Identification of Potential Sinkhole Signatures: Employing Time Series Clustering for Anomaly Detection in InSAR Time Series Over Limburgs Mining District

Ted Manders, Joana E. Martins

Research output: Contribution to conferencePaperpeer-review

Abstract

This paper presents a machine learning-based approach for detecting ground deformation patterns from time series indicative of potential sinkholes in the mining district of Lim-burg, the Netherlands. Our research uses large datasets of un-labeled, high-dimensional InSAR time series from Sentinel-1 and RADARSAT-2 satellites to identify anomalies that signal ground subsidence risks. Our methodology involves an anomaly detection process using dimensionality reduction, clustering time series data and cluster labeling. One of the challenges we face in the Limburg mining district is that there are no annotated datasets and no target labels that are required for a supervised learning approach, therefore we label the clusters by expert judgement. By adopting unsupervised learning, we aim to uncover unknown patterns in the time series, grouping similar temporal features into clusters for further analysis.
Original languageEnglish
Pages7415-7419
Number of pages5
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 - Athens, Greece
Duration: 7 Jul 202412 Jul 2024

Conference

Conference2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
Country/TerritoryGreece
CityAthens
Period7/07/2412/07/24

Keywords

  • Sinkholes
  • InSAR
  • Clustering
  • Machine Learning
  • Autoencoder
  • Unsupervised Learning
  • Mining
  • Mining deformations
  • subsidence monitoring
  • Subsidence
  • Subsidence-induced damage
  • Limburg
  • coal mining
  • after-mining effects

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