There are 0 repository under modis-data topic.
Using GEE to collect and discover land surface temperature data over European river basins.
Using GEE to collect and discover land surface temperature data over custom location input.
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
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
MODIS Mosaic of Antarctica
GEE code for pixel-based land cover classification with Random Forest (RF) algorithm, and for NDVI time series visualization.
Análisis y construcción de modelo de calidad del aire - Sitio: Comunidad de Valencia (España)
Distributed Remote Sensing Processing
NASA Space Apps Challenge 2020 submission. Team: Garlic Bread
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:.
Python package to crawl and analyze historical satellite images (MODIS) of volcano hotspots and filter clouds via spectral imaging
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.
UPC Artificial Intelligence with Deep Learning. Wildfire prediction using Semantic Segmentation on satellite imagery
This is the code used in my masters thesis research.
Phoenix is a realtime forest-fire prediction app | NASA Space Apps Challenge 2020 | Global Nominee
Relating (soil) droughts to spectral indices