Update data.md with MiniFrance

Author: blesaux

_pages/data.md

@@ -56,8 +56,33 @@ You are free to use and/or refer to the HYPERVIEW dataset in your own research (
 
 ## \[2020\] MiniFrance Dataset
 
+![image-left]({{ site.url }}{{ site.baseurl }}/images/MiniFrance-Visual.png){: .align-left}{: width="30%"}
+
 With Javiera Castillo-Navarro et al., we released the first benchmark for semi-supervised learning in Earth observation: [MiniFrance](https://ieee-dataport.org/open-access/minifrance).
 
+In an effort led by [Javiera Castillo-Navarro](https://javi-castillo.github.io/), with [Alexandre Boulch](https://boulch.eu/), [Nicolas Audebert](https://nicolas.audebert.at/), [Sébastien Lefèvre](https://people.irisa.fr/Sebastien.Lefevre/) and myself, we introduced the first large-scale dataset and benchmark for semi-supervised semantic segmentation in Earth Observation, the [MiniFrance suite](https://ieee-dataport.org/open-access/minifrance). MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images, accounting for more than 200 billions samples (pixels); it is varied, covering 16 conurbations in France, with various climates, different landscapes, and urban as well as countryside scenes; and it is challenging, considering land use classes with high-level semantics. 
+
+Nevertheless, the most distinctive quality of MiniFrance is being the first open-access dataset in the field especially designed for semi-supervised learning: it contains labeled and unlabeled images in its training partition, which reproduces a life-like scenario. Hence the success, with nearly 200 000 downloads!
+
+\[ [open-access dataset](https://ieee-dataport.org/open-access/minifrance) / [paper in *Machine Learning* journal](https://link.springer.com/article/10.1007/s10994-020-05943-y) / [open-access arxiv](https://arxiv.org/abs/2010.07830) /  [EO Database entry](https://eod-grss-ieee.com/dataset-detail/ZnNHaldIK2JXQi9xN0ZGTng2b2tpZz09) / [Further used in the Data Fusion Contest 2022 of the IEEE GRSS!](https://www.grss-ieee.org/community/technical-committees/2022-ieee-grss-data-fusion-contest/) \]
+
+{::options parse_block_html="true" /}
+<details>
+Copyright: The images in this dataset are released under IGN's "licence ouverte". More information can be found at http://www.ign.fr/institut/activites/lign-lopen-data The maps used to generate the labels in this dataset come from the Copernicus program, and as such are subject to the terms described here: https://land.copernicus.eu/local/urban-atlas/urban-atlas-2012?tab=metadata Label maps are released under [Creative-Commons BY-NC-SA](https://creativecommons.org/licenses/by/4.0/). If using this dataset, please cite: **Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study** _J. Castillo-Navarro, B. Le Saux, A. Boulch, N. Audebert and Sébastien Lefèvre_, Machine Learning journal, 2021
+
+```
+@article{castillo2020minifrance,
+title={{Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study}},
+author={Castillo-Navarro, Javiera and Audebert, Nicolas and Boulch, Alexandre and {Le Saux}, Bertrand and Lef{\`e}vre, S{\'e}bastien},
+journal={Machine Learning},
+vol={111},
+year={2021}
+} 
+```
+</details>
+
+{::options parse_block_html="false" /}
+
 ## \[2020\] SEN12-FLOOD Dataset
 
 ![image-left]({{ site.url }}{{ site.baseurl }}/images/sen12floods-icon.png){: .align-left}