Abstract
The rising machine learning (ML) models have become the preferred way for landslide detection based on remote sensing images, but the performance of these models in a sample-free area are rarely concerned in many studies. In this study, we used a cross-validation method (training model in one area and validation in another) to compare the model portability of trained ML models applied in an “off-site” area, as a consideration of the landslide detection ability of these models in sample-free areas. We integrate nighttime light imagery, multi-seasonal optical Landsat time-series and digital elevation data, and we employed support vector machines (SVM), artificial neural networks (ANN) and random forest (RF) models to classify the satellite imagery and identify landslides. Samples of two scenarios generated from two subareas of the Jiuzhaigou disaster-stricken region are used for the cross-application and accuracy evaluation of three ML models. The results revealed that when the trained models are applied in areas outside those in which they were developed, the landslide identification accuracy of these three models has declined. Especially for the SVM and ANN models, the accuracy is greatly reduced and there appears a seriously imbalanced user’s and producer’s accuracy. However, although the performance of the RF model is lower than that of SVM and ANN models in their local area, the RF model exhibits stable portability, and retains the original performance and achieves a satisfactory balance between overestimation and underestimation in “off-site” areas. An additional validation from a new area proved that the landslide detection performance of the RF model with stable portability is higher than that of the SVM and ANN models in “off-site” areas. The results suggest that evaluating the model portability through cross-application can be a useful way to determine the most suitable model for landslide detection in “off-site” areas with a similar geographic environment to model development areas, so as to maximize the accuracy of landslide detection based on limited samples.
Highlights
Landslides represent a serious hazard in many areas of the world, and result in enormous casualties and severe economic loss every year [1,2]
The high performance of the random forest (RF) model was retained from the development area to the application area, demonstrating its validity for the development areas and its high portability to the application areas
In view of the above analysis, with the study of the portability of three models to subarea B, there is no obvious difference in the prediction performance of the artificial neural networks (ANN) and RF models, so both ANN and RF models can be recommended for landslide detection in area C because of their better accuracy and good portability; in the study of the portability of the three models to subarea A, the ANN model tends to overestimate and its F1 score and AUC value are lower than the that of the RF model
Summary
Landslides represent a serious hazard in many areas of the world, and result in enormous casualties and severe economic loss every year [1,2]. The information contained within and among these units (e.g., spectral, textural, etc.) can be subjected to a variety of classification algorithms [33], especially the emerging machine learning (ML) methods [15,34], which are considered effective approaches for remote sensing applications with emphasis on image classification and object recognition [35], such as SVM [36,37], ANN [38,39], RF [21,23,40], logistic regression [39,41,42], Bayesian theory [43], Dempster-Shafer theory [14] and neuro-fuzzy classifier [27] All of these approaches above aim to increase the quality of the landslide detection and limit classification errors which are typical of the landslide maps obtained from the classification of satellite images [1], and the above literature review shows that these models have achieved convincing results in different areas. If the hypothesis proposed is proven, this cross-application strategy can provide a feasible idea for model portability evaluation, and recommend the optimal ML model for landslide detection in “off-site” areas with a similar geographic environment as model development areas under the condition of limited time, limited resources and high quality
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