An "R" package for automatic download and preprocessing of MODIS Land Products Time Series

Using GEE to collect and discover land surface temperature data over European river basins.

MODIS Assimilation and Processing Engine

Using GEE to collect and discover land surface temperature data over custom location input.

Canada Wildfire Prediction Using Deep Learning

Research work for cloud and snow segmentation problem using meteorological satellite Electro-L №2 multispectral data, also suitable for GOES-16,17 multispectral data. This project includes all needed functions and utils for preprocessing multispectral data to make your own dataset for cloud and (or) snow segmentation problem

MODIS Mosaic of Antarctica

GEE code for pixel-based land cover classification with Random Forest (RF) algorithm, and for NDVI time series visualization.

Deep learning for Synthetic Aperture Radar(SAR) and Radiometry data. An Ensemble Convolutional Neural Network workflow is implemented with data acquisition, processing, labelling, creating model, training model and launching a model

Análisis y construcción de modelo de calidad del aire - Sitio: Comunidad de Valencia (España)

Relating (soil) droughts to spectral indices

Extract - Transform - Load (ETL) workflow integrating Earth Engine and Meteostat data to monitor wildfire-related conditions across Portugal.

Distributed Remote Sensing Processing

This project uses alternative data to monitor economic trends in Niger following the 2023 coup and civil unrest. It analyzes indicators such as conflict events, nighttime lights, crop conditions, and population movement patterns using geospatial, mobility, and remote sensing data to assess the impacts of conflict and instability.

MODIS NDVI data is processed to create a time series visualization for the Nyando catchment area within the Lake Victoria Basin.

UPC Artificial Intelligence with Deep Learning. Wildfire prediction using Semantic Segmentation on satellite imagery

NASA Space Apps Challenge 2020 submission. Team: Garlic Bread

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.

Jupyter notebooks created for use in a training session on the topic of drought forecasting via satellite:artificial_satellite:. This repo contains the scripts needed to pre-process MODIS data and apply Gaussian Processes to time-series in order to forecast VCI :chart_with_upwards_trend:.

ML emulator for MODIS spectral channels