Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami

• 資料種別: 記事
• タイトル: Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami
• 著者標目: Yanbing Bai; Erick Mas; Shunichi Koshimura
• 出版年月日等: 2018-10-12
• 出版年（W3CDTF）: 2018-10-12
• タイトル（掲載誌）: Remote Sensing
• 巻号年月日等（掲載誌）: 10 10
• 掲載巻: 10
• 掲載号: 10
• 掲載ページ: 1626-
• 掲載年月日（W3CDTF）: 2018-10-12
• 出版事項（掲載誌）: MDPI AG
• 件名標目: 2011 Tohoku earthquake and tsunami; Science; Q; U-net convolutional neural network; semantic segmentation; Microsoft Cognitive Toolkit; operational damage-mapping; semantic segmentation; U-net convolutional neural network; operational damage-mapping; 2011 Tohoku earthquake and tsunami; Microsoft Cognitive Toolkit
• 対象利用者: 一般
• DOI: 10.3390/rs10101626; https://doi.org/10.3390/rs10101626
• 作成日（W3CDTF）: 2018-10-12
• オンライン閲覧公開範囲: インターネット公開
• 著作権情報: https://creativecommons.org/licenses/by/4.0/
• 関連情報（URI）: https://www.mdpi.com/2072-4292/10/10/1626/pdf
• 参照: Deep neural networks based automated extraction of dugong feeding trails from UAV images in the intertidal seagrass beds; https://cir.nii.ac.jp/crid/1050575661501806336; Challenges and implications of predicting the spatiotemporal distribution of dengue fever outbreak in Chinese Taiwan using remote sensing data and deep learning; https://cir.nii.ac.jp/crid/1360017279850095104; Pyramid Pooling Module-Based Semi-Siamese Network: A Benchmark Model for Assessing Building Damage from xBD Satellite Imagery Datasets; https://cir.nii.ac.jp/crid/1360290617397362048; Cross-Domain-Classification of Tsunami Damage Via Data Simulation and Residual-Network-Derived Features From Multi-Source Images; https://cir.nii.ac.jp/crid/1360849945192914304; Tsunami Damage Detection with Remote Sensing: A Review; https://cir.nii.ac.jp/crid/1360853567350749056; Knowledge distillation based lightweight building damage assessment using satellite imagery of natural disasters; https://cir.nii.ac.jp/crid/1360861704789048320
• 参照: Detection of collapsed buildings from lidar data due to the 2016 Kumamoto earthquake in Japan; https://cir.nii.ac.jp/crid/1050005667203087872; Satellite Imagery Analysis for Operational Damage Assessment in Emergency Situations; https://cir.nii.ac.jp/crid/1360019301199055232; Nationwide Post Event Survey and Analysis of the 2011 Tohoku Earthquake Tsunami; https://cir.nii.ac.jp/crid/1360283693563780224; Field survey report and satellite image interpretation of the 2013 Super Typhoon Haiyan in the Philippines; https://cir.nii.ac.jp/crid/1360292620452708224; Detection of damage to building side-walls in the 2011 Tohoku, Japan earthquake using high-resolution TerraSAR-X images; https://cir.nii.ac.jp/crid/1360292621498473984; A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks; https://cir.nii.ac.jp/crid/1360567185279333760; Building Damage Assessment in the 2015 Gorkha, Nepal, Earthquake Using Only Post‐Event Dual Polarization Synthetic Aperture Radar Imagery; https://cir.nii.ac.jp/crid/1360567186824833408; Damage Characteristic and Field Survey of the 2011 Great East Japan Tsunami in Miyagi Prefecture; https://cir.nii.ac.jp/crid/1360846643517201664; Mapping of Building Damage of the 2011 Tohoku Earthquake Tsunami in Miyagi Prefecture; https://cir.nii.ac.jp/crid/1360848661102652160; SATELLITE IMAGE CLASSIFICATION OF BUILDING DAMAGES USING AIRBORNE AND SATELLITE IMAGE SAMPLES IN A DEEP LEARNING APPROACH; https://cir.nii.ac.jp/crid/1360855569328678016; Object-oriented mapping of landslides using Random Forests; https://cir.nii.ac.jp/crid/1361137045731789312; Damage assessment after 2001 Gujarat earthquake using Landsat-7 satellite images; https://cir.nii.ac.jp/crid/1361418518640791424; Road Extraction by Deep Residual U-Net; https://cir.nii.ac.jp/crid/1361418519232769792; Investigation of Tsunami‐Induced Damage and Fragility of Buildings in Thailand after the December 2004 Indian Ocean Tsunami; https://cir.nii.ac.jp/crid/1361418519360360960; U-Net: Convolutional Networks for Biomedical Image Segmentation; https://cir.nii.ac.jp/crid/1361418520447167744; Urban Damage Level Mapping Based on Scattering Mechanism Investigation Using Fully Polarimetric SAR Data for the 3.11 East Japan Earthquake; https://cir.nii.ac.jp/crid/1361699995143362304; Automated Poststorm Damage Classification of Low-Rise Building Roofing Systems Using High-Resolution Aerial Imagery; https://cir.nii.ac.jp/crid/1361981469209272960; Earthquake damage mapping: An overall assessment of ground surveys and VHR image change detection after L'Aquila 2009 earthquake; https://cir.nii.ac.jp/crid/1362262943488999296; A Medical Disaster Response to Reduce Immediate Mortality after an Earthquake; https://cir.nii.ac.jp/crid/1362262943830610688; Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark; https://cir.nii.ac.jp/crid/1362262945693566464; CNTK; https://cir.nii.ac.jp/crid/1362825893358712576; Fully convolutional networks for semantic segmentation; https://cir.nii.ac.jp/crid/1363107371054744576; Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning; https://cir.nii.ac.jp/crid/1363388844098325376; Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning; https://cir.nii.ac.jp/crid/1363670319236124544; Using high-resolution satellite imagery to provide a relief priority map after earthquake; https://cir.nii.ac.jp/crid/1364233270819310464; Earthquake damage identification using multi-temporal high-resolution optical satellite imagery; https://cir.nii.ac.jp/crid/1370019294186405124; GIS and image understanding for near-real-time earthquake damage assessment; https://cir.nii.ac.jp/crid/1370848664445963907
• 連携機関・データベース: 国立情報学研究所 : CiNii Research; https://cir.nii.ac.jp/
• 提供元機関・データベース: Crossref; https://www.crossref.org; 科学研究費助成事業データベース; https://kaken.nii.ac.jp/ja

• 要約等: <jats:p>The satellite remote-sensing-based damage-mapping technique has played an indispensable role in rapid disaster response practice, whereas the ...