Buteo - Geospatial Analysis Meets AI
Buteo is a toolbox designed to simplify the process of working with geospatial data in machine learning. It includes tools for reading, writing, and processing geospatial data, as well as tools for creating labels from vector data and generating patches from geospatial data. Buteo makes it easy to ingest data, create training data, and perform inference on geospatial data.
Please note that Buteo is under active development, and its API may not be entirely stable. Feel free to report any bugs or suggest improvements.
When using, please pin the version of Buteo you are using to avoid breaking changes.
For documentation, visit: https://casperfibaek.github.io/buteo/
Fibaek, Casper. (2024). Buteo: Geospatial Data Analysis Framework for AI/EO. (v0.9.63). Zenodo. https://doi.org/10.5281/zenodo.7936577
Dependencies
numba (https://numba.pydata.org/)
gdal (https://gdal.org/)
Installation
Using pip:
pip install gdal
pip install buteo
Using conda:
conda install gdal
pip install buteo
Quickstart
Reproject (and other functions) to references. (Vector and raster)
import buteo as beo
OUTDIR = "path/to/output/dir"
vector_file_correct_projection = "path/to/vector/file.gpkg"
raster_files_wrong_projection = "path/to/raster/files/*.tif:glob"
paths_to_reprojected_rasters = beo.reproject_raster(
raster_files_with_wrong_projection,
vector_file_with_correct_projection,
out_path=outdir
)
paths_to_reprojected_rasters
>>> [path/to/output/dir/file1.tif, path/to/output/dir/file2.tif, ...]
Align, stack, and make patches from rasters
import buteo as beo
SRCDIR = "path/to/src/dir/"
paths_to_aligned_rasters_in_memory = beo.align_rasters(
SRCDIR + "*.tif:glob",
)
stacked_numpy_arrays = beo.raster_to_array(
paths_to_aligned_rasters_in_memory,
)
patches = beo.array_to_patches(
stacked_numpy_arrays,
256,
offsets_y=1, # 1 overlap at 1/2 patch size (128)
offsets_x=1, # 1 overlap at 1/2 patch size (128)
)
# patches_nr, height, width, channels
patches
>>> np.ndarray([10000, 256, 256, 9])
Predict a raster using a model
import buteo as beo
RASTER_PATH = "path/to/raster/raster.tif"
RASTER_OUT_PATH = "path/to/raster/raster_pred.tif"
array = beo.raster_to_array(RASTER_PATH)
callback = model.predict # from pytorch, keras, etc..
# Predict the raster using overlaps, and borders.
# Merge using different methods. (median, mad, mean, mode, ...)
predicted = predict_array(
array,
callback,
tile_size=256,
)
# Write the predicted raster to disk
beo.array_to_raster(
predicted,
reference=RASTER_PATH,
out_path=RASTER_OUT_PATH,
)
# Path to the predicted raster
>>> "path/to/raster/raster_pred.tif"
| Example Colabs | |
|---|---|
| Create labels from OpenStreetMap data |  |
| Scheduled cleaning of geospatial data | |
| Clip and remove noise from rasters | |
| Sharpen nightlights data | |
| Filters and morphological operations | |
The toolbox is being developed by ESA-Philab, NIRAS, and Aalborg University.
Dependencies
gdal numba
optional: orfeo-toolbox esa-snap
1 maintainer
Metadata
1 maintainer
Release files for buteo 0.9.64
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| buteo-0.9.64.tar.gz | 162.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| buteo-0.9.64-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 370.7 kB
Release files / buteo-0.9.64.tar.gz
| Download URL | buteo-0.9.64.tar.gz |
|---|---|
| Size | 162.1 kB |
| Tags | Source |
|
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| Uploaded via |
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Release files / buteo-0.9.64-py3-none-any.whl
| Download URL | buteo-0.9.64-py3-none-any.whl |
|---|---|
| Size | 208.6 kB |
| Tags | Python 3 |
|
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