satellite-data

#数据仓库#Techniques for deep learning with satellite & aerial imagery

Satellite imagery for dummies.

#Awesome#A curated list of awesome tools, tutorials, code, projects, links, stuff about Earth Observation, Geospatial Satellite Imagery

#数据仓库#Datasets for deep learning with satellite & aerial imagery

Satellite tracker and pass predictor for Android, inspired by Gpredict

framework for large-scale SAR satellite data processing

#计算机科学#Implementation of Machine Learning and Deep Learning techniques to find insights from the satellite data.

#下载器#Download and process GOES-16 and GOES-17 data from NOAA's archive on AWS using Python.

#计算机科学#AiTLAS implements state-of-the-art AI methods for exploratory and predictive analysis of satellite images.

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

#计算机科学#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.

Generalized data analysis workflow via a consistent easy to use interface.

#网络爬虫#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.

A comprehensive list of NASA Earth science data products

#计算机科学#Maritime vessel detection from remote sensing SAR data, based on the architectures of the Faster-RCNN and YOLOv5 networks.

Interactive tools for spectral mixture analysis of multispectral raster data in Python

#计算机科学#A PyTorch implementation of the Light Temporal Attention Encoder (L-TAE) for satellite image time series. classification

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 Das...

#计算机科学#An open-source benchmark framework for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO)