Ganerød, A.J.; Lindsay, E.; Fredin, O.; Myrvoll, T.-A.; Nordal, S.; Rød, J.K. Globally vs. Locally Trained Machine Learning Models for Landslide Detection: A Case Study of a Glacial Landscape. Remote Sens.2023, 15, 895.
Ganerød, A.J.; Lindsay, E.; Fredin, O.; Myrvoll, T.-A.; Nordal, S.; Rød, J.K. Globally vs. Locally Trained Machine Learning Models for Landslide Detection: A Case Study of a Glacial Landscape. Remote Sens. 2023, 15, 895.
Ganerød, A.J.; Lindsay, E.; Fredin, O.; Myrvoll, T.-A.; Nordal, S.; Rød, J.K. Globally vs. Locally Trained Machine Learning Models for Landslide Detection: A Case Study of a Glacial Landscape. Remote Sens.2023, 15, 895.
Ganerød, A.J.; Lindsay, E.; Fredin, O.; Myrvoll, T.-A.; Nordal, S.; Rød, J.K. Globally vs. Locally Trained Machine Learning Models for Landslide Detection: A Case Study of a Glacial Landscape. Remote Sens. 2023, 15, 895.
Abstract
Landslide risk mitigation is limited by data scarcity. This could be improved using continuous landslide detection systems. In order to investigate which image types and machine learning (ML) models are most useful for landslide detection in a Norwegian setting, we compared the performance of five different ML models, for the Jølster case study (30-July-2019), in Western Norway. These included three globally pre-trained models; i) the Continuous Change Detection and Classification (CCDC) algorithm, ii) a combined k-means clustering and Random Forest classification model, and iii) a convolutional neural network (CNN), and two locally-trained models, including; iv) Classification and Regression Trees and v) a U-net CNN model. Images used included Sentinel-1, Sentinel-2, digital elevation model (DEM) and slope. The globally-trained models performed poorly in shadowed areas, and were all outperformed by the locally-trained models. A maximum Matthew’s correlation coefficient (MCC) score of 89% was achieved with model v, using combined Sentinel-1 and -2 images as input. This is one of the first attempts to apply deep-learning to detect landslides with both Sentinel-1 and -2 images. Using Sentinel-1 images only, the locally-trained deep-learning model significantly outperformed the conventional ML model. These findings contribute towards developing a national continuous monitoring system for landslides.
Keywords
NDVI; SAR; change detection; Norway; Sentinel-1; Sentinel-2; deep learning; U-Net; CCDC; Google Earth Engine
Subject
Environmental and Earth Sciences, Geophysics and Geology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.