camera and generates a modified Normalized Differential Vegetation Index (NDVI). Contains standalone with colorbar legend and batch versions. ENDVI and SAVI Indexes also available and with greyscale options.

Get Sentinel-2 data from Google Earth Engine or from cloud-optimized geotiffs in AWS Open data registry maintained by Element84, and compute biophysical parameters using SNAP Biophysical processor algorithm.

QGIS module for calculating Vegetation Indexes on Sentinel-2 multispectral images. There are two branches: "Master" which supports photographs downloaded from scihub and "landviewer" which stands for photographs downloaded from eos-landviewer.

ECOSTRESS Collection 2 STARS Data Fusion Product Generating Executable (PGE)

Feed an AOI --> get the vegetation report

Using time series machine learning models to predict crop growth and processing image field data collected by IoT units as part of an AgAID internship

ECOSTRESS Collection 2 STARS Data Fusion Product Generating Executable (PGE)

Spatiotemporal Analysis of Agricultural Drought Severity and Hotspots in Somaliland. It integrates MODIS-derived vegetation indices and CHIRPS precipitation data to identify and assess drought severity and hotspots over time.

📝 Simplify your note-taking with pyNotes, a user-friendly desktop app for adding, editing, and organizing notes using Python and PyQt6.

Calculating NDVI(vegetation index in russian)/ Вычисление NDVI (вегетационного индекса)

AI-powered dashboard for forecasting vegetation and wind patterns using machine learning. Supports real-time sensor simulation and adaptive climate response insights.

Script for automatic processing of Sentinel 2 images from Open Hub.