--- license: openrail datasets: - DarthReca/hydro-chronos tags: - climate - geospatial - remote-sensing - spatiotemporal - multi-modal - earth-observation - time-series - hydrology library_name: transformers --- # ACTU for Change Detection This is ACTU for change detection of thresholded absolute MNDWI difference. ## Model Details This architecture is a temporal UNet (with ConvLSTMs), featuring an LSTM branch to process climate timeseries and a gating mechanism. It is designed to receive a timeseries of Sentinel-2 images, DEM, and timeseries of climate variables and output a binary mask. - **Developed by:** Daniele Rege Cambrin - **Model type:** ACTU - **License:** OpenRAIL - **Repository:** [Github](https://github.com/DarthReca/hydro-chronos) - **Paper:** [Arxiv](https://arxiv.org/abs/2506.14362) ## How to Get Started with the Model The model is integrated into Transformers, so you can easily load it with the following code: ```python AutoModel.from_pretrained("DarthReca/actu-change-detection", trust_remote_code=True, revision=) ``` Load the model with the desired configuration with the *revision* parameter (the branches of this repo). These configurations are available: | Revision | Backbone | DEM | Climate | |-------------|-----------------|:---:|:-------:| | main | ConvNeXtV2 Base | No | No | | dem-climate | ConvNeXtV2 Base | Yes | Yes | ## Training Details The model is pre-trained on Landsat-5 images and fine-tuned on Sentinel-2 of HydroChronos. ## Citation ```bibtex @misc{cambrin2025hydrochronosforecastingdecadessurface, title={HydroChronos: Forecasting Decades of Surface Water Change}, author={Daniele Rege Cambrin and Eleonora Poeta and Eliana Pastor and Isaac Corley and Tania Cerquitelli and Elena Baralis and Paolo Garza}, year={2025}, eprint={2506.14362}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2506.14362}, } ``` ## Licensing The project uses third-party software. For detailed information on the licensing of each component, please see the [**NOTICE.md**](NOTICE.md) file.