A landslide is a kind of geological disaster with high frequency, great destructiveness, and wide distribution today. The occurrence of landslide disasters bring huge losses of life and property. In disaster relief operations, timely and reliable intervention measures are very important to prevent the recurrence of landslides or secondary disasters. However, traditional landslide identification methods are mainly based on visual interpretation and on-site investigation, which are time-consuming and inefficient. They cannot meet the time requirements in disaster relief operations. Therefore, to solve this problem, developing an automatic identification method for landslides is very important. This paper proposes such a method. We combined deep learning with landslide extraction from remote sensing images, used a semantic segmentation model to complete the automatic identification process of landslides and used the evaluation indicators in the semantic segmentation task (mean IoU [mIoU], recall, and precision) to measure the performance of the model. We selected three classic semantic segmentation models (U-Net, DeepLabv3+, PSPNet), tried to use different backbone networks for them and finally arrived at the most suitable model for landslide recognition. According to the experimental results, the best recognition accuracy of PSPNet is with the classification network ResNet50 as the backbone network. The mIoU is 91.18%, which represents high accuracy; Through this experiment, we demonstrated the feasibility and effectiveness of deep learning methods in landslide identification.

GPUs have become important solutions for accelerating scientific applications. Most of the existing work on climate models now uses code rewritten using CUDA to achieve a limited speedup. This restriction also greatly limits followup development and applications. In this study, we designed and implemented a GPU-based acceleration of the LASG/IAP Climate system Ocean Model (LICOM) version 2, called LICOM2-gpu. Considering the extremely large codebase of the model and the occasional need to modify the code, we implemented the model completely in OpenACC. Several accelerated methods, including OpenACC data locality optimization, loop optimization and interprocess communication optimization, are presented. Developing for GPUs using OpenACC is substantially simpler than using the CUDA port. Thus, OpenACC is a suitable GPU programming model for complex systems, such as the Earth System Model and its components. Our experimental results using 4 NVIDIA K80 cards achieved up to a 6.6x speedup compared with 4 Intel(R) Xeon(R) CPU E5-2690 v2 GPUs.

High-performance computing of atmospheric general circulation models (AGCMs) has been receiving increasing attention in earth science research. However, when scaling to large-scale multi-core computing, the parallelization of an AGCM which demands fast parallel computing for long-time integration or climate simulation becomes extremely challenging due to its inner complex numerical calculation. The previous Institute of Atmospheric Physics of the Chinese Academy of Sciences Atmospheric General Circulation Model version 4.0 (IAP AGCM4.0) with one-dimensional domain decomposition can only run on dozens of CPU cores, so the paper proposes a two-dimensional domain decomposition parallel algorithm for it. In the parallel implementation of the IAP AGCM4.0, its dynamical core utilizes a hybrid form of latitude/longitude decomposition and vertical direction/longitude circle direction decomposition. Through experiments on a multi-core cluster, we confirmed that our algorithm is efficient and scalable. The parallel efficiency of the IAP AGCM4.0 can reach up to 50.88% on 512 CPU cores, and the IAP AGCM4.0 can be run long-term simulations for climate change research. We propose a scalable parallel algorithm with two-dimension domain decomposition for atmospheric general circulation models.We implement the parallelization of the IAP AGCM4.0 model whose parallel efficiency can reach up to 50.88% on 512 CPU cores.We implement computing of thousands of cores of the IAP AGCM4.0.We design different numerical experiments to test our algorithm, and the results demonstrate that our algorithm is effective and scalable.

In recent years, remote sensing images have played an important role in environmental monitoring, weather forecasting, and agricultural planning. However, remote sensing images often contain a large number of cloud layers. These clouds can cover a large amount of surface information. Therefore, an increasing number of cloud recognition methods have been proposed to reduce the impact of cloud cover. There are many difficulties in traditional cloud recognition methods. For example, the threshold method based on spectral features improves the accuracy of cloud detection, but it often leads to omission or misjudgment in cloud detection and depends on prior knowledge. To improve the accuracy and efficiency of cloud recognition, we use deep learning to address cloud recognition problems in remote sensing imagery. We propose a series of methods from the acquisition and production of training datasets to neural network training and cloud recognition applications. This paper describes a realization of cloud recognition of remote sensing imagery based on SegNet architecture. We have proposed two architectures named P_SegNet and NP_SegNet, which are modified from SegNet. Some parallel structures were also employed into the SegNet architecture to improve the accuracy of the cloud recognition. Due to these changes, this paper also discusses the impact of the symmetry network structure on the final accuracy. Our proposed method was compared with the well-known Fully Convolutional Neural Network (FCNN) approach. The results have demonstrated the feasibility and practicality of using deep learning approach for cloud recognition in remote sensing images.