isp-uv-es/dvl-instruct-regional-taco

DVL-Instruct (regional change caption)

This is a repackaging, not a new dataset. It is DVL-Instruct (regional change caption) by DynamicVL authors (Xuan et al.), USDA NAIP (imagery, US public domain), 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: apache-2.0

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

Citation

Please cite the original work:

@inproceedings{xuan2025dynamicvl,
  title     = {DynamicVL: Benchmarking Multimodal Large Language Models for Dynamic City Understanding},
  author    = {Xuan, Weihao and Wang, Junjue and Qi, Heli and Chen, Zihang and Zheng, Zhuo and Zhong, Yanfei and Xia, Junshi and Yokoya, Naoto},
  booktitle = {Advances in Neural Information Processing Systems 38 (NeurIPS 2025) Datasets and Benchmarks Track},
  year      = {2025},
  eprint    = {2505.21076},
  archivePrefix = {arXiv}
}

About the data

1156 regional change-caption samples from DVL-Suite: a 1-10 frame series of one region, with a caption that narrates it year pair by year pair.

1,156 samples · splits: test 377 · train 701 · validation 78 · tasks: change-captioning

Packaged as TACO v3.

Full description

Frames. The release's own region crops under regional_caption/images/, re-rendered as 1024x1024 PNG. They are not the pixels of DVL-Instruct's image sets, so they are packaged separately. Every frame here is three-band: unlike the set imagery, the crops carry no near-infrared at all.

Caption. Split into one entry per narrated span, in the release's own order. caption_full keeps the text verbatim.

Encoding. Re-encoded as COGs, losslessly. No CRS and no transform; the stated 1.0 m GSD is carried on the bands.

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/dvl-instruct-regional-taco", repo_type="dataset")
ds = Dataset(path)                  # the repository is the container: parts + .tacocat
plot_sample(ds[0])

or from a local copy:

ds = Dataset("dvl-instruct-regional")
sample = ds[0]                      # {slot name: SlotValue}, arrays decoded
sample["rgb"].array.shape

Metadata without decoding anything:

import taco
taco.read("dvl-instruct-regional/.tacocat")              # one Arrow table, levels joined

Samples

What a sample contains

role	slot	holds	modality	detail
input	rgb	raster_series	optical	3 band(s), unit 1, requantised
target	caption_spans	text_sequence	text

Licence

apache-2.0

Providers: DynamicVL authors (Xuan et al.), USDA NAIP (imagery, US public domain)

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.