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:.

NASA Space Apps Challenge 2020 submission. Team: Garlic Bread

Phoenix is a realtime forest-fire prediction app | NASA Space Apps Challenge 2020 | Global Nominee

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

This is the code used in my masters thesis research.

MODIS Mosaic of Antarctica

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

Distributed Remote Sensing Processing

Python package to crawl and analyze historical satellite images (MODIS) of volcano hotspots and filter clouds via spectral imaging

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

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

Relating (soil) droughts to spectral indices

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

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

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

Satellite Products capturing Nigeria's 2022 Flooding events

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

This repo contains javascript code used in Google Earth engine to perform various Geospatial Data analysis tasks on satellite data. The code utilizes Google earth engines own archive of Satellite data.

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

MODIS Assimilation and Processing Engine