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An inventory of landslides along Alabama highways was created using both inclinometer readings collected by the Alabama Department of Transportation and based on Detailed Damage Inspection Reports (DDIRs) submitted to the Federal Highway Administration (FHWA) for emergency assistance. For the inclinometer-based dataset, we have processed the readings to extract the change in displacement from the previous reading at the top of the slide plane. For the emergency relief landslides, we have extracted key information from the submitted DDIRs. These datasets can be used for landslide susceptibility modeling, testing remote sensing-based monitoring or warning systems, or to better understand landslide patterns in the southeastern United States.
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SlideSim: 3D Landslide Displacement Monitoring through a Physics-Based Simulation Approach to Self-Supervised Learning
Displacement monitoring is a critical step to understand, manage, and mitigate potential landside hazard and risk. Remote sensing technology is increasingly used in landslide monitoring. While significant advances in data collection and processing have occurred, much of the analysis of remotely-sensed data applied to landslides is still relatively simplistic, particularly for landslides that are slow moving and have not yet “failed”. To this end, this work presents a novel approach, SlideSim, which trains an optical flow predictor for the purpose of mapping 3D landslide displacement using sequential DEM rasters. SlideSim is capable of automated, self-supervised learning by building a synthetic dataset of displacement landslide DEM rasters and accompanying label data in the form of u/v pixel offset flow grids. The effectiveness, applicability, and reliability of SlideSim for landslide displacement monitoring is demonstrated with real-world data collected at a landslide on the Southern Oregon Coast, U.S.A. Results are compared with a detailed ground truth dataset with an End Point Error RMSE = 0.026 m. The sensitivity of SlideSim to the input DEM cell size, representation (hillshade, slope map, etc.), and data sources (e.g., TLS vs. UAS SfM) are rigorously evaluated. SlideSim is also compared to diverse methodologies from the literature to highlight the gap that SlideSim fills amongst current state-of-the-art approaches.
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- Award ID(s):
- 2050047
- PAR ID:
- 10376479
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 14
- Issue:
- 11
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 2644
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs14112644
