矿山场景数据是智慧矿山建设和智能管理的基础数据，如何利用包括遥感影像在内的多源数据快速识别和提取出复杂的矿山场景是重要的研究方向。采用2020年Sentinel-2 影像、GF-6 影像、GF-2 影像进行最优数据集筛选，使用2023年谷歌影像(Google image)数据扩充数据集，并与深度学习算法相结合，建立了2种露天煤矿场地识别模型。研究主要结论：① 利用10 m Sentinel-2影像、8 m GF-6原始影像、2 m GF-6融合影像、3.2 m GF-2原始影像、0.8 m GF-2融合影像建立矿山识别模型，量化选择不同数据产生的模型精度。结果显示，遥感图像空间分辨率从10 m增加到0.8 m，通过相同的方法建立的矿山场景识别模型的精度逐渐提高。其中使用0.8 m空间分辨率的GF-2融合影像建立的矿山场景识别模型的精度最高，平均精准度P_A和(MIOU，Mean Intersection over Union)分别达到了0.702和0.824。② 从多源遥感图像中采集了3 162个多场景、多时段、多尺度矿山场景样本对所有样本进行统一融合处理，建立了矿山场地场景识别模型(MSSRM, Mine Site Scene Recognition Model)和矿山场地边界识别模型(MSBRM, Mine Site Boundary Recognition Model)。MSSRM的P_A达到了0.758，MSBRM平均交并比达到0.864。③ 对比了Faster R-CNN(Faster Region-based Convolutional Neural Network)、YOLO-v5(You Only Look Once-v5)、DETR(Detection Transformer)3种目标识别方法与Mask R-CNN、U-Net、DeepLabV3+三种图像分割方法建立的煤矿场地识别模型精度，其中，DETR方法建立的识别模型与Faster R-CNN和YOLO-v5相比P_A分别提高了7.6%和8.3%。DeepLabV3+建立的分割模型与Mask R-CNN和U-Net相比MIOU分别提高了14%和10.8%。④ 建立了从大范围的遥感影像中自动化、智能化、批量化识别矿山场地场景并绘制矿山场地边界的方法，以干旱、半干旱典型矿区(鄂尔多斯)露天煤矿场地识别应用为例，验证了智能识别矿山场景边界方法的性能，模型制图精度达到了0.817。

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.