The application of geographic information based on smart mobile terminals has become a development trend and has been a research focus in the field of geographic information science. With the increase of acquisition methods for geographic information, massive spatial data are continuously generated from various sources, such as smart mobile devices, UAVs, and remote sensing satellites. Currently, with the rapid development of computer hardware manufacturing industry, the performance of mobile terminals (embedded devices) has get a great improvement in computing speed, memory size and storage capacity. However, compared with spatial data volume, their computing resources and storage resources are still relatively limited, and still can not meet the demand for rapid processing of huge volume spatial data. The contradiction that the improvement rate of the performance of embedded devices can not match the growth rate of spatial data volume, also limits geographic information service quality. How to utilize the limited computing resources and storage resources of embedded devices to store, process and display massive spatial data is a very significant and urgent problem.
Multi-scale representation for spatial data can be used to extract different levels of detail information, which is an effective method to realize rapid processing and rendering for spatial data on embedded devices. And in the overall strategy of ‘Digital China’ geo-spatial framework construction, the National Natural Science Foundation of China has put forward that multi-scale representation for spatial data is one of the core issues for geo-spatial databases technologies. Although the existing research has obtained significant research achievement, there are still some problems to be solved: most multi-scale information extraction technologies for vector data only consider spatial structure features, and do not take semantic features into account; image data storage and organization methods based on commercial database or distributed file system require high hardware configuration and complex management, which is not suitable for efficient storage and management of image data on embedded devices.
In this dissertation, in order to achieve fast rendering for huge volume vector data and massive remote sensing image data on embedded devices, the key technologies for multi-scale representation and fast rendering of spatial data are studied. And a digital field investigation and verification system is developed to solve the integration problem of positioning information, digital map, and attribute information in land field survey. The main tasks of the dissertation are summarized as follows:
(1) A fast multi-scale visualization technology for feature-based vector data. The thoughts that semantic features, spatial structure features and land parcels shape features must be considered when extracting multi-scale information of land-use data, is put forward. And a multi-scale processing model for vector data is constructed by using the offline and online generalization method. Then a solution for fast multi-scale visualization of feature-based vector data on embedded devices is proposed and implemented. The experimental results show that the proposed solution can enable the embedded GIS software to support fast scheduling and viewing for at least 100MB vector data with the average rendering time of less than 2 seconds, and even in the worst case, the rendering time is less than 4 senconds.
(2) An algorithm for fast topological consistent simplification of face features based on key detection-point identification. The lack of traditional simplification algorithms for vector line is enumerated and analyzed. By studying the key detection-point identification strategy that takes spatial relationships into account, an improved DP algorithm based on key detection-point identification is proposed and implemented. The experimental results prove that the improved algorithm not only can guarantee planar topological consistent, but also can slightly reduce computational cost.
(3) A fast rendering algorithm for super-size features based on the improved G-H algorithm. Aiming at the problem that the super-size features rendering on embedded devices is quite time consuming and can cause program exception phenomenon, the experiments reveal that the GDI polygon drawing function on Windows CE and Mobile platform is lack of graphics clipping function, thus it can not select the part of features which intersect with the display area to render. After analyzing the failure problem of the existing polygon clipping algorithms in intersection degenerate cases, an improved G-H polygon clipping algorithm is proposed and implemented. Compared with the previous algorithms, the proposed algorithm can not only resolve the degenerate cases, but also has a higher time efficiency. Then, an improved rendering strategy for vector data based on graphic clipping is put forward to accelerate the viewing speed. The experimental results show that compared with the original method, the improved method can boost rendering efficiency to more than 85% for super-size features, and even more than 94.5% when the display scale exceeds a certain value. The rendering time can even be reduced from tens of seconds to less than a second. And the entire map rendering efficiency can be increased at least 88%, achieving fluently display for vector map in the roaming, zooming process.
(4) A LOD adaptive representation and fast visualization technology for massive image data. In order to resolve the conflict between the limited resources of embedded devices and the growing amount of massive image data to be shown, a solution for fast image data rendering on embedded devices is proposed and implemented. First, an improved algorithm for massive image pyramid construction is put forward, and a storage and organization strategy for tile data is designed. Then, a method, adopting technologies such as view-dependent levels of detail, target-tiles quick search and tiles seamless connection, is presented for fast scheduling and viewing of images. After that, to overcome the problem that embedded GIS software can not process images with more than 3GB, which is caused by the limitation of the memory card of embedded device, an improved adaptive solution for LOD processing and rendering of images is put forward. The experimental results show that the improved solution can enable the embedded GIS software to support fast scheduling and viewing for at least 50GB image data with the rendering time of less than a second, and the rendering speed does not depend on the image size.
(5) Development and application of digital field investigation and verification system. Based on the above research results, a digital field investigation and verification system based on smart mobile devices with independent intellectual property rights is developed, which can provide technical support for quickly obtaining accurate geographic information data. The system design scheme is expounded from the aspects of system architecture, frame structure, function module division and overall work flow. Some design patterns, such as strategy, command and abstract factory, are adopted for improving the reusability, extensibility and maintainability of the embedded GIS framework.
The system has been successfully applied to all kinds of land surveying business andication. can provide an important reference for the formulation of relevant investigation scheme and standards in our country. The practical application shows that the survey efficiency of the new method using the system is 2-3 times higher than that of the traditional method. It has become a digital and dynamic geographical information infrastructure, and will result in good economic and social benefits with its promotion and appl