Image Segmentation
Transformers
Safetensors
actu
feature-extraction
climate
geospatial
remote-sensing
spatiotemporal
multi-modal
earth-observation
time-series
hydrology
custom_code
Instructions to use DarthReca/actu-direction-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DarthReca/actu-direction-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="DarthReca/actu-direction-classification", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DarthReca/actu-direction-classification", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: openrail | |
| datasets: | |
| - DarthReca/hydro-chronos | |
| pipeline_tag: image-segmentation | |
| tags: | |
| - climate | |
| - geospatial | |
| - remote-sensing | |
| - spatiotemporal | |
| - multi-modal | |
| - earth-observation | |
| - time-series | |
| - hydrology | |
| library_name: transformers | |
| # ACTU for Magnitude Regression | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| This is ACTU for direction classification of MNDWI difference. | |
| ## Model Details | |
| <!-- Provide a longer summary of what this model is. --> | |
| 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 tri-class mask of future MNDWI direction of change. | |
| - **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-direction-classification", trust_remote_code=True, revision=<model_type>) | |
| ``` | |
| 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. | |