- AWS Account
- Docker
- node + npm
# Build Amazon linux AMI docker container + Install Python modules + create package
git clone https://github.com/SpaceNetChallenge/ml-lambda-api.git
cd ml-lambda-all
make all
#configure serverless (https://serverless.com/framework/docs/providers/aws/guide/credentials/)
# Create account with permissions for SLS deployment
# Record api endpoint
npm install
sls deploy
Inputs:
- url: any valid url
example:
$ curl {you-endpoint}/stac/bounds?url=https://any-stac-item.json
{"url": "https://any-file.on/the-internet.tif", "bounds": [90.47546096087822, 23.803014490532913, 90.48441996322644, 23.80577697976369]}
Inputs:
- url: any valid url
- asset_key: asset key from STAC_ITEM : default = raster Options:
- rgb: select bands indexes to return (e.g: (1,2,3), (4,1,2))
- nodata: nodata value to create mask
example:
$ curl {you-endpoint}/stac/tiles/7/10/10.png?url=https://any-stac-item.json
Inputs:
- url: any valid url
- asset_key: asset key from STAC_ITEM : default = segmentation_mask
Outputs:
- pixel_count: Number of pixels of value out of 255
- object_count: Number of objects after polygonization
example:
$ curl {you-endpoint}/stac/tiles/7/10/10.png?url=https://any-stac-item.json&asset_key=segmentation_mask
{
"pixel_count": 555,
"object_count": 1000,
}
Inputs:
- url: any valid url
example:
$ curl {you-endpoint}/bounds?url=https://any-file.on/the-internet.tif
{"url": "https://any-file.on/the-internet.tif", "bounds": [90.47546096087822, 23.803014490532913, 90.48441996322644, 23.80577697976369]}
Inputs:
- url: any valid url
Options:
- rgb: select bands indexes to return (e.g: (1,2,3), (4,1,2))
- nodata: nodata value to create mask
example:
$ curl {you-endpoint}/tiles/7/10/10.png?url=https://any-file.on/the-internet.tif