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Ensemble learning methods can be used to evaluate landslide susceptibility when combined with remote sensing (RS) and geographic information systems (GIS). In this study, the rotation forest (ROF) and random forest (RF) ensemble learning models were applied to evaluate landslide susceptibility. The experiments selected the factors by analysing the linear relationship between the factors, explored the optimal proportions of non-landslide samples and landslide samples based on an unbalanced sample dataset, and used the factors before and after the selection to generate landslide susceptibility maps (LSMs) in the Zigui-Badong area. The results show that a suitable ratio between the sample types in the training set can achieve good results for both sensitivity and specificity. The RF models of the study area with 21 factors and 16 factors had sensitivities of 94.22% and 93.59%, respectively. The ROF models with 21 factors and 16 factors had sensitivities of 90.63% and 88.84%, respectively. Although both the RF and ROF models exhibited high accuracy, the RF model achieved a more reasonable and accurate LSM.

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 operation of dye-sensitized solar cells and quantum dot-sensitized solar cells (QDSSCs) depends strongly on the photoanode material employed. This is addressed in the present work by developing photoanodes based on a double-layer TiO 2 inverse opal material with different interconnected pore sizes in the bottom and upper layers for use in QDSSCs. The proposed photoanode material leads to better infiltration of the sensitizers and the hole transporting material through the entire depth of the TiO 2 layer. Double-layer TiO 2 inverse opal-based QDSSCs are demonstrated to facilitate the greater absorbance of quantum dots and obtain higher photocurrent and power conversion efficiency than QDSSCs adopting single-layer TiO 2 inverse opal photoanodes. Various QDSSCs employing double-layer TiO 2 inverse opal photoanodes with different pore sizes in the layers are tested. The CdS/CdSe co-sensitized solar cell adopting the optimum photoanode configuration and thickness provided the highest QDSSC conversion efficiency of 5.79%.

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.