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| 1 | Area,lake-level and volume variations of typical lakes on the Tibetan Plateau and their response to climate change,1972-2019显示文摘Most lakes have undergone significant changes on the Tibetan Plateau in recent decades,affecting water resources on the Tibetan Plateau and its surrounding areas.In this paper,we investigated the variations of 25 lakes in five sub-regions on the Tibetan Plateau from 1972 to 2019 based on SRTM DEM data and Landsat imagery.We used a method to derive lake-levels based on DEM and lake boundaries delineated from Landsat imagery,and then calculated the changes in lake area,level,and volume in 1972 to 2019.We also analyzed the potential impacts of temperature,precipitation,glacial and permafrost melting in lake changes during this period.The results show that the lakes tended to shrink until 2010 in southern and western plateau,after which they began to expand gradually but the overall trend is still shrinking.Limited meltwater from glaciers and permafrost and low precipitation are the main reasons for their shrinkage.The lakes in the central plateau,northwest plateau and northeast plateau tend to expand overall.The reason for the expansion of the lakes is not only precipitation but also the melting of glaciers and permafrost.Overall,the lake changes have gone through 3 phases,namely a slight decrease during 1972-2000,a rapid increase during 2000-2010,and a slowdown in the last decade(2010-2019).Multiple factors such as temperature,precipitation,the state of glaciers and permafrost have contributed to the changes in the lake. | Ju Zhang Qingwu Hu Yingkui Li Haidong Li Jiayuan Li | 2021 | Geo-Spatial Information Science2021，24，3： | 2 |
| 2 | VNLSTM-PoseNet: A novel deep ConvNet for real-time 6-DOF camera relocalization in urban streets显示文摘Image-based relocalization is a renewed interest in outdoor environments,because it is an important problem with many applications.PoseNet introduces Convolutional Neural Network(CNN)for the first time to realize the real-time camera pose solution based on a single image.In order to solve the problem of precision and robustness of PoseNet and its improved algorithms in complex environment,this paper proposes and implements a new visual relocation method based on deep convolutional neural networks(VNLSTM-PoseNet).Firstly,this method directly resizes the input image without cropping to increase the receptive field of the training image.Then,the image and the corresponding pose labels are put into the improved Long Short-Term Memory based(LSTM-based)PoseNet network for training and the network is optimized by the Nadam optimizer.Finally,the trained network is used for image localization to obtain the camera pose.Experimental results on outdoor public datasets show our VNLSTM-PoseNet can lead to drastic improvements in relocalization performance compared to existing state-of-theart CNN-based methods. | Ming Li Jiangying Qin Deren Li Ruizhi Chen Xuan Liao Bingxuan Guo | 2021 | Geo-Spatial Information Science2021，24，3： | 1 |
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