A collection of tools to easliy acquire and crop large-scale satellite imagery into samples for machine/deep learning.
Attention: Make sure you have access to Google Earth Engine API.
This is an easy-to-use project for acquiring and cutting large-scale satellite imagery into samples for machine/deep learning. This project is mainly based on geemap
, gdal
, colab
and QGIS desktop
.
- Step 1: Prepare your region of interest (ROI) shapefiles. If your ROI is located in China, please try this link: Get shapefile of ROI.
- Step 2: Use QGIS desktop to create the grid of your ROI. If you don't familar with this processing, please explore more in this link: QGIS operation.
- Step 3: Upload the shapefile of the grids followed by last step to your GEE assets.
- Step 4: Use the
notebooks
in this project to download Sentinel 1/2 satelite images with preprocessing. - Step 5: Use the
.py
files in this project to crop the satelite images to samples.
Sentinel-1.ipynb
: use to download the Sentinel-1_GRD satelite imagery of your ROI.(VV,VH,UInt8)Sentinel-2.ipynb
: use to download the Sentinel-2 satelite imagery of your ROI.S2_process.py
: use to process Sentinel-2 images includingfloat32()toUInt16()
,Compress
.dataset_crop/crop_v2.py
: use to crop the satelite images to samples such as 512*512.
If you encounter any problem in using the Sat2samples or have any feedback, please contact:
- E-mail: 2309162135@geemail.one
- GEE: Awesome-GEE.
- Sentinel 1: Google-Sentinel-1.
- Sentinel 2: Google-Sentinel-2.
- Mapshaper: Mapshaper.