# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 1 Introduction  
An old landslide is the result of prolonged and intricate geological processes occurring on slopes [1]. Although the majority of old landslides exhibit long-term stability, they possess the potential for reactivation and renewed sliding. This reactivation is triggered by factors, notably seismic events, precipitation, and human-engineered activities, which exert their influence on pre-existing slide accumulations. The slide mass resulting from old landslides is a site for human activities, primarily influenced by the underlying topography and landforms [2]. In recent years, the escalating human engineering activities and ever-changing global climatic conditions have led to a sharp increase in the frequency of old landslide activations. This phenomenon has resulted in significant harm to both human life and property safety, as well as the natural environment [3, 4, 5]. Therefore, to safeguard the safety of human lives and property, it is imperative to undertake extensive detection and monitoring of old landslides on a large scale. The existing landslide detection methods mainly fall into the following three categories: (1) Visual interpretation method. This approach relies excessively on expert experience, demanding a substantial investment of time and effort, leading to relatively low efficiency [6, 7]. Nevertheless, its high accuracy in identifying old landslides compensates for these limitations; (2) Machine learning method. In contrast to the visual interpretationmethod, this approach exhibits a higher degree of automation [8; 9; 10; 11; 12]. However, it necessitates the extraction of a large number of image features and involves conducting extensive feature selection and hyperparameter tuning experiments based on the feature data, which incurs a substantial workload; (3) Deep learning method. Over the past few years, Convolutional Neural Networks have witnessed rapid advancements and achieved remarkable milestones in the domain of image processing [13; 14; 15; 16; 17; 18]. In contrast to traditional machine learning methods, deep learning obviates the need for manual feature engineering and selection when dealing with landslide characteristics. Moreover, deep learning is amenable to larger sample sizes and is well suited for landslide detection in more expansive scenes. The detection of nascent landslides has experienced rapid advancements in the field of deep learning [19]. However, the detection of old landslides still faces technical challenges that need to be solved in relevant research areas. The presence of old landslides is characterized by their long history and considerable time interval, leading to various degrees of transformation over time [20]. Subsequent to the occurrence of a landslide, vegetation often reestablishes itself over several years, blending with the surrounding environment. As a result, differentiating old landslides from recent ones primarily relies on scrutinizing the inherent morphological characteristics of the landslide itself and identifying certain traces of human-induced alteration [21]. [PERSON] et al. [20] designed an iterative classification and semantic segmentation network to classify and segment old landslides on the Loess Plateau, and the results show that the designed network is extremely effective for old landslides that are difficult to identify there. For the semantic segmentation task, the F1 score increased from 0.5054 to 0.5448 and the detection accuracy of the old landslide improved to 0.9 compared to the basic network. [PERSON] et al. [22; 23] used Mask R-CNN to automatically identify old landslides in the loess area. The results show that the two-stage algorithm has a better ability to detect old landslides in the Loess Plateau. [PERSON] et al. [24] used CNNs and DEM data to identify old landslides in the Loess Plateau, with a detection rate of 95.7% and a recall rate of 100%.