├── LICENSE ├── README.md └── example.png /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 AICyberTeam 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # UBC-dataset 2 | Urban Building Classification dataset 3 | 4 | ## Update! 5 | We have finished our work on UBCv2! Please go to the [UBCv2](https://github.com/AICyberTeam/UBC-dataset/tree/UBCv2) branch for more details. 6 | - 7 | ## Introduction 8 | We present a dataset for building detection and classification from very high-resolution satellite imagery with the focus on object-level interpretation of individual buildings. 9 | It is meant to provide not only a flexible test platform for object detection algorithms but also a solid basis for the comparison of city morphologies and the investigation of urban planning. 10 | 11 | The details of this dataset can be seen in paper 'Urban Building Classification (UBC) – A Dataset for Individual Building Detection and Classification from Satellite Imagery' (Link will be added after the publication). 12 | 13 | Here is an example of the annotation. 14 | 15 | 16 | 17 | Input image (a), building footprints (b, green polygons), roof types (c) and functions (d, coarse classes) 18 | 19 | ## Download 20 | Currently, we only provide the standard [COCO](https://cocodataset.org/#home) instance segmentation format. 21 | 22 | Annotations of roof coarse, roof fine and use coarse are build. 23 | 24 | The train and valitation set of this dataset can be downloaded from: 25 | 26 | BaiduNetdisk: [https://pan.baidu.com/s/1M6yYD1lvbqsVpn5MHGa2tg?pwd=7hbm](https://pan.baidu.com/s/1M6yYD1lvbqsVpn5MHGa2tg?pwd=7hbm) password: 7hbm 27 | 28 | Google Grive: [https://drive.google.com/file/d/1XnKFKqjoa95PLXFw01HcXx4Az49Qw37i/view?usp=sharing](https://drive.google.com/file/d/1XnKFKqjoa95PLXFw01HcXx4Az49Qw37i/view?usp=sharing) 29 | ## References 30 | If you use our dataset, please cite our CVPR EarthVision 2022 paper: 31 | ``` 32 | @INPROCEEDINGS{9857458, 33 | author={Huang, Xingliang and Ren, Libo and Liu, Chenglong and Wang, Yixuan and Yu, Hongfeng and Schmitt, Michael and Hänsch, Ronny and Sun, Xian and Huang, Hai and Mayer, Helmut}, 34 | booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, 35 | title={Urban Building Classification (UBC) – A Dataset for Individual Building Detection and Classification from Satellite Imagery}, 36 | year={2022}, 37 | volume={}, 38 | number={}, 39 | pages={1412-1420}, 40 | doi={10.1109/CVPRW56347.2022.00147}} 41 | ``` 42 | -------------------------------------------------------------------------------- /example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AICyberTeam/UBC-dataset/aafd0db502c82eb13b39935e88b957946afe5a55/example.png --------------------------------------------------------------------------------