As the ecological problems caused by mine development become increasingly prominent, the conflict between mining activity and environmental protection is gradually intensifying. There is an urgent problem regarding how to effectively monitor mineral exploitation activities. In order to automatic identify and dynamically monitor open-pit mines of Hubei Province, an open-pit mine extraction model based on Improved Mask R-CNN (Region Convolutional Neural Network) and Transfer learning (IMRT) is proposed, a set of multi-source open-pit mine sample databases consisting of Gaofen-1, Gaofen-2 and Google Earth satellite images with a resolution of two meters is constructed, and an automatic batch production process of open-pit mine targets is designed. In this paper, pixel-based evaluation indexes and object-based evaluation indexes are used to compare the recognition effect of IMRT, faster R-CNN, Maximum Likelihood (MLE) and Support Vector Machine (SVM). The IMRT model has the best performance in Pixel Accuracy (PA), Kappa and MissingAlarm, with values of 0.9718, 0.8251 and 0.0862, respectively, which shows that the IMRT model has a better effect on open-pit mine automatic identification, and the results are also used as evaluation units of the environmental damages of the mines. The evaluation results show that level Ⅰ (serious) land occupation and destruction of key mining areas account for 34.62%, and 36.2% of topographical landscape damage approached level I. This study has great practical significance in terms of realizing the coordinated development of mines and ecological environments.

The traditional mine remote sensing information pre-survey is mainly based on manual interpretation, and interpreters delineate the mine boundary shape. This work is difficult and susceptible to subjective judgment due to the large differences in the characteristics of mining complex within individuals and small differences between individuals. CondInst-VoV and BlendMask-VoV, based on VoVNet-v2, are two improved instance segmentation models proposed to improve the efficiency of mine remote sensing pre-survey and minimize labor expenses. In Hubei Province, China, Gaofen satellite fusion images, true-color satellite images, false-color satellite images, and Tianditu images are gathered to create a Key Open-pit Mine Acquisition Areas (KOMMA) dataset to assess the efficacy of mine detection models. In addition, regional detection was carried out in Daye Town. The result shows that the performance of improved models on the KOMMA dataset exceeds the baseline as well as the verification accuracy of manual interpretation in regional mine detection tasks. In addition, CondInst-VoV has the best performance on Tianditu image, reaching 88.816% in positioning recall and 98.038% in segmentation accuracy.

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In the south area of China, there covers thick soil and flourish vegetation on the top of the rocks, so there is little research on lithology analysis by remote sensing in the south area, and there is also no mature methods on this aspect. Three Gorges are the south area which is thickly covered by soil and flourish vegetation and the lithology analysis is very difficult. Three Gorges possess the characters of complicated terrain, frequent geologic disasters and full-grown vegetation and soil, so it is very important to make lithology analysis in the area. In the paper we made analysis of lithology with remote sensing images focusing on the area of Three Gorges, adopted the idea of oriented-object, built three sorts of guideline sets of spectrum, texture and vegetation covering, made quantification of the guideline sets and based on the algorithm of concept grid mined the association rules of the lithology in the stratums of Three Gorges: Jia Second and Third Sections Jia Ling River Group T₁j² and T₁j³, Ba First and Second Sections Ba Dong GroupT₂b¹ and T₂b², and Da Ye Group T₁d.