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Semi-supervised identification and mapping of surface water extent using street-level monitoring videos

Big Earth data 2023年第4期7卷 986-1004页

作者： Ruo-Qian Wang Yangmin Ding Department of Civil and Environmental Engineering RutgersThe State University of New JerseyPiscatawayNJUSA NEC Laboratories America Inc 4 Independence WayPrincetonNJUSA

Urban flooding is becoming a common and devastating hazard,which causes life loss and economic *** and understanding urban flooding in a highly localized scale is a challenging task due to the complicated urban landscape,intri-cate hydraulic process,and the lack of high-quality and resolution *** emerging smart city technology such as monitoring cameras provides an unprecedented opportunity to address the data ***,estimating water ponding extents on land surfaces based on monitoring footage is unreliable using the tradi-tional segmentation technique because the boundary of the water ponding,under the influence of varying weather,background,and ilumination,is usually too fuzzy to identify,and the oblique angle and image distortion in the video monitoring data prevents geor-eferencing and object-based *** paper presents a novel semi-supervised segmentation scheme for surface water extent recognition from the footage of an oblique monitoring *** semi-supervised segmentation algorithm was found suitable to determine the water boundary and the monoplotting method was successfully applied to georeference the pixels of the monitoring video for the virtual quantification of the local drainage *** correlation and mechanism-based analysis demon-strate the value of the proposed method in advancing the under-standing of local drainage *** workflow and created methods in this study have a great potential to study other street-level and earth surface processes.

关键词： Segmentation deep learning monoplotting smart city monocular visual data

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