Developing a Framework for Rapid Collapsed Building Mapping Using Satellite Imagery and Deep Learning Models

Author:

Adriano Bruno1,Miura Hiroyuki2,Liu Wen3,Matsuoka Masashi4,Koshimura Shunichi1

Affiliation:

1. Tohoku University,International Research Institute of Disaster Science,Japan

2. Hiroshima University,School of Advanced Science and Engineering,Japan

3. Chiba University,Graduate School of Engineering,Japan

4. Tokyo Institute of Technology,Department of Architecture and Building Engineering,Japan

Publisher

IEEE

Reference10 articles.

1. Development of Earthquake-induced Building Damage Estimation Model Based on ALOS/PALSAR Observing the 2007 Peru Earthquake

2. Openearthmap: A benchmark dataset for global high-resolution land cover mapping;xia;Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV),2022

3. The M7.8 and M7.5 Kahramanmara? Earthquake Sequence struck near Nurda??, Turkey (Türkiye) on February 6, 2023,2023

4. U-net: Convolutional networks for biomedical image segmentation;ronneberger;Int Conf Med Image Comput Comput Assist Interv,2015

5. Learning from multimodal and multitemporal earth observation data for building damage mapping

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1. Deep Learning for Cross-Domain Building Change Detection from Multi-Source Very High-Resolution Satellite Imagery;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

2. Integrating Remote Sensing for Earthquake Risk Assessment in Istanbul's Kartal District;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

3. Quick Extraction Of Collapsed Buildings Due To The 2023 Turkey Earthquake From X-Band SAR Data;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

4. A shape-aware enhancement Vision Transformer for building extraction from remote sensing imagery;International Journal of Remote Sensing;2024-02-02

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