#数据仓库#Techniques for deep learning with satellite & aerial imagery
GDAL is an open source MIT licensed translator library for raster and vector geospatial data formats.
翻译 - GDAL是开源的X / MIT许可翻译器库，用于栅格和矢量地理空间数据格式。
#计算机科学#Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
翻译 - 用于执行大规模对象检测/实例分割的轻量级视觉库。
#Awesome#Long list of geospatial tools and resources
#计算机科学#🛰️ List of satellite image training datasets with annotations for computer vision and deep learning
A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
翻译 - 用于与Google Earth Engine，ipyleaflet和ipywidgets进行交互式映射的Python程序包
#数据仓库#TorchGeo: datasets, samplers, transforms, and pre-trained models for geospatial data
翻译 - TorchGeo：地理空间数据的数据集、转换和模型
#计算机科学#An open source library and framework for deep learning on satellite and aerial imagery.
List of datasets, codes, and contests related to remote sensing change detection
A collection of 360+ Jupyter Python notebook examples for using Google Earth Engine with interactive mapping
翻译 - 300多个Jupyter Python笔记本示例的集合，这些示例将Google Earth Engine与交互式映射一起使用
#Awesome#A curated list of awesome tools, tutorials, code, projects, links, stuff about Earth Observation, Geospatial Satellite Imagery
翻译 - 精选的工具，教程，代码，有用的项目，链接，有关地球观测和地理空间的内容清单！
A curated list of Google Earth Engine resources
翻译 - 精选的Google Earth Engine资源列表
Search and download Copernicus Sentinel satellite images
An advanced geospatial data analysis platform
A ready-to-use curated list of Spectral Indices for Remote Sensing applications.
A collection of 300+ Python examples for using Google Earth Engine in QGIS
翻译 - 290多个在QGIS中使用Google Earth Engine的Python示例的集合
#计算机科学#GRASS - free and open-source geospatial processing engine
翻译 - GRASS GIS-免费和开源的地理信息系统（GIS）
#Awesome#Community Datasets added by users and made available for use at large
#计算机科学#A review of change detection methods, including codes and open data sets for deep learning. From paper: change detection based on artificial intelligence: state-of-the-art and challenges.
#数据仓库#Datasets for deep learning with satellite & aerial imagery