Landslide monitoring: DenseNET and image segmentation techniques to classify type and compute run-out displacement and hazard area

Shashwat Gunjan (), Prathyusha Dokku () and Vrushali Kamalakar ()
Additional contact information
Shashwat Gunjan: Southern Illinois University
Prathyusha Dokku: Southern Illinois University
Vrushali Kamalakar: Matrusri Engineering College

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 14, No 8, 16345-16370

Abstract: Abstract Exploring technologies and advancements in AI is done for reaching the heights that are difficult for humans to do manually. In landslide disaster management, current approaches often emphasize real-time forecasting using sensor-based environmental data,yet such systems can be hindered by limited availability, resolution, or timeliness during actual events. To overcome these limitations, our study introduces a post-event, image-driven framework that delivers rapid and reliable analysis of landslide dynamics using satellite or drone imagery. The proposed system performs an automated reconnaissance-style assessment, achieving 99.5% accuracy in identifying landslide characteristics and 92% accuracy in occurrence-related inference. It further estimates run-out displacement (RMSE: 0.222km) and impacted area (RMSE: 0.408km2), providing essential spatial metrics for disaster response and laying the groundwork for future integration of velocity and momentum estimation. By significantly reducing interpretation time, this approach enhances situational awareness and sets the foundation for future integration of momentum and velocity estimations. With real-time deployment, such systems could play a transformative role in early warning and rapid hazard mitigation efforts.

Keywords: Landslide assessment; Remotesensing; DenseNET; Image segmentation; Run-out distance; Hazard area; Rule-based inference; Historical data; Hazard monitoring (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-025-07431-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:121:y:2025:i:14:d:10.1007_s11069-025-07431-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-025-07431-9

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().