Update README.md

README.md

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-license: cc-by-nc-sa-4.0
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+---
+license: cc-by-nc-sa-4.0
+task_categories:
+- image-segmentation
+---
+
+# The Dataset
+
+The LandCover.ai (**Land Cover** from **A**erial **I**magery) dataset is a dataset for automatic mapping of buildings, woodlands, water and roads from aerial images.
+
+### Dataset features
+- land cover from Poland, Central Europe (1)
+- three spectral bands - RGB
+- 33 orthophotos with 25 cm per pixel resolution (~9000x9500 px)
+- 8 orthophotos with 50 cm per pixel resolution (~4200x4700 px)
+- total area of 216.27 km2
+
+### Dataset format
+- rasters are three-channel GeoTiffs with EPSG:2180 spatial reference system
+- masks are single-channel GeoTiffs with EPSG:2180 spatial reference system
+
+(1): Image source: Head Office of Geodesy and Cartography, Poland
+
+# Reproduce and compare
+
+We provide split.py to split images into 512x512 pieces, and following files: train.txt, val.txt and test.txt containing lists of pieces used for training, validation and testing respectively.
+
+# Versions
+
+## Version 1
+
+- classes: building (1), woodland (2), water(3), road(4)
+- areas: 1.85 km2 of buildings, 72.02 km2 of woodlands, 13.15 km2 of water, 3.5 km2 of roads
+
+[Paper](https://arxiv.org/abs/2005.02264v4)
+
+# Citation
+
+To cite our work, please use the following:
+```
+@InProceedings{Boguszewski_2021_CVPR,
+    author = {Boguszewski, Adrian and Batorski, Dominik and Ziemba-Jankowska, Natalia and Dziedzic, Tomasz and Zambrzycka, Anna},
+    title = {LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
+    month = {June},
+    year = {2021},
+    pages = {1102-1110}
+}
+```
+
+# License
+
+This dataset is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
+
+# Contact
+
+If you encounter any problem or have any feedback, please contact [email protected]