Deep learning (DL) shows remarkable performance in extracting buildings from high resolution remote sensing images. However, how to improve the performance of DL based methods, especially the perception of spatial information, is worth further study. For this purpose, we proposed a building extraction network with feature highlighting, global awareness, and cross level information fusion (B-FGC-Net). The residual learning and spatial attention unit are introduced in the encoder of the B-FGC-Net, which simplifies the training of deep convolutional neural networks and highlights the spatial information representation of features. The global feature information awareness module is added to capture multiscale contextual information and integrate the global semantic information. The cross level feature recalibration module is used to bridge the semantic gap between low and high level features to complete the effective fusion of cross level information. The performance of the proposed method was tested on two public building datasets and compared with classical methods, such as UNet, LinkNet, and SegNet. Experimental results demonstrate that B-FGC-Net exhibits improved profitability of accurate extraction and information integration for both small and large scale buildings. The IoU scores of B-FGC-Net on WHU and INRIA Building datasets are 90.04% and 79.31%, respectively. B-FGC-Net is an effective and recommended method for extracting buildings from high resolution remote sensing images.
Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced convolutional neural network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN-based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected Zhangzha Town in Sichuan Province, China, and Lantau Island in Hong Kong, China, as the study areas. Each landslide inventory and corresponding predisposing factors were stacked to form spatial datasets for LSM. The receiver operating characteristic analysis, area under the curve (AUC), and several statistical metrics, such as accuracy, root mean square error, Kappa coefficient, sensitivity, and specificity, were used to evaluate the performance of the models. Finally, the trained models were calculated, and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine learning-based models have a satisfactory performance. The CNN-based model exhibits an excellent prediction capability and achieves the highest performance but also significantly reduces the salt-of-pepper effect, which indicates its great potential for application to LSM.
Hunan continuously operating reference station network is one of the most important infrastructures of the regional geospatial datum in Hunan province, China. It provides the official 24-h RTK service to the public. How to reveal the user behavior pattern by spatio-temporal analysis on location-based big data is significant for the service promotion. With procedures, such as cleaning, sampling, and so on, the usage count, fixing rate, and network delay data from August 2017 to July 2018 are first analyzed on multiple spatial and temporal scales. The results show that user behavior is strongly correlated to the surveying field work habits. Overall, the usage count is much more in the central and eastern, developed, and plain or hill area, while it is less in the western, underdeveloped, mountain and lake area. The suburbs are the most popular area. The usage count is also correlated to the local economic profile. Meanwhile, the Huaihua and Shaoyang cities need to be paid more attention to promotions. The hot spots revolution in 24 h can be divided into six stages as sleeping, recovery, first and second busy stages, adjustment, and dormancy when the hot spot successively increased and decreased around the Changsha-Zhuzhou-Xiangtan urban agglomeration and other 11 urban centers in the Hunan province. INDEX TERMS Location based big data, global positioning system, spatial-temporal analysis, user behavior, kernel density analysis, HNCORS.
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