Abstract: Mine site data is a crucial foundation for the construction of smart mines and intelligent management. The rapid identification and extraction of complex mine sites from multi-source data, including remote sensing images, is an important research direction. This paper uses Sentinel-2 images from 2020, GF-6 images, and GF-2 images to select the optimal dataset. Google image data from 2023 is used to expand the dataset, which is combined with deep learning algorithms to establish two types of open-pit coal mine site recognition models. The main conclusions of the study are: ① A mine recognition model was established using 10 m Sentinel-2 images, 8 m GF-6 raw images, 2 m GF-6 fusion images, 3.2 m GF-2 raw images, and 0.8 m GF-2 fusion images. The accuracy of the model produced by different data was quantitatively selected. The results show that as the spatial resolution of remote sensing images increases from 10 meters to 0.8 meters, the accuracy of the mine site recognition model established by the same method gradually improves. Among them, the mine site recognition model established using GF-2 fusion images with a spatial resolution of 0.8m has the highest accuracy, with an average precision (P_A) and mean intersection over union (MIOU) of 0.702 and 0.824 respectively. ② A total of 3162 multi-scene, multi-time period, and multi-scale mine site samples were collected from multi-source remote sensing images. All samples were uniformly fused to establish a Mine Site Scene Recognition Model (MSSRM) and a Mine Site Boundary Recognition Model (MSBRM). The P_A of MSSRM reached 0.758 and the average intersection over union of MSBRM reached 0.864. ③ The accuracy of coal mine site recognition models established by three object recognition methods: Faster R-CNN (faster region-based convolutional neural network), YOLO-v5 (You Only Look Once-v5), DETR (Detection Transformer), and three image segmentation methods: Mask R-CNN, U-Net, DeepLabV3+ were compared. Among them, compared with Faster R-CNN and YOLO-v5, the P_A of the recognition model established by DETR increased by 7.6% and 8.3%, respectively. Compared with Mask R-CNN and U-Net, the MIOU of the segmentation model established by DeepLabV3+ increased by 14% and 10.8%, respectively. ④ A method for automatically, intelligently, and batch recognizing mine site scenes from large-scale remote sensing images and drawing mine site boundaries was established. Taking the application of open-pit coal mine site recognition in typical arid and semi-arid mining areas (Ordos) as an example, the performance of the intelligent recognition method for mining scene boundaries was verified, and the model mapping accuracy reached 0.817.