Although the exploitation of mineral areas brings wealth to society, it inevitably leads to the degradation of the surrounding natural environment. To understand and assess the influences of mining activities on the geological and ecological environment, land cover classification in open-pit mine areas (LCCMA) is of great significance. This research proposes an intelligent classification framework for LCCMA based on an object-oriented method and multitask learning (MTL), named the MTL Classification Framework (MTLCF). With the help of MTL, each land cover type in open-pit mine areas obtains its exclusive and receivable object-oriented feature sets using the model-agnostic method. After that, the feature sets are fused with the original images. EfficientNet, a spatial pyramid pooling module, and a global attention upsample module are assembled as the segmentation models with the structure of the encoder and decoder to classify intelligently each land cover type in open-pit mine areas. Finally, the models were trained, and ablation experiments were performed. The experimental results show that our proposed framework -MTLCF was effective for classification in LCCMA, and the overall accuracy and the mean of F1 score for the MTLCF in LCCMA were 85.6% and 86.06%, respectively.

中文翻译：

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基于面向对象和多任务学习的露天矿区土地覆被类型智能分类

矿区开采虽然为社会带来财富，但也不可避免地导致周边自然环境的退化。为了了解和评估采矿活动对地质和生态环境的影响，露天矿区土地覆盖分类（LCCMA）具有重要意义。本研究提出了一种基于面向对象方法和多任务学习 (MTL) 的 LCCMA 智能分类框架，称为 MTL 分类框架 (MTLCF)。在 MTL 的帮助下，露天矿区的每种土地覆被类型使用模型无关的方法获得其专有的和可接收的面向对象的特征集。之后，特征集与原始图像融合。EfficientNet，一个空间金字塔池化模块，以及一个全局注意力上采样模块，作为具有编码器和解码器结构的分割模型，对露天矿区的每种土地覆盖类型进行智能分类。最后，对模型进行训练，并进行消融实验。实验结果表明，我们提出的框架-MTLCF对LCCMA中的分类是有效的，在LCCMA中MTLCF的总体准确率和F1分数的平均值分别为85.6%和86.06%。