Deep object segmentation and classification networks for building damage detection using the xBD dataset-Reference-Cited by-同舟云学术

Deep object segmentation and classification networks for building damage detection using the xBD dataset

Published:2024-01-08 Issue:1 Volume:17 Page:
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Zhao Zongze¹,Wang Fenglei¹,Chen Shiyu²³,Wang Hongtao¹,Cheng Gang¹

Affiliation:

1. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, People’s Republic of China

2. School of Geographic Sciences, Xinyang Normal University, Xinyang, People’s Republic of China

3. Henan Engineering Research Center for Big Data of Remote Sensing and Intelligent Analysis in Huaihe River Basin, Xinyang Normal University, Xinyang, People's Republic of China

Funder

Natural Science Foundation of Henan Province

Henan Polytechnic University Basic Research Expenses special project

Basic Scientific Research Fund Project of Henan Polytechnic University

Doctoral Fund of Henan Polytechnic University under Grant

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Computer Science Applications,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2024.2302577

Reference61 articles.

1. Deep learning-based semantic segmentation of urban-scale 3D meshes in remote sensing: A survey

2. Terrestrial laser scanning intensity data applied to damage detection for historical buildings

3. Building-damage detection using post-seismic high-resolution SAR satellite data

4. Bromley, J., I. Guyon, Y. Lecun, Eduard Sckinger, and R. Shah. 1993. “Signature Verification Using a Siamese Time Delay Neural Network.” Paper presented at the Advances in Neural Information Processing Systems 6, [7th NIPS Conference, Denver, Colorado, USA, 1993].

5. Building damage assessment in decimeter resolution SAR imagery: A future perspective