4.7 Article

Urban Building Classification (UBC) V2-A Benchmark for Global Building Detection and Fine-Grained Classification From Satellite Imagery

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2023.3311093

关键词

Building detection; fine-grained classification; instance segmentation; multimodal; optical imagery; synthetic aperture radar (SAR)

向作者/读者索取更多资源

This study proposes a benchmark for building detection and fine-grained classification from high-resolution satellite imagery. The dataset includes extensive annotations of building instances, roof types, and building functions from cities worldwide. Additionally, finely aligned synthetic aperture radar images are provided for the development and evaluation of multimodal image approaches.
Datasets play a key role in developing superior building detection approaches. However, most of the previous work focuses on accurate building masks and scale expansion, while the categories are always missing, which hinders the further analysis of urban development and cultures. Therefore, we propose a benchmark for building detection and fine-grained classification from very high-resolution (VHR) satellite imagery. An extensive annotation is performed for about 0.5 million building instances with 12 fine-grained roof types and individual polygons. The annotation of building functions of two cities in the previous version (UBCv1) (Huang et al., 2022) is also integrated. To ensure the building variety, it consists of VHR optical images of 20 unique cities worldwide with various landforms and styles of architecture. Its variety and fine-grained categories pose great challenges and meanwhile provide a foundation for the building extraction and fine-grained classification on a global scale. Besides, 17 cities are provided with finely aligned synthetic aperture radar (SAR) images, which can be used for the development and evaluation of approaches optionally based on optical, SAR, or multimodal images. Significantly, the proposed benchmark is used as the base of the 2023 IEEE GRSS Data Fusion Contest (Persello et al., 2023). The dataset and codes of the baseline methods are available at: https://github.com/AICyberTeam/UBC-dataset/tree/UBCv2.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据