Scene-level buildings damage recognition based on Cross Conv-Transformer-Reference-Cited by-同舟云学术

Scene-level buildings damage recognition based on Cross Conv-Transformer

Published:2023-09-28 Issue:2 Volume:16 Page:3987-4007
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Shi Lingfei¹,Zhang Feng¹²,Xia Junshi³,Xie Jibo⁴

Affiliation:

1. School of Earth Sciences, Zhejiang University, Hangzhou, People’s Republic of China

2. Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou, People’s Republic of China

3. Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan

4. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of China

Funder

National Key Research and Development Program of China

KAKENHI

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Computer Science Applications,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2023.2261770

Reference48 articles.

1. Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia

2. Change Detection Based Building Damage Assessment Method Using Radar Imageries with GLCM Textural Parameters

3. Vision Transformers for Remote Sensing Image Classification

4. Optimal segmentation of high spatial resolution images for the classification of buildings using random forests

5. Chen C. Q. Fan N. Mallinar T. Sercu and R. Feris. 2019. “Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition.” 7th International Conference on Learning Representations ICLR 1–20.

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