首页|Using machine learning and satellite data from multiple sources to analyze mining,water management,and preservation of cultural heritage

Using machine learning and satellite data from multiple sources to analyze mining,water management,and preservation of cultural heritage

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Remote sensing,particularly satellite-based,can play a valuable role in monitoring areas prone to geohazards.The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential hazards.This paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data,with a particular emphasis on:(1)detecting mining sub-sidence,where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar(InSAR)technology.The results showed that the Efficient Channel Attention(ECA)U-Net model performed better than the U-Net(baseline)model in terms of Mean Intersection over Union(MIoU)and Intersection over Union(loU)indicators;(2)monitor-ing water conservancy and hydropower engineering.The Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets(SBAS)InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north side.The dam body also showed obvious deformation with a velocity exceeding 60 mm/a;(3)the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural preservation.The overall outcome of these methods showed that the use of Artificial Intelligence(AI)techniques in combination with InSAR data leads to more efficient analysis and interpretation,resulting in improved accuracy and prompt identification of potential hazards;and(4)finally,this study also presents a method for detecting landslides in mountainous regions,using optical imagery.The new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods,this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time such as decades.

Landslide detectionInSAR time series analysismachine learningheritage preservationmining subsidencewater conservancy infrastructures

Joaquim J.Sousa、Jiahui Lin、Qun Wang、Guang Liu、Jinghui Fan、Shibiao Bai、Hongli Zhao、Hongyu Pan、Wenjing Wei、Vanessa Rittlinger、Peter Mayrhofer、Ruth Sonnenschein、Stefan Steger、Luís Paulo Reis

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School of Science and Technology,University of Trás-Os-Montes E Alto Douro,Vila Real,Portugal

Centre for Robotics in Industry and Intelligent Systems(CRIIS),INESC Technology and Science,Porto,Portugal

Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing,China

International Research Center of Big Data for Sustainable Development Goals,Beijing,China

College of Resources and Environment,University of Chinese Academy of Sciences,Beijing,China

Application system Department Ⅰ,China Siwei Surveying and Mapping Technology Co.Ltd,Beijing,China

China Aero Geophysical Survey and Remote Sensing Center for Natural Resources,Beijing,China

College of Marine Science and Engineering,Nanjing Normal University,Nanjing,China

Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu,China

Institute for Earth Observation,Eurac Research,Bolzano,Italy

Faculty of Engineering,University of Porto,Porto,Portugal

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2024

10.1080/10095020.2023.2234008

地球空间信息科学学报(英文版)
武汉大学（原武汉测绘科技大学）

地球空间信息科学学报(英文版)

影响因子：0.207
ISSN：1009-5020
年,卷(期)：2024.27(3)