Abstract. In many famous tourist cities, there is a lack of big data analysis and perception of tourist behavior, which reflected in the existence of a large number of basic data in the scenic area. Through the traditional and/or non-special sensors of the Internet of Things, a large amount of special -temporal change data is collected, including video monitors data, RFID, WIFI, temperature and humidity, water depth sensors and other big data of the Internet of Things(IoT)that can perceive the location and environmental resource information of tourists. In addition, the thermodynamic data of the distribution location of tourists' mobile phones, although these data include the behavior data of tourist groups and individuals in the scenic spot, which can be most shown by supplying, lack of in-depth analysis and intelligent service application.As we all know, Guilin, Guangxi is a famous tourist attraction in the word. In this tourism city, the big data processing methods of location service include establishing a data processing framework, information extraction, fusion and batch processing technology. As a result of the location data of tourists in the scenic spot are constantly created over time, the location service data will be processed by stream mode. At the same time, dimension reduction analysis of big data is an important link in processing as it has a large volume but a low-value density. The analysis methods of location service include kernel density analysis, GIS spatial and temporal analysis, artificial intelligence deep learning, two-dimensional mapping, visualization processing results, and extraction of hot spots or scenic spots, etc. Spatial and temporal index technology is used to manage the big data of scenic spot location service and to improve the efficiency of data query and access. At the same time, a reasonable predictive analysis of data processing results is an important research method.The main system structure of an intelligent service platform includes platform, data processing sing, and analysis end, mobile management end. The platform includes a general user service module, location service module, a communication module, etc, which is responsible for providing reasonable service to customers. The data processing and analysis end include a data receiving module and a data processing and analysis module. Responsible for receiving and processing the location service data returned by the client. The mobile management end includes the scenic spot management module. Responsible for providing the manager with the distribution of tourists in the scenic area, the location information of the staff, the location of the emergency situation and other contents, and providing great help for the manager to maintain the order of the scenic area, reasonably dispatch personnel, and launch rescue in time. Development and design to show users good models and development concepts and typical artificial intelligence products, to improve the scenic spot tourist's playing efficiency and humanized experience and reduce the management cost.

Abstract. In recent years, Tourism has become more and more Chinese leisure travel choice The research on the smart scenic spot is getting deeper and deeper, but the problem of accurate location l in the natural scenic spot still needs to be solved. Semantic maps contain a wealth of environmental information and can be more efficient for location-aware services, and are attracting more and more attention from researchers at home and abroad. In order to better ensure the travel experience of tourists, the range of scenic spots is too large, and the signal interference is high. Complex terrain in the scenic area, Branch and leaf features Visitors cannot rely on traditional positioning systems to get their current accurate location. It is proposed to construct a navigation semantic map for the perception of scenic space. In the construction process, the operation based on the location perception of the tourists and the surrounding environment and the extraction of the feature information is the key to constructing the semantic map. The general image recognition method is used to obtain the environment image information, and the acquired feature image is recognized to obtain the semantic information in the environment; in order to obtain more feature environment information to better complete the location-aware service task, the GBP descriptor is used. The method divides and stores different semantic regions in the environment, and generates a semantic map with rich semantic information and feature information according to the three-dimensional map model.

Abstract. Nowadays, 3D navigation systems and intelligent services are costly to use, and the accuracy of intelligent navigation does not meet the needs of tourists, and the practicality is not strong. Based on the current situation of the construction of the smart scenic navigation system and the actual needs of tourists, this study determines the main principles and methods of the tour guide system and intelligent services. Taking a certain scenic spot as the specific research object, the A* algorithm is used to solve the path. By optimizing the online gracing service of the arc gis server network, the appropriate data interpolation method is used to improve the accuracy of the elevation data of the scenic spot, and the characteristics of the three-dimensional symbol system are combined. The 3D symbol design system is optimized, and a high-precision 3D navigation map model is established. Finally, the corresponding 3D tour guide system and scenic area intelligent service are designed based on this, and the principle and application flow of the intelligent scenic area navigation system construction are summarized. The three-dimensional map model and its navigation system designed by this research institute reduce the risk of tourists during the tour, improve the quality and efficiency of the tour, and provide a practical service optimization solution for the smart scenic three-dimensional navigation system. The construction and use cost of the high-precision three-dimensional navigation system in the scenic spot is reduced.

Abstract. China's urban illegal buildings are emerging in an endless stream with a large number. There is a wide demand for urban illegal buildings monitoring in urban management departments, including Beijing, Shanghai, Guangzhou and other regions where urban management is facing increasingly serious problems of illegal buildings. It is urgent to solve the common problem of "urban disease" caused by urban illegal buildings, and a new automatic monitoring method that can reduce the cost of urban management is urgently needed. This kind of automated monitoring method of illegal buildings has a wide market demand in Urban Management and Law Enforcement and the Ministry of Land and Resources. Existing technologies cannot realize long-term, autonomous, rapid and intelligent dynamic real-time monitoring of urban illegal buildings, which leads to the problem that illegal buildings’ behaviors cannot be stopped in time. There is a possibility to solve these problems using the ubiquitous network of base stations in cities to monitor illegal buildings.This paper proposes a dynamic monitoring method of illegal buildings using spatiotemporal big data based on urban high lying zones. Through the Spatiotemporal sensor network technology, the tilt-type stereo camera is set up at the high lying zones around the survey area. According to the real-time ambient temperature and humidity numerical data fed back by the temperature and humidity sensor, the tilt-type stereo camera uses intelligent time-lapse photogrammetry technology to obtain multiple stereo pairs. The tilted remote sensor transmits the multiple stereo pairs to the urban dynamic analysis service network using wireless transmission. The urban dynamic analysis service network will complete a series of analysis and processing operations without any human intervention, and then transmit the results of the analysis to the early-warning terminal successively through the base station, WIFI and other wireless transmission methods. Finally, the illegal building data is uploaded to the terminal. That is to say, the urban dynamic analysis service network can realize the intelligent, automatic analysis and processing of spatial analysis server and the operation of calling all database data and storing data.The method proposed in this paper uses the high lying zones around the survey area to expand the monitoring range, improve the accuracy of monitoring data, realize long-term real-time monitoring, and fully utilize the characteristics of Spatiotemporal sensing network technology intelligent, autonomous, wireless transmission, etc., significantly reducing labor. It greatly shortens the process from the emergence to the discovery of illegal buildings in cities. The workload of monitoring has improved the efficiency of dynamic monitoring and warehousing of illegal buildings data in cities.

Abstract. With the rapid development of market economy and the continuous improvement of urbanization process in China, housing construction in almost all areas of densely populated cities has shown explosive growth. The existence of illegal construction, to a certain extent, not only causes the waste of land and resources, but also leads to the increase of the cost of the development of affordable housing, but also increases the security risks. It is urgent to solve the common problem of “urban disease” in violation of construction, but the conventional means of monitoring illegal construction mainly rely on on on-site inspection by law enforcement departments and mass reporting. Due to the limited inspection power and time, there are inevitably omissions. At the same time, there are difficulties in obtaining evidence in violation of construction investigation. Therefore, a new type of monitoring method is urgently needed. There is a wide market demand in China's urban management departments and land and resources departments for automated monitoring methods to reduce the cost of urban management. In this paper, urban spatial geographic information is acquired by means of remote sensing change detection, and compared with urban construction land planning approval data, including illegal matching recognition algorithm. Based on the technology of automatic urban change detection of grid image blocks, an efficient algorithm for building change detection is proposed. Establish a threshold recognition and accuracy test algorithm of urban building construction progress model parameters, and obtain information of illegal building construction progress and area based on grid image blocks. Artificial Intelligence (AI) is used to identify and extract buildings from satellite remote sensing images in different time periods. The dynamic change information of the research area is reflected by multi-source and large data integration technology of satellite and UAV remote sensing. Several optical image sample sets and test sets are established. The convolution neural network model is designed by sample sets. The accuracy and sensitivity of illegal identification can be improved by the combination of AI and in-depth learning. Using the method of monitoring, analyzing and comparing the big data of urban construction to monitor the illegal buildings in cities is not only fast and efficient, but also provides a scientific and objective basis for the relevant departments to enforce the law.