🤗 Add DatasetCard

README.md

@@ -0,0 +1,68 @@
+---
+language: en
+license: unknown
+task_categories:
+- change-detection
+pretty_name: ChaBuD MSI
+tags:
+- remote-sensing
+- earth-observation
+- geospatial
+- satellite-imagery
+- change-detection
+- sentinel-2
+---
+
+# ChaBuD MSI
+
+<!-- Dataset thumbnail -->
+![ChaBuD MSI](./thumbnail.png)
+
+<!-- Provide a quick summary of the dataset. -->
+ChaBuD is a dataset for Change detection for Burned area Delineation and is used for the ChaBuD ECML-PKDD 2023 Discovery Challenge. This is the MSI version with 13 bands.
+- **Paper:** https://doi.org/10.1016/j.rse.2021.112603
+- **Homepage:** https://huggingface.co/spaces/competitions/ChaBuD-ECML-PKDD2023
+
+## Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+
+- **Total Number of Images**: 356
+- **Bands**: 13 (MSI)
+- **Image Size**: 512x512
+- **Image Resolution**: 10m
+- **Land Cover Classes**: 2
+- **Classes**: no change, burned area
+- **Source**: Sentinel-2
+
+
+## Usage
+
+To use this dataset, simply use `datasets.load_dataset("blanchon/ChaBuD_MSI")`.
+<!-- Provide any additional information on how to use this dataset. -->
+```python
+from datasets import load_dataset
+ChaBuD_MSI = load_dataset("blanchon/ChaBuD_MSI")
+```
+
+## Citation 
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+If you use the ChaBuD_MSI  dataset in your research, please consider citing the following publication:
+
+
+```bibtex
+@article{TURKOGLU2021112603,
+    title = {Crop mapping from image time series: Deep learning with multi-scale label hierarchies},
+    journal = {Remote Sensing of Environment},
+    volume = {264},
+    pages = {112603},
+    year = {2021},
+    issn = {0034-4257},
+    doi = {https://doi.org/10.1016/j.rse.2021.112603},
+    url = {https://www.sciencedirect.com/science/article/pii/S0034425721003230},
+    author = {Mehmet Ozgur Turkoglu and Stefano D'Aronco and Gregor Perich and Frank Liebisch and Constantin Streit and Konrad Schindler and Jan Dirk Wegner},
+    keywords = {Deep learning, Recurrent neural network (RNN), Convolutional RNN, Hierarchical classification, Multi-stage, Crop classification, Multi-temporal, Time series},
+}
+```