## 1 Introduction  
The Qinghai-Tibet Plateau is the birthplace of the Yangtze and Yellow Rivers and is known as the water tower of Asia. Protecting the ecological environment of the Qinghai-Tibet Plateau region (Zhao, Chen, and Fan 2020) is related to the water security of many Asian countries [1]. In recent years, grassland degradation (Jiyuan, Xinliang, and Quanqin 2008) and soil erosion have become serious in Qinghai Province [2], and the grassland diversity ([PERSON] et al., 2013) is rapidly decreasing [3]. In the new era, in order to implement the concepts of green water, green mountains, golden mountains, and the system of mountain, water, forest, lake, lake, grass, and sand management, it is especially important to scientifically and effectively manage the problem of grassland degradation in the Sanjiangyuan area. In grassland ecosystems, rodents occupy an extremely important position. A moderate number of rats and rabbits play a positive role in the biological chain, but an excess of rats and rabbits can cause damage to grasslands and thus lead to rodent infestations. The proliferation of grassland rodents is often accompanied by many hazards, such as the degradation of grasslands, with reduced livestock carrying capacity; the sanding of grasslands, with severe soil erosion; and the spread of diseases, which threatens the health of farmers and herders ([PERSON] et al., 2021) [4]. The highland sage-grouse is widely distributed in the Qilian Mountains, and excessive feeding and digging activities threaten the grassland ecosystem in the region ([PERSON] et al., 2022) [5]. In addition to the large amount of forage and grazing animals competing for food, the damaging effects of the grassland are mainly due to the damage to the soil structure on which the grass plants grow and the reduction in the available area for grazing ([PERSON] et al., 2003) [6]. Therefore, there is a need for a more scientific program to determine the level of damage caused by plateau sage-grouse and to take timely measures for precise management. Deep learning plays an essential role in the vast field of ecological resource monitoring. For instance, in classification tasks, [PERSON] et al. utilized deep learning methods to classify images of 38 plant diseases, some of which may be related to rodent infestations, thereby establishing a potential connection for detecting rodent-related damage in plants ([PERSON], [PERSON], and [PERSON], 2016) [7]. In another study, [PERSON] et al. (2018) applied deep learning to automatically classify and count images captured by camera traps [8], effectively enabling animal population monitoring, which could be adapted for rodent monitoring in the future [9]. In terms of detection and localization, [PERSON] et al. (2017) focused on detecting and localizing parts like wheat ears and leaf tips, showcasing deep learning's potential in precise plant feature recognition [10]. This approach has applications in identifying rodent-damaged regions within vegetation, such as recognizing plant features impacted by rodent activity. Additionally, [PERSON] et al. (2019) used RetinaNet ([PERSON] et al., 2017) to achieve canopy detection, attaining significant improvements in speed and accuracy, which may prove helpful in the large-scale monitoring of rodent disruptions [11; 12]. Furthermore, deep learning has made notable advances in animal monitoring ([PERSON], [PERSON], and [PERSON], 2018), cloud detection ([PERSON] et al., 2016), and plant disease detection [PERSON], [PERSON], and [PERSON], 2016), supporting the efficient processing and accurate identification of ecological data [7; 13; 14]. In semantic segmentation, [PERSON] et al. (2019) achieved segmentation of soil and plant roots in images, providing an effective approach for analyzing plant-soil interactions [15].