文献类型：期刊文献

英文题名：A Novel Weighted Ensemble Transferred U-Net Based Model (WETUM) for Postearthquake Building Damage Assessment From UAV Data: A Comparison of Deep Learning- and Machine Learning-Based Approaches

作者：Khankeshizadeh, Ehsan[1,2];Mohammadzadeh, Ali[1,2];Arefi, Hossein[3,4];Mohsenifar, Amin[4];Pirasteh, Saied[1,5];Fan, En[1];Li, Huxiong[1];Li, Jonathan[6]

机构：[1]Shaoxing Univ, Inst Artificial Intelligence, Shaoxing 312000, Zhejiang, Peoples R China;[2]K N Toosi Univ Technol, Geomat Engn Fac, Dept Photogrammetry & Remote Sensing, Tehran 1543319967, Iran;[3]Univ Tehran, Sch Surveying & Geospatial Engn, Tehran 1417466191, Iran;[4]Hsch Mainz Univ Appl Sci, D-55128 Mainz, Germany;[5]Saveetha Sch Engn, Dept Geotech & Geomat, Saveetha Inst Med & Tech Sci, Chennai 602105, Tamil Nadu, India;[6]Univ Waterloo, Dept Geog & Environm Management, Waterloo, ON N2L 3G1, Canada

年份：2024

卷号：62

起止页码：1

外文期刊名：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

收录：SCI-EXPANDED(收录号：WOS:001174295800019)、、EI(收录号：20240415431221)、Scopus(收录号：2-s2.0-85182934266)、WOS

基金：No Statement Available

语种：英文

外文关键词：Feature extraction; Buildings; Autonomous aerial vehicles; Earthquakes; Drones; Data models; Reliability; Building damage map (BDM); deep transfer learning (DTL); machine learning (ML); spectral and geometrical features (SGFs); U-Net; unmanned aerial vehicle (UAV) data

外文摘要：Nowadays, unmanned aerial vehicle (UAV) remote sensing (RS) data are key operational sources used to produce a reliable building damage map (BDM), which is of great importance in instant response and rescue operations after earthquakes. This study proposes a novel weighted ensemble transferred U-Net-based model (WETUM) consisting of two major steps to create a reliable binary BDM using UAV data. In the first step of the proposed approach, three individual initial BDMs are predicted by three pretrained U-Net-based composite networks. In the second step, these three individual predictions are linearly integrated through a proposed grid search technique so that an optimized hybrid BDM (OHBDM) incorporating complementary damage information is made. The proposed WETUM was then compared with several conventional deep learning (DL) and machine learning (ML) models. The models were compared across two pivotal scenarios, addressing the impact of diverse feature sets on model performance and generalizability. Specifically, the first scenario focused solely on spectral features (SFs), while the second incorporated both spectral and geometrical features (SGFs). To make the comparisons, this study conducted empirical analyses using UAV spectral and geometrical data acquired over Sarpol-e Zahab, Iran. The experimental findings showed that the synergic use of spectral and geometrical data boosted both DL- and ML-based approaches in damage detection. Moreover, the proposed WETUM with damage detection rate (DDR) values of 65.22% and 78.26%, respectively, for the first and second scenarios, outperformed all the compared methods. Notably, WETUM with only spectral data outperformed the random forest (RF) classifier equipped with many hand-crafted SGFs, indicating the highest potential and generalizability of the proposed WETUM for building damage evaluation in a new unseen earthquake-affected area.