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Article DOI:10.1038/s41598-023-47595-7 GitHub release (latest SemVer including pre-releases) PyPI PyPI - Python Version PyPI - License

Logo georeader

georeader is a package to process raster data from different satellite missions. georeader makes easy to read specific areas of your image, to reproject images from different satellites to a common grid (georeader.read), to go from vector to raster formats (georeader.vectorize and georeader.rasterize) or to do radiance to reflectance conversions (georeader.reflectance).

georeader is mainly used to process satellite data for scientific usage, to create ML-ready datasets and to implement end-to-end operational inference pipelines (e.g. the Kherson Dam Break floodmap). See georeader concepts and protocols for basic concepts and API.

Install

The core package dependencies are numpy, rasterio, shapely and geopandas.

pip install georeader-spaceml

Getting started

Read from a Sentinel-2 image a fixed size subimage on an specific lon,lat location:

from georeader.rasterio_reader import RasterioReader
from georeader import read

# S2 image from WorldFloodsv2 dataset
s2url = "https://huggingface.co/datasets/isp-uv-es/WorldFloodsv2/resolve/main/test/S2/EMSR264_18MIANDRIVAZODETAIL_DEL_v2.tif"
rst = RasterioReader(s2url)

# lazy loading bands
rst_rgb = rst.isel({"band": [3, 2, 1]}) # 1-based list as in rasterio

cords_read = (45.43, -19.53) # long, lat
crs_cords = "EPSG:4326"

# See also read.read_from_bounds, read.read_from_polygon for different ways of croping an image
data = read.read_from_center_coords(rst_rgb,
                                    cords_read, shape=(504, 1040),
                                    crs_center_coords=crs_cords)

data_memory = data.load() # this loads the data to memory

data_memory # GeoTensor object

>>  Transform: | 10.00, 0.00, 539910.00|
| 0.00,-10.00, 7842990.00|
| 0.00, 0.00, 1.00|
         Shape: (3, 504, 1040)
         Resolution: (10.0, 10.0)
         Bounds: (539910.0, 7837950.0, 550310.0, 7842990.0)
         CRS: EPSG:32738
         fill_value_default: 0


from georeader import plot
plot.show((data_memory / 3_500).clip(0, 1))
awesome georeader

Saving the GeoTensor as a COG GeoTIFF: 

from georeader.save import save_cog

# Supports writing in remote location (e.g. gs://bucket-name/s2_crop.tif)
save_cog(data_memory, "s2_crop.tif", descriptions=["B4","B3", "B2"])

Tutorials

Sentinel-2

Read rasters from different satellites

Used in other projects

Citation

If you find this code useful please cite: 

@article{portales-julia_global_2023,
    title = {Global flood extent segmentation in optical satellite images},
    volume = {13},
    issn = {2045-2322},
    doi = {10.1038/s41598-023-47595-7},
    number = {1},
    urldate = {2023-11-30},
    journal = {Scientific Reports},
    author = {Portalés-Julià, Enrique and Mateo-García, Gonzalo and Purcell, Cormac and Gómez-Chova, Luis},
    month = nov,
    year = {2023},
    pages = {20316},
}
@article{ruzicka_starcop_2023,
    title = {Semantic segmentation of methane plumes with hyperspectral machine learning models},
    volume = {13},
    issn = {2045-2322},
    url = {https://www.nature.com/articles/s41598-023-44918-6},
    doi = {10.1038/s41598-023-44918-6},
    number = {1},
    journal = {Scientific Reports},
    author = {Růžička, Vít and Mateo-Garcia, Gonzalo and Gómez-Chova, Luis and Vaughan, Anna, and Guanter, Luis and Markham, Andrew},
    month = nov,
    year = {2023},
    pages = {19999},
}

Licence

The georeader package is published under a GNU Lesser GPL v3 licence

georeader tutorials and notebooks are released under a Creative Commons non-commercial licence.

Acknowledgments

This research has been supported by the DEEPCLOUD project (PID2019-109026RB-I00) funded by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) and the European Union (NextGenerationEU).

DEEPCLOUD project (PID2019-109026RB-I00, University of Valencia) funded by MCIN/AEI/10.13039/501100011033.