added DOI

README.md

@@ -66,13 +66,21 @@ since they contain the [external links](https://docs.h5py.org/en/stable/high/gro
 
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 ```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}, 
+@inproceedings{10.1145/3748636.3762732,
+  author = {Rege Cambrin, Daniele and Poeta, Eleonora and Pastor, Eliana and Corley, Isaac and Cerquitelli, Tania and Baralis, Elena and Garza, Paolo},
+  title = {HydroChronos: Forecasting Decades of Surface Water Change},
+  year = {2025},
+  isbn = {9798400720864},
+  publisher = {Association for Computing Machinery},
+  address = {New York, NY, USA},
+  url = {https://doi.org/10.1145/3748636.3762732},
+  doi = {10.1145/3748636.3762732},
+  abstract = {Forecasting surface water dynamics is crucial for water resource management and climate change adaptation. However, the field lacks comprehensive datasets and standardized benchmarks. In this paper, we introduce HydroChronos, a large-scale, multi-modal spatiotemporal dataset for surface water dynamics forecasting designed to address this gap. We couple the dataset with three forecasting tasks. The dataset includes over three decades of aligned Landsat 5 and Sentinel-2 imagery, climate data, and Digital Elevation Models for diverse lakes and rivers across Europe, North America, and South America. We also propose AquaClimaTempo UNet, a novel spatiotemporal architecture with a dedicated climate data branch, as a strong benchmark baseline. Our model significantly outperforms a Persistence baseline for forecasting future water dynamics by +14\% and +11\% F1 across change detection and direction of change classification tasks, and by +0.1 MAE on the magnitude of change regression. Finally, we conduct an Explainable AI analysis to identify the key climate variables and input channels that influence surface water change, providing insights to inform and guide future modeling efforts.},
+  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},
+  pages = {265–276},
+  numpages = {12},
+  keywords = {spatiotemporal forecasting, surface water dynamics, remote sensing, explainable AI, multi-modal data fusion},
+  location = {The Graduate Hotel Minneapolis, Minneapolis, MN, USA},
+  series = {SIGSPATIAL '25}
 }
 ```