Awesome

UBC-dataset

Urban Building Classification dataset

Update!

We have finished our work on UBCv2! Please go to the UBCv2 branch for more details.

Introduction

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. 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.

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).

Here is an example of the annotation.

Input image (a), building footprints (b, green polygons), roof types (c) and functions (d, coarse classes)

Download

Currently, we only provide the standard COCO instance segmentation format.

Annotations of roof coarse, roof fine and use coarse are build.

The train and valitation set of this dataset can be downloaded from:

BaiduNetdisk: https://pan.baidu.com/s/1M6yYD1lvbqsVpn5MHGa2tg?pwd=7hbm password: 7hbm

Google Grive: https://drive.google.com/file/d/1XnKFKqjoa95PLXFw01HcXx4Az49Qw37i/view?usp=sharing

References

If you use our dataset, please cite our CVPR EarthVision 2022 paper:

@INPROCEEDINGS{9857458,
  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},
  booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, 
  title={Urban Building Classification (UBC) – A Dataset for Individual Building Detection and Classification from Satellite Imagery}, 
  year={2022},
  volume={},
  number={},
  pages={1412-1420},
  doi={10.1109/CVPRW56347.2022.00147}}