isp-uv-es/spacenet5-taco

SpaceNet 5: Road Networks and Route Travel Time

This is a repackaging, not a new dataset. It is SpaceNet 5: Road Networks and Route Travel Time by SpaceNet LLC / CosmiQ Works, Maxar (WorldView-3), converted to TACO. Pixel values and labels are kept as released except where the description below says otherwise. All credit belongs to the original authors: if you use it, please cite them and follow their licence.

original dataset · paper · licence: CC-BY-SA-4.0

Repackaged into TACO by the Image and Signal Processing Group (ISP), Universitat de València, within the ELLIOT project.

Citation

Please cite the original work:

@article{vanetten2018spacenet,
  title   = {SpaceNet: A Remote Sensing Dataset and Challenge Series},
  author  = {Van Etten, Adam and Lindenbaum, Dave and Bacastow, Todd M.},
  journal = {arXiv:1807.01232},
  year    = {2018}
}

About the data

2369 WorldView-3 chips over Moscow and Mumbai. Each carries a 1300x1300 panchromatic band at 0.31 m, the co-located 325x325 8-band multispectral cube at 1.24 m, and the road centrelines through it with SpaceNet's inferred speed per segment.

2,369 samples · splits: test 498 · train 1,391 · validation 480 · tasks: semantic-segmentation

Packaged as TACO v3.

Full description

Target. The same products and layout as SpaceNet 3: a 2 m road mask buffered in pixel space, the polylines kept so the graph survives, and speed per segment rather than as a raster. The two releases therefore read alike and extend the benchmark from four cities to six.

Splits. The challenge test chips ship without labels, so the split here is not the publisher's: it is cut by longitude within each city. Train 1391, val 480, test 498.

Held-out half

spacenet5_test holds the 604 samples whose targets the publisher withheld. Same inputs, no target slots: it is there to be predicted on and submitted, and it belongs in no training mixture.

import os
from huggingface_hub import snapshot_download
from taco.ml import Dataset

root = snapshot_download("isp-uv-es/spacenet5-taco", repo_type="dataset",
                         allow_patterns=["spacenet5_test", "spacenet5_test/*", "spacenet5_test/.tacocat/*"])
ds = Dataset(os.path.join(root, "spacenet5_test"))

Getting started

git clone --recursive https://github.com/OscarPellicer/taco
pip install -e "taco/python[ml]"     # builds the reader: C++23, CMake, Ninja, pkg-config, libcurl >= 7.83, OpenSSL >= 3

Read it straight from the Hub:

from huggingface_hub import snapshot_download
from taco.ml import Dataset, plot_sample

path = snapshot_download("isp-uv-es/spacenet5-taco", repo_type="dataset", ignore_patterns=["spacenet5_test", "spacenet5_test/*"])
ds = Dataset(path)                  # the repository is the container: parts + .tacocat
plot_sample(ds[0])

or from a local copy:

ds = Dataset("spacenet5")
sample = ds[0]                      # {slot name: SlotValue}, arrays decoded
sample["pan"].array.shape

Metadata without decoding anything:

import taco
taco.read("spacenet5/.tacocat")              # one Arrow table, levels joined

Samples

What a sample contains

role	slot	holds	modality	detail
input	pan	raster	optical	1 band(s), digital_number
input	ms	raster	multispectral	8 band(s), digital_number
target	roads	mask	label_raster	2 classes
target	centrelines	polygon		unit degree

Licence

CC-BY-SA-4.0

Terms of use:

• Adapted material must be shared under CC-BY-SA-4.0.

Required credits:

• The SpaceNet Dataset by SpaceNet Partners is licensed under CC-BY-SA-4.0
• Imagery: Maxar WorldView-3

Providers: SpaceNet LLC / CosmiQ Works, Maxar (WorldView-3)

Acknowledgements

TACO was designed by César Aybar and is specified at https://asterisk.coop/taco/spec/.

Built by Oscar Pellicer within the ELLIOT project at the Image and Signal Processing Group (ISP), Universitat de València.