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B Iaild dataset by elliotvincent
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Dataset

B Iaild

by elliotvincent hf-dataset--elliotvincent--b-iaild
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38.0 Top 100%
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Dataset Information Summary
Entity Passport
Registry ID hf-dataset--elliotvincent--b-iaild
License etalab-2.0
Provider huggingface
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Cite this dataset

Academic & Research Attribution

BibTeX 
@misc{hf_dataset__elliotvincent__b_iaild,
  author = {elliotvincent},
  title = {B Iaild Dataset},
  year = {2026},
  howpublished = {\url{https://huggingface.co/datasets/elliotvincent/b-iaild}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
elliotvincent. (2026). B Iaild [Dataset]. Free2AITools. https://huggingface.co/datasets/elliotvincent/b-iaild

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Quality (Q) 0

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FNI V2.0 for B Iaild: Semantic (S:0), Authority (A:0), Popularity (P:0), Recency (R:0), Quality (Q:0).

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Dataset Specification

b-IAILD: bi-temporal extension of IAILD

b-IAILD

Dataset Description

b-IAILD is a temporal extension of the IAILD (Inria Aerial Image Labeling Dataset) dataset [1] focused on building change detection in urban areas. The dataset provides bi-temporal orthoimage pairs with binary building footprint annotations, covering locations in the USA and Austria.

Project page: https://xavibou.github.io/CDviaWTS/

Dataset Summary

  • Task: Building change detection via weak temporal supervision
  • Coverage: Urban areas in USA (Austin, Chicago, Kitsap) and Austria (Tyrol, Vienna)
  • Resolution: 0.6 m/px (standardized)
  • Patch Size: 256×256 pixels
  • Total Training Pairs: 15,500 (3,100 per city)
  • Total Validation Pairs: 2,500 (500 per city)

Dataset Structure

The dataset consists of bi-temporal image pairs with the following temporal coverage:

Location Original IAILD (t1) New Acquisition (t2) Time Gap
Austin, TX Before 2017 2022 >5 years
Chicago, IL Before 2017 2023 >6 years
Kitsap, WA Before 2017 2023 >6 years
Tyrol, Austria Before 2017 2023 >6 years
Vienna, Austria Before 2017 2024 >7 years

Dataset Creation

Source Data

The dataset extends the original IAILD training set by adding new orthoimage acquisitions over the same geographic locations:

Preprocessing

All images were standardized to a common spatial resolution:

  1. Original resolutions (new acquisitions):

    • Vienna: 0.15 m/px
    • Tyrol: 0.20 m/px
    • Austin, Chicago, Kitsap: 0.60 m/px
    • Original IAILD: 0.30 m/px

  2. Standardized resolution: 0.60 m/px (all images resampled)

  3. Patching: 2500×2500 images split into 256×256 patches with 6-pixel overlap

Annotations

Single-temporal binary building footprint masks are provided for each pair, enabling change detection via weak temporal supervision.

References

[1] E. Maggiori et al. (2017). Can semantic labeling methods generalize to any city? The Inria Aerial Image Labeling Benchmark. In IGARSS

Citation

If you use this dataset, please cite the following publication:

bibtex 
@article{bou2026remote,
  title={Remote Sensing Change Detection via Weak Temporal Supervision},
  author={Bou, Xavier and Vincent, Elliot and Facciolo, Gabriele and Grompone von Gioi, Rafael and Morel, Jean-Michel and Ehret, Thibaud},
  journal={arXiv preprint arXiv:2601.02126},
  year={2026}
}

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🆔 Identity & Source

id
hf-dataset--elliotvincent--b-iaild
slug
elliotvincent--b-iaild
source
huggingface
author
elliotvincent
license
etalab-2.0
tags
task_categories:image-segmentation, language:en, license:etalab-2.0, size_categories:10k

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