## 1 Introduction
A multitude of scholars both domestically and internationally have utilized remote sensing data, including Landsat, MODIS, Sentinel, domestic high-resolution remote sensing data, and environmental satellite data, for the purpose of categorizing land use information [10; 11; 12; 13; 14]. With the continuous development of machine learning and the arrival of the big data era, deep learning has been gradually applied to many fields such as computer vision [15; 16], communication signal processing [17; 18], and natural language processing [19; 20] and has achieved many important research results. Various research has shown [21; 22] that convolutional neural networks are capable of producing accurate outcomes for the categorization and extraction of remote sensing imageries. [PERSON] et al. [23] introduced a feature extractor that utilizes convolutional neural networks to learn distinctive representations from pairs of pixels. They also employed a voting method to refine the classification results. In contrast, [PERSON] et al. [24] employed a 3D convolutional neural network to directly extract profound spectral-spatial features from unprocessed remote sensing images, resulting in favorable classification outcomes. The predominant method of coal mining in China is underground mining. Coal mining activities result in the formation of a significant region of subsidence on the surface [25]. In their 2010 study, [PERSON] et al. [26] examined the classification models for coal mining subsidence in Huaibei City, reviewed the current issues with subsidence treatment, and offered a solution. In their 2014 study, [PERSON] et al. [27] proposed four distinct approaches for reclaiming and managing coal mining subsidence deep waterlogging areas. These approaches include comprehensive cultivation, plain reservoir construction, wetland ecological park establishment, and leisure and sightseeing tourism development. The authors developed these modes based on the specific distribution patterns of subsidence waterlogging in China and a thorough analysis of the associated damage characteristics. [PERSON] et al. [28] suggested developing an all-encompassing fishery management model that combines aquaculture and ecological tourism in the waters of the subsidence area in southern Shandong Province. [PERSON] et al. [29] proposed a fishery restoration management model that takes into account the existing conditions of the coal mining subsidence area in Jining City. This paper utilizes Sentinel-1 radar images as a data source and applies D-InSAR technology to process the images in order to extract the surface collapse range and collapse depth of the Zhaogu Mining Area. Additionally, it compensates for the lack of continuous-time optical remote sensing images of the study area by using multi-source remote sensing images from Sentinel-2 and GF-1. Furthermore, it constructs SSRN (Spectral-Spatial Residual Network Modeling) with improved classification accuracy. A deep learning model was developed to categorize the land use data in the collapse zone of the research area. Building upon this, a fuzzy comprehensive evaluation model was created using the improved G1 method to accurately assess the extent of land degradation in the collapse zone. Therefore, a governance model specifically designed for the subsidence zone in the study area is suggested. This model aims to offer technical assistance and data resources for the management of the subsidence zone in the Zhaogu Mining Area.