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.

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.

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.

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.

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.

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 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.

This paper is finished on the purpose of writing an article of astronomical map for “Near-Modern Chinese Map Annals”, and we take a systematically study on the outstanding achievements of Chinese ancient astronomers in founding the "Theory of Sphere-Heavens", manufacturing “Armillary Sphere”, implementing astronomical observation and astronomical geodetic survey, and creating astronomical maps. The earliest recorded “Star table” was created by "Shi Shenfu" during the warring states period before in the 4th century B.C.; then, “Zhang Heng” created "Theory of Sphere-Heavens" and made the first “Turn Leaky Armillary Sphere” in the world during Han Dynasty in the 2nd century; Zhang Sui completed the national astronomical geodetic survey at 13 spots across China and is the first one who calculated the 1° arc length of local meridian at latitude 34° north to be 110.6 kilometers during Tang Dynasty; and Guo Shoujin” implemented "Universal Measurement" and "Leveling" during Yuan Dynasty. The ancient Chinese astronomical map discovered so far including: “the astronomical figure on a paint box cover in the Tomb of Marquis Yi of Zeng in Sui county before the warring states period of China " which is created in 433 B.C.; “the start figure on the tomb murals indicating four quadrant symbols and 28 lunar mansions” which is created in the Western Han Dynasty in Xi’an city, Shaanxi Province; the "DunHuang star map" which contains more than 1350 stars and is created during 705-710 A.D.; the “Suzhou stone carving astronomical map" which contains more than 1440 stars and is created by “Huang Chang” in 1190 A.D.; the "constellation chart of crossing seas” which is created by Zheng He in 1405-1433 A.D.; the "Star Barrier Figure" which contains 1812 stars and is created by “Xu Guangqi” in the early 17th century; and the “total star map on the north and south sides of equator” which contains 1876 stars and is created by “Verbiest” in the mid 17th century. In a word, the ancient Chinese astronomical geodetic results provided foundation for global positioning, and the “circular methods” of ancient astronomical map are similar to the “azimuthal projection”, which greatly improved and enhanced China's traditional cartography. The scientific methods of making the astronomical map with “circular” showed the excellence of “Traditional Cartography Family”, and made important contribution in the process of developing Chinese traditional cartography.

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.

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.

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.

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.

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.

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.

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.

The navigational charts in China's Ming dynasty are famous all over the world at that time, such as the ‘Hydrographic Guide Map’ so far discovered in the early Ming Dynasty, which collected the detailed shipping route to transport grain from the south to the north along the coastline between Ningbo and Liao river over thousands of years; and the ‘ZhengHe Navigational Chart’, which reveals the style and features in a period of great prosperity on Chinese ancient navigation achievements and navigational chart techniques and includes 14 seaway routes crossing Asia, Africa and Europe recognized as the first one in the world. The inheritance and development of the ‘Maritime Silk Road’ since Han dynasty, once again advocates the main purpose of the traditional business between china and foreign countries, which is the culture exchange and local product trading. The ‘Sea Route Book’ recorded the navigational guide of South China Sea as the legacy and at the cost of generations of fishermen's life during the ancient times. The ‘Ryukyu Nautical Charts’ shows the distribution of navigation routes that ancient Chinese navigators recorded during their visits to Ryukyu, Taiwan, Japan and other places across the east ocean of China. The ‘Selden Map of China’ keeps the functions of a general nautical map, and perfected the nautical chart series of Ming dynasty. This paper also systematically discusses the realistic style of the navigational charts, their matching descriptions or drawings in Ming dynasty, as well as the adoptions of ‘Opposite Scenery Method’, ‘Lead Star Board’, ‘Water Compass’, ‘Astronomical Observation Instruments’, ‘Speed Detection On Board’ and other unique techniques.

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.

Coastline change detection is critical for analyzing the rise of sea levels, coastal erosion, harbor siltation, wetland ecological resources, and the offshore environment. Satellite remote sensing technology has a wide application and plays an indispensable role in coastline monitoring. The Pearl River Estuary is one of city groups with the high density population and the most developed economy in China. With the consistent increase of the reclamation and coastal zone exploitation, the coastline changes in the Pearl River Estuary are dramatic. In this paper, a set of Landsat images from 1973 to 2015 were collected to detect the coastline evolutions in the Pearl River Estuary. Firstly, the coastlines were divided into 8 categories and extracted with the aid of remote sensing and geographic information system (GIS) technologies. In addition, the spatiotemporal evolution characteristics of coastline length, categories, and spatial changes were analyzed during the study period. A coastline utilization index was proposed to determine the impact of human activities. Finally, the driving factors of coastline changes were discussed. The results are as follows: ① The total length of coastlines in the Pearl River Estuary increased by 135.46 km, which was equivalent to a growth of 3.15 km per year. The artificial coastline increased significantly, with a net increase of 315.94 km in length. The natural coastline constantly declined, with the most decrease in mud coastline. The change intensity of the coastline length showed remarkable periodicity. It was slow before 1990, peaked from 1990 to 2000, and then weakened after 2000. ② The coastline category was changed from natural coastline to artificial coastline in the study period. The natural coastline was the main coastline category before 1990, but the artificial coastline took the lead position thereafter. Among all coastline categories, the proportion of the construction coastline changed most dynamically, which increased from 7.09% in 1973 to 46.49% in 2015. ③ During the period of 1973-2015, the coastline showed a prevailing trend of advancing seaward, reaching an annual rate of 39.10 m. The seaward extension rate had significant difference in different area. The greatest extension speed appeared on the coastline between Jiti outlet and Hutiao outlet. The seaward extensions of the coastlines between Modao outlet and Jiti outlet, and between Jiao outlet and Hongqi outlet, were remarkable, too. Other regions had an advancing seaward but with a small magnitude. ④ In the past 40 years, the coastline utilization index grew stably. The growth rate increased markedly from 1973 to 1995 and changed gently after 1995. The coastline utilization index in the east coast of the Lingding Sea occupied the largest increasing extent because more and more natural coastline had been artificialized. ⑤ The coastlines in the Pearl River Estuary are affected mainly by human activities, such as outlet renovation, coastal zone construction, and sea farming. Environmental conditions, demographic and economic growth, as well as policies are important driving forces of coastline changes. This study will provide scientific support for the coastline change detection, coastal zone management and sustainable development in the coastal area.

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.

China is a country with centuries’old historical civilization. City culture is one of the most important parts in the long history of the Chinese splendid national culture.Following the prosperity of human civilization, a large amount of cities with various sizes appeared and kept growing continuously. Some cities have developed into world-famous with abundant historical culture and significant historic value, which are originated from the achievements of Chinese ancient civilization. In the present day, when cities are developing rapidly, it isparticularly important to study the evolution of cities since ancient times. One efficient way to achieve this is to study city maps which reflect the spatial pattern of cities in a relatively accurate sense. Therefore, city maps can provide great reference values for exploring the changesin city patterns from a historical view. This paper aims to study the modern city maps produced during the period from the Ming Dynasty to the Republican period. In terms of the inheritance of traditional ancient city maps and the developments encouraged by the western cartography, this paper intends to explore the characteristics, developing trends and values of Chinese modern city maps. On one hand, modern city maps had not completely got rid of the features of Chinese traditional city maps from the aspects of method and content; on the other hand, modern city maps had also been influenced and motivated by western knowledge. Therefore, Chinese modern city maps were gradually developing towards diversification, thematization and modernization during the process of inheritance and development, reflecting the trait of city development and regional features at that time. This paper also applied the case study method to explore the Chinese modern city maps with typical examples from various time and cities, and to further discuss and make comments on the representations, contents, techniques as well as significant values of these maps.

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.