This study proposed a method of traffic risk assessment of snow disaster based on multi-source information fusion including meteorological observation information and network information. Using the meteorological monitoring data in long time scale, the temporal and spatial characteristics of snowfall in Hebei province was analyzed with regard to frequency of snowfall and maximum of snow depth. The snow hazard intensity in different cases of return period events was calculated by function distribution fitting. Besides, we classified the exposure of highway in Hebei province by collecting the information of road congestion during holidays including Spring Festival and National Day from portal news websites, highway websites and so on. Finally, the risk matrix method was adopted to analyze the traffic risk of snow in Hebei province. That method was applied to Hebei province, and the study results were as follows: ① In recent 5 years, the snowfall in Hebei province has decreased. However, the snowfalls in different area fluctuated from decade to decade over a long-term scale. The high value areas of snow depth were located in Zhangjiakou, Chengde, and Shijiazhuang City, but they changed in different decades; the high frequency areas of snowfall were basically fixed, which was located in Kangbao, Guyuan and Chongli County in Zhangjiakou City, and the northwest of Fengning County in Chengde City. ② The sections of highway with high exposure were important provincial and city-level linking-up roads and the expressways which mainly connected Beijing with Shanghai, Guangzhou, Harbin and other major cities. ③ Affected by the comprehensive effects of hazard intensity and exposure, the high-risk sections of snow disaster were mainly concentrated in Beijing-Hong Kong-Macao Expressway (Shi'an Expressway G4), Jingkun Expressway G5, Beijing-Chengde Expressway G45, Changshen Expressway G25, and Zhangjiakou-Chengde Expressway G95, which need good risk prevention measures prepared against the snow disaster.

DMSP/OLS (Defense Meteorological Satellite Program Operational Linescan System) night-time light images can objectively reflect the intensity of human activities; therefore they were widely used in a variety of fields for urban remote sensing. However, the raw night-time dataset cannot be used directly in these researches due to the lack of inflight calibration, thus it needs to be further corrected. There are two problems existed in the long-time series of DMSP/OLS night-time light image dataset that should be addressed in the image correction procedure. First, every image in the raw night-time light image dataset cannot directly compare with each other due to the issue of discontinuity; second, there is a pixel saturation phenomenon existed in every image of the raw night-time light image dataset. In order to solve these problems, a method based on invariant region was proposed. This method included the intercalibration, the saturation correction, and the continuity correction procedures among all the images from the raw images dataset. All the night-time light images of China, which were extracted from the raw images dataset, were corrected using this method. Finally, this correction method was evaluated by analyzing the relationships between the night-time light images and the corresponding gross domestic product (GDP) data and the corresponding electric power consumption data respectively. Through the analysis toward the evaluated results, two main conclusions were acquired. One was that this method had solved the problem of discontinuity in the raw image dataset; the other one was that this method could reduce the pixel saturation phenomenon that existed in every images of the raw night-time light image dataset. However, this method has not completely solved the problem of pixel saturation. How to perfectly solve this problem is the core issue for future research on night-time light data application.

People′s movement in a city is driven by purpose. Moreover, the distribution of urban spatial structure can cause the phenomenon of human convergence or dispersion, and this phenomenon is always changing over time. Therefore, understand the spatio-temporal patterns of human convergence and dispersion could provide us a good knowledge of human travel demand in the urban context, so that the better decisions can be carried out to meet the demands of citizens. With the rapid development and widespread use of location-aware devices, it becomes relatively easy to collect the large-scale human sensor datasets and to bring new opportunities and challenges to the study of urban human mobility. Especially in recent years, mobile phone data has become a rich resource for research and it is widely used to study the human mobility patterns from various aspects, with regard to its advantage in tracking the long-term and large-volume of urban citizens with low cost. In this paper, taking Shenzhen City as an example, we firstly extracted the origin-destination flow matrix from the mobile phone location data and employed Local Moran′s I to identify people’s convergence or dispersion areas. And then a time series matrix was constructed according to the temporal signatures of these areas. SOM algorithm was selected to cluster the matrix into nine typical human convergence-dispersion patterns. Based on the land use data, we have calculated the percentage of different land use types for each pattern to explain the human convergence-dispersion phenomenon, thus we could understand the relationship between human mobility patterns and urban spatial function. This study helps us to acquire a good knowledge of the daily human convergence and dispersion patterns within different urban functional areas. The findings derived from this study could give us the insights about where and when the convergence and dispersion of people would occur in Shenzhen. This knowledge is helpful for the city planners to improve the urban local planning and makes it more suitable for human mobility applications, such as making targeted adjustments to optimize the urban transportation facilities to improve their service efficiency.

Target classification and recognition (TCR) of high resolution remote sensing image is an important approach of image analysis, for the understanding of earth observation system (EOS), and for extracting information from the automatic target recognition (ATR) system, which has important values in military and civil fields. This paper reviews the latest progress and key technologies between domestic and international remote sensing image TCR in optical, infrared, synthetic aperture radar (SAR) and synthetic aperture sonar (SAS). The main research levels and the contents of high resolution remote sensing image TCR are firstly discussed. Then, the key technologies and their existing problems of high resolution remote sensing image TCR are deeply analyzed, from aspects such as filtering and noise reduction, feature extraction, target detection, scene classification, target classification and target recognition. Finally, combined with the related technologies including parallel computing, neural computing and cognitive computing, the new methods of TCR are discussed. Specifically, the main framework includes three aspects, which are detailed in the following. Firstly, the predominant techniques of high resolution remote sensing image processing are discussed based on high performance parallel computing. And the hybrid parallel architecture of high resolution remote sensing image processing based on Apache Hadoop, open multi-processing (OpenMP) and compute unified device architecture (CUDA) are also presented in this paper. Secondly, application prospects of TCR accuracy promotion are analyzed based on a thorough study of neuromorphic computing, and the method of multi-level remote sensing image target recognition based on the deep neural network (DNN) is introduced. Thirdly, the model and algorithm of big data uncertainty analysis for remote sensing images are discussed based on probabilistic graphical model (PGM) of cognitive computing, and the multi-scale remote sensing image scene description is given based on hierarchical topic model (HTM). Moreover, according to the related research of multi-media neural cognitive computing (MNCC), we discuss the development trend and research direction of TCR for remote sensing images big data in the future.

Mean solar exoatmospheric irradiances over band b (ESUN_b) is an important parameter for computing apparent reflectance. In recent years, ZY-1 02C, ZY-3 and GF-1 were launched and they have played an important role in land and resources survey as well as urban planning and construction. However, ESUN_b values of these domestic remote sensing satellites have not been released publicly, and till now it causes difficulties in processing their DN values to the physical quantities. In order to calculate ESUN_b values, Extraterrestrial Solar Spectral Irradiance and Spectral Response Function (SRF) are necessary. This paper aimed to calculate the unknown ESUN_b based on a selection of optimal solar spectrum from nine released solar spectra, including ASTM-E490, WRC, Wehrli, etc. A number of medium spatial resolution and high spatial resolution sensors whose ESUN_b had been officially released were chosen, such as EO1/ALI, Terra/ASTER, QuickBird, etc. Through calculating the mean absolute error (MAE) and the standard deviation of absolute error (SDAE) between the calculated ESUN_b values and the officially released values for these sensors, WRC solar spectrum and Wehrli solar spectrum were selected as the optimal solar spectra for sensors with medium spatial resolution and high spatial resolution respectively. This is because WRC solar spectrum showed the least MAE (3.208 W·m²·μm^-1) and Wehrli solar spectrum showed the least MAE (0.701 W·m²·μm^-1) and SDAE (1.034 W·m²·μm^-1). Based on WRC solar spectrum and Wehrli solar spectrum, ESUN_b values of ZY-1 02C/PMS, ZY-3/Multispectral camera and Three-line array camera, GF-1/WFV and PMS were calculated and given accordingly. The resultant values were between the maximum and minimum ESUN_b values for all the nine solar spectra. In addition, the uncertainty analysis was conducted and their relative biases due to the selection of different solar spectra were between -1.938% and 1.477%. Calculating ESUN_b values in this way is simple and easy, because it can ensure the comparability of data between different remote sensors. This method can be applied to the other new remote sensing sensors so as to fully utilize their data.

Data visualization is an important service in remote sensing applications. To address the problems that it is difficult for the static pre-built map tile service to meet the requirements of professional data view, map configuration, spatial analysis and other applications, this paper presented a solution architecture for the real-time rendering and interactive visualization of remote sensing big data. Firstly, on the rendering nodes, a rendering-tile structure for image was constructed to improve the reading speed of remote sensing images. Secondly, on the visualization servers, a data-computing load balancing strategy was proposed to optimize the rendering efficiency of map tiles. Thirdly, a set of service interfaces for the interactive visualization was designed for the front ends of services. Compared with the traditional technology, this solution can not only achieve the real-time rendering and the interactive visualization of remote sensing data, but also obtain an equivalent service performance to the pre-built tile map service. Finally, based on the above solutions, an interactive visualization prototype system of remote sensing data was developed and was applied into the demonstrations of the real-time viewing of remote sensing images, the visualized computing and the visualized analysis.

Urban population distribution and activities are always the hot research topics. Identifying the spatial-temporal variation and predicting future trends are of great significance for estimating population accurately, making policy effectively, and warning of population booming timely. With the availability of data and the development of data processing technique, multisource data with both spatial and temporal features, such as mobile signaling data, have been used in population studies. In this paper, q-statistic was firstly applied as an exploratory analysis, then Bayesian spatial-temporal models were used to evaluate patterns of urban population and make prediction of future trends. The Chaoyang, Beijing in 2017 was selected as empirical study of this model. The spatially stratified heterogeneity was detected by q-statistic in Geodetector firstly. Then we explored the overall spatial variation, overall time trend and the departures of the local trends from the overall trend of resident population in Chaoyang by use of Bayesian spatial-temporal hierarchical model. Secondly, we applied Bayesian Gaussian predictive process to predict the resident population in December of 2017 by incorporating other relevant influential factors. The results show the perfect spatial stratified heterogeneity for resident population in Chaoyang, and the overall spatial variation demonstrates an increasing trend of population from center to the outside along the main ring road in Beijing. The overall time trend is still growing all over Chaoyang district, while the local trends, which departure from the overall trend of resident population, are different between each sub-districts in Chaoyang. Moreover, the spatial distribution of predicted resident population shows a high consistency with the observed resident population, and the prediction accuracy can be well accepted on the scale of Chaoyang district. However, prediction accuracy shows obvious difference on scale of sub-districts, with the worst prediction accuracy in the capital airport area. These findings show that Bayesian hierarchical model and Bayesian Gaussian predictive process are reliable in empirical study of population evaluation and prediction by effective application of multisource spatial-temporal data. Researches in this paper can be an excellent theoretical and practical support for mining multisource spatial-temporal data and assisting multiscale analysis with Bayesian spatial-temporal model, and provide an important basis for population controlling and early warning in urban population management.

In recent years, China's health industry has made rapid progress, but there are still gaps between different regions. As one of the basic public services, medical services is closely related to the quality of resident’s life. However, there are still many problems in getting medical services with high quality in some areas, e.g., inconvenient transportation, lack of medical facilities, poor medical services, and so on. Therefore, it is critical to evaluate the rationality of the distribution of medical resources in a region. Spatial accessibility of medical facilities is an important index to evaluate the rationality of medical service distribution. Among a wide range of methods in measuring the spatial accessibility of facilities, the two-step floating catchment area method (2SFCA) is most popular. In this study, we analyzed the spatial accessibility of medical facilities in Beibei District, Chongqing, by using the modified two-step floating catchment area method and GIS spatial analysis technology. The modified two-step floating catchment area method takes the scale of hospital grade and the distance between supply and demand points into account, and adds Multi Catchment Sizes and Gaussian distance decay to make up for deficiencies of the traditional two-step floating catchment area method, so it is more widely used in spatial accessibility analysis. The spatial accessibility of medical facilities in Beibei were visualized by spatial interpolation. Moreover, the cluster of spatial accessibility was analyzed by Hot Spot Analysis. The basic unit of analysis was administrative villages. The results show that: (1) The results obtained by the original/unmodified and the modified two-step floating catchment area methods have different characteristics, but the modified takes into account the attraction of hospital scale to residents and the influence of distance attenuation factors to residents travel intention, it has higher sensitivity in ide.pngying high accessibility regions with internal differences and low marginal accessibility regions, so its results can better reflect the spatial accessibility of medical facilities. (2) Overall, the spatial accessibility of medical facilities in Beibei District is high, illustrating that the medical services are more accessible to local residents. Meanwhile, the spatial accessibility of medical facilities in Beibei gradually decreased from central areas to surrounding areas. (3) The spatial accessibility of medical facilities in Beibei District varies greatly with obvious polar differences. The high-value areas are mainly concentrated in Dongyang Street, Chaoyang Street, Tiansheng Street, Beiwenquan Street, and Longfengqiao Street, while the low-value areas are mainly concentrated in marginal areas such as Jindaoxia Town, Liuyin Town, Sansheng Town, Fuxing Street, and Jingguan Town, etc. Our findings can provide reference for the relevant departments to make more informed decision-making.

The spectral information of remote sensing images has integrated and realistic characteristics. It has become an important means of using remote sensing information and GIS technology to estimate forest biomass in global change research area. Firstly,the development of using remote sensing information to estimate forest biomass was summarized in this paper. Then four methods which included the method based on relationship between remote sensing information and biomass, the method based on fusion remote sensing data and process model, the method based on K-Nearest neighbor and the method based on artificial neural network were discussed. Finally the shortcomings of current research and the emphases of future research were given in this paper.

With the wide application of Unmanned Aerial Vehicle （UAV) in the inspection of power transmission line, the demand for objects detection and data mining from images acquired by UAV also grows significantly. Traditional detecting methods use some classical machine learning algorithms, such as support vector machine （SVM), random forest or adaboost etc. and combine the low level features such as gradient, colors or texture to detect electrical devices. These image features must be carefully designed and changed a lot from various object kinds. Thus, they are not suitable for UAV images with complex background and multiple kinds of object. On the other hand, the disadvantages of these methods are that they cannot take advantage of the high quantity and large coverage of UAV acquired images, and cannot get a satisfactory accuracy. The recent developing Deep Learning method brings light to this problem. Convolutional neural network （CNN) performs excellently in object recognition area and outstand many other methods used in the past. Without the need of extracting images’ features, CNN becomes the many state-of-the-art methods in object recognition rapidly. In object detection, Region-based convolutional neural networks （RCNN) retrieves the region that may contain the object from the images to detect and recognize the object. However, the computation is so expensive that it cannot meet the requirement of detecting massive UAV’s images and cannot be used in practical projects. Fast R-CNN and Faster R-CNN solve this problem by changing the way of object retrieval. They use features produced by CNN network layers and apply a region proposal network layer behind to locate the object. After that, fully connected layers and softmax layer follow to classify the features corresponding to object into special kinds. Using this strategy, Fast R-CNN and Faster R-CNN save lots of time to produce region proposal and can perform object detection at nearly real time. The principle and processes of Faster R-CNN and several other object detection methods are described in this paper, and they are tested for electrical devices detection from images of the power transmission line obtained by UAV. We analyzed the influence of several key parameters to the device detection results, such as the dropout ratio, non-maximum suppression （nms) and batch size. Then, we gave some constructive advice of tun ing parameters in Faster R-CNN. We also analyzed the advantages and weakness of three advanced detection algorithms, including Deformable Part Models （DPM) and two deep learning-based methods named Spatial pyramid pooling networks （SPPNet) and Faster R-CNN. Finally, we constructed image datasets of power transmission line inspection obtained by UAV and tested the three methods above. The recall ratio and accuracy ratio of them are compared and the superiority of the Faster R-CNN is validated. Testing results showed that Faster R-CNN method can detect various electrical devices of different categories in one image simultaneously within 80 milliseconds and achieve an accuracy of 92.7% on a standard test set, which is of great significance in real-time power transmission line inspection. These results also showed the advantages of the Faster R-CNN and we apply Faster R-CNN in our practical projects to detect electrical devices.

As a complement for urban public transportation, taxi plays an increasingly important role in people’s life, which is also taken as one of the most important windows opening to the public. With the rapid development of economy, traffic condition is becoming more and more terrible, which causes the heavy traffic situation in many cities in China. Taxi is a type of transportation resourece that is dynamic and unbalanced in different road networks and at different time, which faces a lot of problems, such as the difficulty in finding a taxi for a passenger or finding a passenger for a taxi driver. This makes taxi transportation to be poorly efficient, and negatively affects the performance of government. It is helpful to learn about the dynamic space in the city, and the patterns of citizens’ working, living and travelling after studying the features and rules of taxis’ pick-up and drop-off distribution. Moreover, it is helpful to learn about the “hotspots” in the city, which represents the areas with huge volumes of taxis’ pick-up and drop-off activities. Based on taxis’s GPS trajecotries big data, this paper puts forward a new model called Line Density Model (LDM) to detect the space time distribution pattern of taxis’pick-up and drop-off activities, in which there are linear trends existing within the taxis’ pick-up and drop-off, and the “hotspots” exhibiting linear trends near the road network in the city. Finally, Wuhan city is taken as the testing area, and the experimental result shows that taxis’ pick-up and drop-off distribution is unbalanced in different areas and at different time, which helps to understand the dynamic and the pattern of the public’s working, living and travelling, and gives a reference to find the “hotspots” at different time in Wuhan city.

The ability to acquire the remote sensing data is increasing day by day, which directly causes the remote sensing data to become diverse and massive, and the issue that the massive amount of data is being non-affordable to store has become more and more prominent. On the other hand, due to the lack of an effective and efficient method of storage management, the data that theterminal application need is difficult to found in a timely manner, therefore, is stored but useless. This paper focuses on the storage and management problems of the massive, high through put and spatially structured remote sensing data and the basic land information products. We have presented a storage and management method which uses the big data structure and can integrate both the vector and raster data. Based on the MongoDB database, the prototype system is realized and we use the data of PB rangeto test it. Eventually, we have proved that this method meets the demand for the storage and management of the remote sensing vector-raster data in the era of big data. On the basis of the study results and prototype system, the following studies need to be further explored: (1) The organization and management methods for internal data of resources, especially the objective and timely management for the vector data; (2) Real-time interactive visualization methods for different data types and storage modes of resources, achieving dynamic extraction and rendering ability based on in the heterogeneous data model; (3) To construct large data computing architecture on the heterogeneous type storage mode, and to implement multimodal computing framework to meet the needs of the remote sensing applications require.

Citizens′ intra-city trips are often influenced by the allocation of resources and urban functional areas, such as the educational areas, entertainment areas, business areas and residential areas. Therefore, citizens′ travelling pattern can reflect the city structure and unveil the urban function zoning. Meanwhile, the widespread of GPS vehicle navigation equipment makes it possible to achieve a vast amount of vehicle trajectory. With the support of the vast vehicle trajectory data, we can analyze citizens′ travelling mode and understand the city structure. In this paper, we investigated citizens′ travelling pattern and the urban functional structure of Beijing with the taxi trajectory data of one-month period and the information of land parcels divided by major roads. To analyze the citizen′s travelling mode, we extracted the trip volume time series in every parcel and adopted a new method which could cover the proximity on both the values and the behavior to cluster the time series data. In the end, we discussed the correlation between citizens′ travelling mode and urban functions in different regions, based on Beijing′s POI data. The result showed that there were obvious differences in the travelling patterns between the weekdays and weekends. During the weekdays, there were two rush hours, which were different from the ordinary commute rush hours. Looking at the clustering results of the weekday data, the spatial distribution of different clusters basically arranged like concentric circles, and the travelling volume of every circle decreased with respect to the increasing distance to its center. The conclusions made in this research are meaningful for the analysis of citizens′ travelling mode and for assisting urban transportation planning.

Web texts contain a great deal of implicit geospatial information, which provide great potential for the geographic knowledge acquisition and service. Geographic knowledge graph is the key to extend traditional geographic information service to geographic knowledge service, and also the ultimate goal of the collection and processing of implicit geographic information from web texts. This paper systematically reviews the state of the arts of the researches on open geographic semantic web, geographic entity and relation extraction, geographic semantic web alignment, and knowledge graph storage methods. The pressing key scientific issues are also addressed, including the quality evaluation of geospatial information collected from web texts, geographic semantic understanding, spatial semantic computing model, and heterogeneous geographic semantic web alignment.

The efficiency for querying complex spatial information resources is an important indicator to evaluate the performance of current spatial databases. Traditional single node relation spatial data management is difficult to meet the demand of high-performance in querying large amounts of spatial data, especially for the complex join query on multi-table. In order to solve this problem, we design and implement a spatial database middleware prototype system. This system takes full advantages of the massive parallel processing (MPP) and shared-nothing architecture. In consideration of the characteristics of spatial data, we design the spatial data parallel import, multi-spatial-tables join strategy, spatial data query optimization and other algorithms and models. This paper firstly introduces the development status of parallel database systems in recent years, and then elaborates its MPP architecture and its organizational model, and the strategy of the join query on multi-spatial-table. Finally, we made some query experiments on massive spatial data and analyzed the results of these inquiries. The experimental results show that this system indicates a good performance (nearly linear speedup) in processing the complex query of massive spatial data. Compared with the tradition single node database, this system can fully improve the efficiency of complex querying for large spatial data, and it is a more efficient solution to solve the complex spatial data queries.

Ground Penetrating Radar(GPR) has been more and more widely used in archaeological investigations, because it can be a non-destructive, cost-effective way to locate buried structures in archaeological studies. Compared with the conventional geophysical tools used in the shallow explorations, the electromagnetic method, ground-penetrating radar (GPR), is more economical and is capable to produce large amounts of continuous, high resolution subsurface data. GPR canextend the exploration range of remote sensing (RS) to subsurface. However, because of the non-uniqueness of inversion, an anomaly could be raised by the archaeological interest or the inhomogeneity of underground matrixes, therefore studying the typical anomalies of diferent archaeological targets on GPR images is helpful to distinguish the “true” anamolies from the “fake” anamolies. Furthemore, some experiences and references could be provided. The following experiments have been carried out: firstly, in order to analysize how the small targets of different materials and rammed earth will raise anomalies on the GPR maps, GPR was emploied to detect five pre-buried targets that are equivalent to the archaeological interest and a beacon tower in a integrated experiment station of remote sensing. The first experiment of GPR prospection was designed to simulate the buried-enviorment of the archaeological structure in the northwest region of China whose climate is predominantly arid. Secondly, the authors applied GPR in detecting the residual city walls of Xuanquanzhi ruins, then analysized the response features of the walls, and found that the detecting results well fitted the excavation. The engineering practice indicates that the ground penetrating radar technology is successful and effective in invetigating the archaeological remains which are of small scale, buried shallowly and very analogical with the matrixes in electromagnetic nature. The response models of different archaeological targets, which are respectively considered as the point, line and surface shape, have been proposed and explained according to the principle of rectilinear propagation of electromagnetic wave.

Gannan region is located in the southern Jiangxi Province, China. Gannan region includes 2 districts and 15 counties in Ganzhou. It has hilly land resources and its climate conditions are benefit to plant citrus. With the support and guidance from local government, Gannan region has experienced the boom of citrus planting and become the largest citrus production region in China over the past decades. Despite the economic success, the rapid and extensive citrus orchard expansion has brought great concern about ecological consequences. It is necessary to map citrus orchard for understanding the effects of citrus expansion. The objective of this study is to map the citrus orchard in 1990, 1995, 2000, 2005, 2010 and 2016 in Gannan Region. An image composite method was applied and total 2140 tiles of Landsat historical images were employed to generate seasonal images with lowest cloud composite at the pixel level. Random Forest classifier was used to classify multiple dimensional features from spectral, spatial and topographic domains. The image composite and classification were implemented with Google Earth Engine (GEE) platform. Results showed that: (1) GEE can effectively execute complex workflows of remote sensing data processing and information digging. (2) Lowest cloud composite at the pixel level is a reliable method of producing clear seasonal images for the region influenced by cloud and rain frequently. (3) Random forest classifier was suitable for mapping citrus orchard with an average overall accuracy (OA) of 93.15% and a Kappa coefficient of 0.90. (4) The citrus orchard has expanded extensively with the area from 9.77 km² in 1990 to 2200.34 km² in 2016. Citrus orchard was becoming clustered especially in Xunwu, Xinfeng and Anyuan and was mainly converted from forest, bush and cropland.

Human mobility has received much attention in many research fields such as geography, sociology, physics, epidemiology, urban planning and management in recent years. On the one hand, trajectory datasets characterized by a large scale, long time series and fine spatial-temporal granularity become more and more available with rapid development of mobile positioning, wireless communication and mobile internet technologies. On the other hand, quantitative studies of human mobility are strongly supported by interdisciplinary research among geographic information science, statistical physics, complex networks and computer science. In this paper, firstly, data sources and methods currently used in human mobility studies are systematically summarized. Then, the research is comprehended and divided into two main streams, namely people oriented and geographical space oriented. The people oriented research focuses on exploring statistical laws of human mobility, establishing models to explain the appropriate kinetic mechanism, as well as analyzing human activity patterns and predicting human travel and activities. The geographical space oriented research focuses on exploring the process of human activities in geographical space and investigating the interactions between human movement and geographical space. Followed by a detailed review of recent progress around these two streams of research, some research challenges are proposed, especially on data sparsity, data skew processing and heterogeneous data mining, indicating that more integration of multidiscipline are required in human mobility studies in the future.

In the era of big data, the rapid growth of geographic spatial temporal data has challenged the conventional application concepts, technical framework and service modes. In this paper, the concept and features of geographic spatial temporal big data is elaborated firstly. Then, the characteristics and challenges of the geographic spatial temporal big data computation are analyzed. Particularly, the theory of collaborative computing and service for the geographic spatial temporal big data is developed, which includes four levels of collaboration: data collaboration, technology collaboration, service collaboration and producing collaboration. According to the demand of the market-oriented operation and platform-based service, the technical frameworks of the geographic spatial temporal big data collaborative computing are designed. Furthermore, four common key technologies are discussed, including the remote sensing data preprocessing, the geographic spatial temporal data storage and management, the high performance computing and the visualization of geographic spatial temporal big data. Next, the remote sensing data processing system is developed, and is taken as a case to illustrate the implementation of collaborative computing and service of geographic spatial temporal big data. At last, this paper forecasts the future application mode of geographic spatial temporal big data.

As the space for human habitation and activity, urban buildings are an important part of the city. Their renewal and renovation affects development of the city and changes people’s life at all times. Functional classification of urban buildings provides supporting evidence for categorizing urban functional areas, and also helps the government in land use planning, as well as managing the distribution of population and resources, promoting the sustainable development of urban areas. However, current classification technology of remote sensing is insufficient to make functional classification of urban buildings. In this paper, we analyzed urban information in great depth, by classifying the function of urban buildings. The efficiency and precision of the classification is improved after combining remote sensing, the Internet POI (Point of Interest) data and GIS technology. We first chose the method of CNN (Convolutional Neural Networks) to extract building information from remote sensing images of high resolution. The precision of the extraction is above 93% as is shown by precision evaluation. POI data was then classified into 3 types by manual work, namely buildings used for commercial service, public service and residence. The classified POI data were estimated by Kernel Density. After which the mean Kernel Density value of every type of buildings was calculated and these three types of buildings were delimited by thresholds. Thus, buildings for commercial service, public service and residence could be recognized from the building information assisted by POI data, achieving the functional classification of urban buildings. This method has shown good extraction efficiency compared to visual interpretation-the overall accuracy is 86.85% and Kappa Coefficient is 0.8153 according to precision evaluation. In future research, this method can be used to classify and identify different types of urban buildings. However, there are still some problems to be discussed in this method. For example, when defining buildings’ functional types by threshold of Kernel Density, one building may have more than one or no type. Besides, POI data have some limitations when representing the range of different types of buildings: one point may represent either a grand shopping mall or a convenience store. These will be addressed in future studies.

Arising from rapid growth of economy and population,urban sprawl has become a major challenge for sustainable urban development in the world. In order to assist urban planning, applicable methods and models are required to guide and constrain the growth of urban areas. Nowadays, urban growth boundaries (UGBs) has been regarded as a common tool used by planners to control the scale of urban development and protect rural areas which has a significant contribution to local ecological environment. However, existing models mainly focus on the delimitation of UGBs for urban development in single-scenarios. To date, there are rarely studies to develop efficient and scientific methods for delimiting the UGBs by taking the influences of macro policy and spatial policy into account. This paper presents a future land use simulation and urban growth boundary model (FLUS-UGB) which aims to delimit the UGBs for the urban areas in multi-scenarios. The top-down system dynamics (SD) model and bottom-up cellular automaton (CA) model are integrated in FLUS sub-model for simulating the urban growth pattern in the future. Furthermore, the UGB sub-model is developed to generate the UGBs that uses a morphological technology based on erosion and dilation according to the urban form produced by FLUS. This method merges and connects the cluster of urban blocks into one integral area and eliminates the small and isolated urban patches at the same time. We selected the Pearl River Delta region (PRD), one of the most developed areas in China, as the case study area and simulate the urban growth of PRD region from 2000 to 2013 for validate the proposed model. Then we used FLUS-UGB model to delimit the UGBs in PRD region of 2050 under three different planning scenarios (baseline, farmland protection and ecological control). The results showed that: (1) the model has high simulation accuracy for urban land with Kappa of 0.715, overall accuracy of 94.539% and Fom 0.269. (2) the method can maintain the edge details well in areas with high urban fragmentation and fractal dimension. This research demonstrates that the FLUS-UGB model is appropriate to delineate UGB under different planning policies, which is very useful for rapid urban growth regions.

The international relations are intricate and ever-changing since the 21^st century, and have brought profound changes to the world's economy, security, and diplomacy. These changes have had a major impact on China's internal and external policies. A comprehensive and timely analysis of international relations and its changing characteristics has important reference value for China's economic and diplomatic development planning. The analysis of international relations has spatio-temporal characteristics, and it needs real-time processing. Thus, it needs to introduce the methods of spatio-temporal big data analysis to analyze international relations. Traditional mass media such as news, radio, etc. record all kinds of events happening in the world. It contains a wealth of information. Compared with social media data recording personal activities, it is more suitable for large-scale and long-term analysis of human society. The Global Database of Events Language, and Tone (GDELT) is a free and open news database which monitors news from print, broadcast, and online media in the world, analyzes the texts and extracts the key information such as people, place, organization, and event. This paper researches the network characteristics of GDELT based on theory of complex network and further analyze the relations between countries. Firstly, this paper constructs national interaction networks using GDELT, then analyze the interaction relationship between countries through network characteristic statistics, and finally detect the time series changes of the national conflict event interaction network. The results show that: (1)The National interaction network has scale-free characteristics, the interaction between countries is unevenly distributed from a global and local perspective. Very few countries have lots of interactions while most countries have very few interactions, and one country has lots of interactions with a few countries while a few interactions with most countries. (2) Sudden changes in the national interaction network of conflict events often indicates some significant national conflict events. This paper can provide a new perspective for the exploration of international relations and a reference for the analysis of news media in the era of big data.

Trajectory clustering, which aims to uncover the meaningful spatial distributions and temporal variations of moving objects, is of much importance in understanding potential dynamic mechanisms and predicting future development. However, placing many focuses on locational changes, many studies have made limited use of the time dimension in trajectories. This paper presents a density-based clustering method, which integrates time and space information in identifying significant migrating paths from trajectory datasets. Definition of temporal distances between any line segments decomposed from trajectories as well as the criterion of distance threshold selection is provided in detail. The experiments conducted on ocean eddies in the South China Sea demonstrate the effectiveness of this method in obtaining spatiotemporal migrating patterns. The migrating paths in the results are shortened, or separated into parts, or they turn insignificant as the effect of including time component in density clustering, which reveal more specific movement characteristics in the temporal domain covered by spatial clustering. This advantage facilitates the analysis of objects moving along the same path while displaying distinct time patterns.

Three-dimensional GIS (3D GIS) is one of the primary and typical contents of GIS technology at present and in the future, which overcomes the constraints of representing 3D GIS spatial information in two-dimensional map, as well as provides a more effective decision-making support for people's daily life. This paper focuses on the research progress and its key technologies of 3D GIS, including the data model, database management and visual analysis. The pilot applications of 3D GIS in Wuhan are also illustrated. The entire 3D space of the city is represented by 3D GIS. Then construction of the large-scale city digitalization is enabled with the improvement of city management. Finally, the applications of 3D GIS for spatio-temporal information bearing engine and spatial intelligence in smart city and city safety are investigated.

Rainstorm-induced geological disasters happen frequently in recent years and cause great loss in some regions of China. Enshi Autonomous Prefecture which located in Wuling Mountain Region is one of the fourteen Continuous Extremely Poverty Areas (CEPA) in China. The region is characterized with more frequent geological disasters, large amount of minority population and wide range of poor people distribution. Rainstorm-induced geological disaster threatens the communities usually, which pushes the people into a worse condition. Based on the theory of disaster system and natural disaster risk assessment, the rainstorm-induced geological disaster risk indicator system about Enshi Autonomous Prefecture was established considering the hazard affected bodies, hazard-formative environment and hazard bodies of this region. A comprehensive risk assessment model system is constructed and the sensitivity, vulnerability and risk of the rainstorm-induced geological disaster hazard in Enshi Autonomous Prefecture were explored at a l km × l km scale. The results are concluded as follows: (1) The inducing factor of the disaster is heavy rainfall, and the high hazard mainly occurs in the central of Enshi city and the southeast of Hefeng county. (2) By applying the Information Value Method and Analytical Hierarchy Process (AHP), the hazard-formative environment sensitivity evaluation index system was set up, including the topography, geomorphology, basic geology, hydrology and human activity. The less high and high sensitivity areas are mainly distributed in the Badong county, Enshi city and Hefeng county. (3) The building construction, population, social economy, arable land were selected for vulnerability assessment of hazard-affected bodies, and Lichuan city and Laifeng county were concluded to be occupied with more high vulnerability areas. (4) Rainstorm-induced geological disasters risk in Enshi Autonomous Prefecture is high according to the sensitivity and vulnerability assessment, and the areas having the highest risk mainly distributed in Badong county and Enshi city, which is consistent with the actual situation.