Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction.

It is of great importance for petroleum exploration to study the sedimentary features and the growth pattern of shoal water deltas in lake basins. Taking spatio-temporal remote sensing images as the principal data source, combined with field sedimentation survey, a quantitative research on the modern deposition of Ganjiang delta in the Poyang Lake Basin is described in this paper. Using 76 multi-temporal and multi-type remote sensing images acquired from 1973 to 2015, combined with field sedimentation survey, remote sensing interpretation analysis was conducted on the sedimentary facies of the Ganjiang delta. It is found that that the current Poyang Lake mainly consists of three types of sand body deposits including deltaic deposit, overflow channel deposit, and aeolian deposit, and the distribution of sand bodies was affected by the above three types of depositions jointly. The mid-branch channels of the Ganjiang delta increased on an exponential growth rhythm. The main growth pattern of the Ganjiang delta is dendritic and reticular, and the distributary channel mostly arborizes at lake inlet and was reworked to be reticulatus at late stage.