Towards Onboard Continuous Change Detection for Floods

This repository contains the official implementation of the HiT (History-Injection Transformer) mechanism, as described in the paper: "Towards Onboard Continuous Change Detection for Floods."

HiT maintains historical context from previous observations while reducing data storage by over 99% of original image size (compared to bi-temporal baseline). Testing on the STTORM-CD-Floods dataset confirms that the HiT mechanism within the PrithVi-tiny foundation model maintains detection accuracy compared to the bi-temporal baseline.

Model	F1	Precission	Recall	Parameters	Input Size
Baseline	0.41 ± 0.06	0.73 ± 0.05	0.29 ± 0.05	8.5M	2×
HiT-PrithVi	0.38 ± 0.08	0.70 ± 0.03	0.27 ± 0.08	7.8M	1.004×
ContUrbanCD	0.46 ± 0.26	0.82 ± 0.06	0.35 ± 0.25	25M	n×

Checkpoints for HiT-PrithVi are available on Hugging Face - link.

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📊 Datasets

• STTORM-CD-Floods
• RaVAEn-Floods

Installation

git clone https://github.com/zaitra/HiT-change-detection.git
cd Hit-change-detection
pip install .

📜 Citation

If you use this work, please cite the following paper:

@misc{kyselica2026towards,
      title={Towards Onboard Continuous Change Detection for Floods}, 
      author={Daniel Kyselica and Jonáš Herec and Oliver Kutis and Rado Pitoňák},
      year={2026},
      eprint={2601.13751},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2601.13751}, 
}

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Maintained by Zaitra.