Techniques for deep learning with satellite & aerial imagery
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Updated
May 13, 2024
Techniques for deep learning with satellite & aerial imagery
A curated list of awesome tools, tutorials, code, projects, links, stuff about Earth Observation, Geospatial Satellite Imagery
Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.
framework for large-scale SAR satellite data processing
Satellite imagery for dummies.
Datasets for deep learning with satellite & aerial imagery
Software behind the RACE dashboard by ESA and the European Commission (https://race.esa.int), the Green Transition Information Factory - GTIF (https://gtif.esa.int), as well as the Earth Observing Dashboard by NASA, ESA, and JAXA (https://eodashboard.org)
Generalized data analysis workflow via a consistent easy to use interface.
AiTLAS implements state-of-the-art AI methods for exploratory and predictive analysis of satellite images.
Python scripts to download and preprocess air pollution concentration level data aquired from the Sentinel-5P mission
DSen2-CR: A network for removing clouds from Sentinel-2 images. This repo contains the model code, written in Python/Keras, as well as links to pre-trained checkpoints and the SEN12MS-CR dataset.
Multi-Class Semantic Segmentation on Dubai's Satellite Images.
Download and process GOES-16 and GOES-17 data from NOAA's archive on AWS using Python.
Interactive tools for spectral mixture analysis of multispectral raster data in Python
Algorithms for computing global land surface temperature and emissivity from NASA's Landsat satellite images with Python.
SlideRule Earth Example Noteboks: On-demand, cloud-based processing of satellite mission data (NASA ICESat-2, GEDI, ArcticDEM/REMA, HLS)
A PyTorch implementation of the Light Temporal Attention Encoder (L-TAE) for satellite image time series. classification
API to get enormous amount of high resolution satellite images from satellites.pro quickly through multi-threading! create map your own map dataset. Bringing data to Humans.
Tools for Downloading, Customizing, and Processing Time Series of Satellite Images from Landsat, MODIS, and Sentinel
Evapotranspiration (ET) models for use in python and with integration into Google Earth Engine
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