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

Remote sensing (Basel, Switzerland), 2019-04, Vol.11 (7), p.886 [Tạp chí có phản biện]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11070886

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Nhan đề:
Multi-Source Data Fusion Based on Ensemble Learning for Rapid Building Damage Mapping during the 2018 Sulawesi Earthquake and Tsunami in Palu, Indonesia
Tác giả: Adriano, Bruno ; Xia, Junshi ; Baier, Gerald ; Yokoya, Naoto ; Koshimura, Shunichi
Chủ đề: 2018 Sulawesi earthquake-tsunami ; Algorithms ; Annotations ; Artificial intelligence ; Building failures ; Buildings ; Casualties ; Classification ; Classifiers ; Damage detection ; Damage patterns ; Data integration ; Datasets ; Digital Elevation Models ; Disasters ; Earthquake damage ; Earthquakes ; ensemble learning classifier ; Learning algorithms ; Machine learning ; Mapping ; Multisensor fusion ; Post-production processing ; rapid building damage mapping ; Remote sensing ; Seismic activity ; Sensors ; Synthetic aperture radar ; Tsunamis
Là 1 phần của: Remote sensing (Basel, Switzerland), 2019-04, Vol.11 (7), p.886
Mô tả: This work presents a detailed analysis of building damage recognition, employing multi-source data fusion and ensemble learning algorithms for rapid damage mapping tasks. A damage classification framework is introduced and tested to categorize the building damage following the recent 2018 Sulawesi earthquake and tsunami. Three robust ensemble learning classifiers were investigated for recognizing building damage from Synthetic Aperture Radar (SAR) and optical remote sensing datasets and their derived features. The contribution of each feature dataset was also explored, considering different combinations of sensors as well as their temporal information. SAR scenes acquired by the ALOS-2 PALSAR-2 and Sentinel-1 sensors were used. The optical Sentinel-2 and PlanetScope sensors were also included in this study. A non-local filter in the preprocessing phase was used to enhance the SAR features. Our results demonstrated that the canonical correlation forests classifier performs better in comparison to the other classifiers. In the data fusion analysis, Digital Elevation Model (DEM)- and SAR-derived features contributed the most in the overall damage classification. Our proposed mapping framework successfully classifies four levels of building damage (with overall accuracy >90%, average accuracy >67%). The proposed framework learned the damage patterns from a limited available human-interpreted building damage annotation and expands this information to map a larger affected area. This process including pre- and post-processing phases were completed in about 3 h after acquiring all raw datasets.
Nơi xuất bản: Basel: MDPI AG
Ngôn ngữ: English
Số nhận dạng: ISSN: 2072-4292
EISSN: 2072-4292
DOI: 10.3390/rs11070886
Nguồn: Directory of Open Access Journals
ROAD: Directory of Open Access Scholarly Resources
ProQuest Central

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

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs11070886

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