buteo 0.8.15

Tools for merging Geospatial Analysis and AI.

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.

For documentation, visit: https://casperfibaek.github.io/buteo/

Dependencies
numba (https://numba.pydata.org/)
gdal (https://gdal.org/)

Installation
Using pip:

pip install buteo

Using conda:

conda install buteo --channel casperfibaek

Quickstart

import buteo as beo

OUTDIR = "path/to/output/dir"

# Reproject (and other functions) to references. (Vector and raster)
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, ...]

import buteo as beo

# Align, stack, and make patches from rasters

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(
    path_to_stacked_numpy_arrays,
    256,
    offsets_y=3,
    offsets_x=3,
)

# patches_nr, height, width, channels
patches
>>> np.ndarray([10000, 256, 256, 9])

import buteo as beo

# Predict a raster using a model

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,
)

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

Build steps

python -m run_tests; python -m build_documentation; python -m build; python -m twine upload dist/*;

python -m run_tests && python -m build_documentation python -m build && python -m twine upload dist/*

python -m build_anaconda -forge -clean;