1. Introduction
The southern region of China experiences a high frequency of rainfall-induced landslides, particularly under extreme precipitation conditions. This can lead to the development of a distinct form of shallow, mass landslide hazards within specific basins. These hazards exhibit unique characteristics, including small individual scales, prolonged durations, multiple hazard locations and distinct watershed subordination features. Consequently, they generate a cumulative and amplifying impact throughout the hazard event, resulting in substantial damage [
1,
2,
3]. In addition, the complex geological conditions in the area are influenced by a variety of natural factors, resulting in a large number of hazards from mass landslides, including casualties, property damage, infrastructure damage, economic loss, ecological damage and many other aspects [
4,
5,
6]. Such landslides induced by extreme rainfall are not uncommon around the world, such as in the Andean region [
7], Hong Kong [
8], America [
9] and Malaysia [
10], where rainfall-induced landslide disasters have caused severe human and economic losses.
Traditional methods of landslide identification are time-consuming, labor-intensive and relatively difficult to identify for large-scale areas [
11]. However, remote sensing technology is a very effective means of obtaining topographic and geomorphological information to identify potential landslides and landslide histories, which in turn can lead to landslide hazard assessment and landslide risk analysis [
12]. Although predicting rainfall can effectively prevent geological disasters from occurring in advance, and some scholars are now conducting research in this field [
13], the identification of regional landslides after extreme heavy rainfall weather events not only helps to reveal the causes and mechanisms of landslides and provide a scientific basis for landslide prevention and control but also helps to predict and allows for warning of landslides, thus reducing disaster losses. Therefore, the accurate identification and monitoring of regional landslides is of great academic and practical significance and can make a positive contribution to safeguarding people’s lives and property, maintaining social stability and promoting economic development.
Currently, four methods are widely used in traditional large-scale landslide identification studies of remote sensing imagery: visual interpretation, image-based methods, object-oriented methods and deep learning. The visual interpretation method is more accurate, but it relies on expert knowledge and experience, which is costly in terms of time and manpower, and has a relatively small scope of application and cannot be interpreted quickly [
14,
15,
16,
17]. The image element–based method has the advantages of fast recognition and high accuracy, but it requires analysis under the same resolution of the geographical environment, which is prone to information waste and the “salt and pepper” phenomenon [
18,
19]. The object-oriented method can classify objects with similar spectral texture features into one category, which effectively improves recognition accuracy and avoids the “salt and pepper” phenomenon, but many small noises will be generated in the process of image object segmentation, and these units need to be ignored during classification, which presents certain limitations [
20,
21,
22,
23].
With the rapid development of computer image recognition technology, the cross-fertilization between the field of deep learning and the field of landslide detection has become one of the current research hotspots. Many scholars have carried out research work in this area, and some of them have proposed new algorithmic models using convolutional neural networks to develop the principle, such as U-Net [
24], DeepLab V3+ [
25], Faster R-CNN [
26], Mask R-CNN [
27], Yolov [
28] and so on. Other scholars have improved the algorithmic models based on their predecessors by optimizing the model structure or combining them with other algorithms to improve the model feature extraction capability, thus increasing the recognition efficiency [
29,
30,
31,
32,
33]. Some other scholars believe that deep learning network models are still inadequate in extracting features at multiple scales and need to be improved by adding constraints to their design [
34,
35,
36,
37,
38]. Deep learning methods have the advantages of being adaptable and having good recognition effects, which can better solve the problems in traditional methods and thus present a new direction for landslide recognition.
Rainfall-induced landslides in South China are small in monolithic scale and not well recognizable from other features such as bare land, roads and mining areas. Located in the Beijiang basin area of South China, the area is heavily covered by vegetation and is often affected by cloudy and rainy weather, which leads to the inability of visible light and microwaves to penetrate effectively through the surface vegetation; the areas with a high degree of vegetation cover in this region are also usually steeply sloped and densely vegetated, which makes it obviously difficult to carry out field investigations [
39]. Therefore, accurate remote sensing interpretation of the landslide hazard remains a significant challenge. The Mask R-CNN algorithm model can not only accurately detect targets in images but also generate pixel-level segmentation masks for each detected target. It is also able to discriminate object boundaries in complex environments, improving the accuracy of detection segmentation. In addition, the Mask R-CNN algorithm model is able to perform detection and segmentation directly on the input image without additional pre-processing steps, providing a high recognition rate and high accuracy [
40].
Since He et al. [
27] proposed Mask R-CNN, the algorithm has been widely used in various fields. For example, Sui et al. [
41] improved the Mask R-CNN model by introducing the CBAM attention module to achieve façade damage detection of complex building faces after an earthquake. In addition, Liu et al. [
42] used Mask R-CNN as a base model and introduced an attention mechanism to establish an automatic landslide identification model for InSAR observation and achieved better results. In addition, Jiang et al. [
43] used the Sichuan–Tibet transportation corridor as the study area and combined Mask R-CNN and migration learning methods to successfully detect old and new landslides, indicating that the migration-learning-improved Mask R-CNN can be effectively used for landslide and ice avalanche detection. Yang et al. [
44] proposed a background enhancement method by adding landslide triggering factors to the data as auxiliary information and compared the applicability of Mask R-CNN, U-Net and PSP-Net methods. These research results not only provide new ideas and methods for landslide hazard monitoring, early warning and prevention but also promote the development and application of deep learning techniques.
This paper aims to perform remote sensing interpretation through combining the masked R-CNN algorithm model with the constraints of spectral, structural, morphological and physical features of landslides in remote sensing imagery. The method makes use of the stacking of data layers to effectively extract features from the data, which improves the precision and accuracy of landslide identification and demonstrates a good ability to identify small, shallow landslides in southern China. First, a landslide recognition sample dataset is established based on known landslide information, and then various deep learning models based on sample libraries and constraints, including Mask R-CNN, Faster R-CNN, U-Net and YOLOv3, are trained and tested to compare the recognition accuracy of each algorithmic model. This is followed by a comparison of the effect of different rotation angles and different combinations of constraints on the recognition of the masked R-CNN model. Finally, the Mask R-CNN model is used to automatically extract landslides from all the image data and to exclude erroneously identified blocks of bare ground based on the physical characteristics of landslides. The results of this study can be used in the investigation of shallow rainfall-type landslide disasters in mountainous areas to provide technical methods, data support and decision guidance.
7. Conclusions
In this study, a Mask R-CNN model constructed on the basis of geographic factors affecting landslide generation and landslide remote sensing identification features is proposed to address the lack of training dataset and misdiagnosis of confusing features (e.g., bare ground, river floodplains, buildings, roads, etc.). The method was validated using data from June 2022 rainfall-induced landslides in the Beijiang River Basin. In addition, Faster-CNN, U-Net and YOLOv3 models were used to compare the applicability of different depth models with the incoming Mask R-CNN model, and the results showed that the performance of the Mask R-CNN model was better compared to the other models, with a precision rate of 81.91%, a recall rate of 84.07% and an accuracy of 87.28%. By comparing the results of model training with different rotation angles, it is found that the model has the strongest generalization ability and robustness when rotated by 90°, which can effectively improve the efficiency and accuracy of model recognition. By adding constraints, the recognition accuracy and precision can be improved. From the comparison experiments of different constraints, it can be found that superimposing landslide-evoking factor data and texture feature sample data improves the recognition accuracy of the model. This finding can bring improvement to different deep learning models, as it involves modifications at the data level. However, there are still errors in the extraction results, and how to find effective features and make reasonable combinations of them instead of simply adding various triggering factors to train the model will become a future research trend. The exceptionally heavy rainfall in June 2022 was a major factor in triggering this landslide. This study can help to understand the distribution pattern of rainfall-induced, shallow, massive landslides in the Beijiang River Basin and provide data and technology for the prevention of rainfall-induced geological hazards in the hilly areas of southeastern China.