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.

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

#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.

#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)