Revisiting Change Detection Methods for their Application to Serac Fall Time-Lapse Monitoring

2026-05-27

AI Summary

This study addresses the challenges of automated visual monitoring of slope instability events such as serac collapse, which are hindered by extreme deformations, drastic illumination changes, and limitations in traditional sensor deployment. The work introduces, for the first time, a dedicated subtask of volume change detection tailored to serac collapse, presents a new dataset named SeracFallDet, and provides a systematic evaluation of various change detection methods. Experimental results demonstrate that unsupervised feature matching approaches—both dense and semi-dense—exhibit strong generalization capabilities despite lacking task-specific training, whereas supervised methods are constrained by data scarcity and label imbalance. These findings underscore the potential of unsupervised techniques for real-world environmental monitoring and highlight hybrid approaches as a promising direction for future research.

Abstract

In an era where climate change aggravates environmental uncertainties, the identification and detection of event precursors are becoming crucial to mitigate the impacts of disastrous natural hazards. While classical sensors such as interferometric lasers or seismometers are reliable, their widespread deployment is often hindered by logistical and economic barriers, leaving numerous blind spots. Time-lapse cameras, which already provide cost-effective, high-resolution visual context to such sensors, present a promising alternative. However, processing their output automatically faces significant challenges, notably linked to extreme shape and lighting variations. Overcoming those issues is essential to deploy them at large-scale as a monitoring tool. This paper introduces a novel sub-task of change detection, namely volumetric change detection, applied to time-lapse cameras and slope instabilities. We conduct a comprehensive review of state-of-the-art change detection methods and related tasks, analyze their core components and assess their applicability to this context. To that end, we introduce the new dataset SeracFallDet, which contains serac fall annotations and has been thoroughly annotated to meet the latter demand. Through generalization experiments, we demonstrate that dense and semi-dense feature matching, although not trained specifically for this task, exhibit robust performance. Alternatively, supervised approaches struggle with data scarcity and annotation imbalance. This suggests that hybrid methods may offer a path forward by leveraging the strengths of both tasks. These findings highlight the potential of feature matching techniques and the need for further innovation to overcome the challenges of real-world deployment in environmental monitoring.

Problem

• change detection
• time-lapse monitoring
• serac fall
• volumetric change
• slope instability

Innovation

• volumetric change detection
• time-lapse monitoring
• feature matching
• SeracFallDet dataset
• slope instability