Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.

Algorithms for computing global land surface temperature and emissivity from NASA's Landsat satellite images with Python.

An introduction to script-based satellite image processing using Python

The official code repository for paper "Rethinking the Encoding of Satellite Image Time Series".

Retrieves 3D cloud properties from multi-angle images of reflected solar radiation

A list of all the scale and offset parameters for each raster dataset in Google Earth Engine.

Satellite Image Analytics and Earth Data Science Experiments in Python

Multiband spectro-radiometric satellite image analysis with K-means cluster algorithm

Tool package to download and read Geostationary Operational Environmental Satellite 2nd, 3rd and 4th Generation (GOES-8 to GOES-19) and Gridded Satellite (GridSat-B1 and GridSat-GOES) imagery datasets from NOAA's AWS and NCEI cloud repositories using Python.

Geo-spatial utilities for Remote Sensing and GIS application

Computer vision and satellite imagery analysis for precision agriculture. Detects crop diseases, monitors soil health, predicts yields, and optimizes irrigation using drone imagery and multispectral data.

Earth Observation Project

PhenoFit Pro is a browser-based web application for interactive phenological data analysis.

Script per l'analisi di foto aeree e immagini satellitari per l'automazione della fotointerpretazione; algoritmi classici e basati su machine learning.

Project to investigate and develop spatial data-driven Geo-AI models (Convolutional Neural Network) to identify urban greenspace from Satellite images by integrating multiple data sources (e.g. vector data of urban parks)

Funções práticas para uso do Earth Engine e geemap. Practical functions for working with Earth Engine and geemap.

Jupyter notebook walk-through of doing satellite imagery analysis.

A Python workflow using the Google Earth Engine API to generate Landsat 8 timelapse videos and surface change metrics for predefined regions. Includes automated date filtering, cloud masking, and export of spectral index summaries for vegetation, water, and urban change analysis.

Train AI models on satellite image dataset to classify different types of land.

Satellite imagery analysis combined with geological and archaeological data to identify potential undiscovered ancient civilization sites using multi-modal fusion.