## 1 Introduction  
The stability of mined-out areas (goafs) is one of the key issues in mine safety production and environmental protection. During the mining process, the stability of the mined-out area, affected by the destruction and deformation of the strata, directly impacts the mining efficiency, operational safety, and stability of the surrounding environment. Particularly for underground mined-out areas, their stability depends on the complex underground stress field, which is influenced by the roof and surrounding rock during the mining process. Therefore, analyzing and predicting the stress field in mined-out areas is crucial for ensuring long-term stability and preventing potential disasters.  
Although recent years have seen progress in the research on the stability of mined-out areas and stress fields, there are still many shortcomings. First, most studies focus on the overall stability analysis of mined-out areas, while predictive research on the stress field is relatively limited. Some studies even skip stress analysis and directly predict the stability of mined-out areas. For example, [PERSON] et al. used principal component analysis and differential evolution algorithms combined with a multi-class support vector machine to classify the risks of 120 measured mined-out areas [1]. While this method performs well in classification accuracy, it overlooks the prerequisite impact of the stress field on stability. Secondly, the existing stress prediction research mainly relies on methods such as theoretical modeling analysis and numerical simulation analysis, with few adopting data-driven machine-learning methods. Given the complex underground geological conditions and stress distribution in mined-out areas, especially when dealing with stress fields characterized by high nonlinearity and multivariable coupling, traditional methods, despite their strong theoretical foundation, may struggle to ensure prediction accuracy. Lastly, the current research often neglects the potential application of vast amounts of data accumulated in engineering practice, particularly image data. As an important source of engineering information, image data contain rich spatial information, which is crucial for improving the accuracy of stress field predictions. However, the use of 3D laser scanning images in existing studies remains in its early stages, and a systematic methodology has yet to be developed.  
In this context, this study attempts to introduce 3D laser scanning images of mined-out areas as a resource for stress prediction. By extracting image features using pre-trained computer vision models, various machine-learning models are then constructed for stress field prediction, and SHapley Additive exPlanations (SHAP) are used for model interpretation. This approach is a first attempt in the field of roof stress prediction in mined-out areas, aiming not only to improve prediction accuracy and provide solutions for mine safety production and environmental protection but also to explore the complex characteristics of stress distribution, promoting knowledge discovery in this field. Furthermore, it offers a new methodological guide for predicting the stability of mined-out areas. The second part of the paper reviews the methods of stress prediction, the third part explains the overall approach and methodology of this study, the fourth part presents the research findings, and the final part summarizes and discusses the results.