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Hi-UCD: A Large-scale Dataset for Urban Semantic Change Detection in Remote Sensing Imagery

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arxiv 2011.03247 v7 pith:XLZSDHMN submitted 2020-11-06 cs.CV eess.IV

Shiqi Tian , Ailong Ma , Zhuo Zheng , Yanfei Zhong This is my paper · ORCID
classification cs.CVeess.IV
keywords changeurbandatasethi-ucdbenchmarkdetectionimageslack
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With the acceleration of the urban expansion, urban change detection (UCD), as a significant and effective approach, can provide the change information with respect to geospatial objects for dynamical urban analysis. However, existing datasets suffer from three bottlenecks: (1) lack of high spatial resolution images; (2) lack of semantic annotation; (3) lack of long-range multi-temporal images. In this paper, we propose a large scale benchmark dataset, termed Hi-UCD. This dataset uses aerial images with a spatial resolution of 0.1 m provided by the Estonia Land Board, including three-time phases, and semantically annotated with nine classes of land cover to obtain the direction of ground objects change. It can be used for detecting and analyzing refined urban changes. We benchmark our dataset using some classic methods in binary and multi-class change detection. Experimental results show that Hi-UCD is challenging yet useful. We hope the Hi-UCD can become a strong benchmark accelerating future research.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 40 citations worldwide. Full citation record

  1. OPTIMUS: Observing Persistent Transformations in Multi-temporal Unlabeled Satellite-data

    cs.CV 2025-06 conditional novelty 7.0 of 10

    A self-supervised temporal ordering objective plus a pivot score detects persistent, non-seasonal changes in satellite time series with AUROC 0.876.

  2. Foundation Model-Driven Semantic Change Detection in Remote Sensing Imagery

    cs.CV 2026-02 unverdicted novelty 6.0 of 10

    PerASCD sets new state-of-the-art Sek scores on SECOND and LandsatSCD datasets by using a modular cascaded gated decoder on PerA foundation model features plus a new consistency loss.

  3. DynamicEarth: How Far are We from Open-Vocabulary Change Detection?

    cs.CV 2025-01 reject novelty 4.0 of 10

    The paper shows that composing mask proposal, feature comparison, and open-vocabulary classification models can detect arbitrary-category changes in satellite images without training.

  4. Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives

    cs.CV 2025-05 conditional novelty 3.0 of 10

    A structured review of remote sensing vision-language models, organizing contrastive, instruction-tuned, and generative approaches alongside their datasets and benchmarks.

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