Residents And Automatically Integrated Smart Study

Map is not only a graphic description of spatial information, but also an artwork. There are many creative works in a map, such as map design and cartographic generalization. When we read a map, designed and made perfectly, we can easily get the information we need, furthermore, we can feel the beauty of art. Today, the digital age, GIS is a main tool for spatial analysis. Map is the best description form of spatial information yet. Getting and updating map data quickly is the key to extending GIS' application field. Cartographic Generalization is indispensable to getting multi-scale map data, whether through remote sensing image or map scanning digitizing. Digital cartography cry for automatic cartographic generalization, Automating cartographic generalization has been the bottleneck of GIS' development.Whereas automatic cartographic generalization stands pivotal location in the informational age, it has been one hotspot of science study. But it is difficult to automate cartographic generalization because cartographic generalization is a creative work. Current study attempt to do some work, for example, Simulating optical thinking, making generalization decision and controlling generalization process by using the study fruit of AI, such as neural network, GA and Agent etc., Developing arithmetic and model for graphic and image processing and mathematical computation. Realizing AI maybe is the only way for complete automation in cartographic generalization.Considering that truth, this paper attempt to apply ANN and pattern recognition to automatic cartographic generalization, and design new intelligent algorithm based on vector-grid uniform data model , mathematical morphology and computation geometry etc.. Most of study and work had been put into map generalization for habitation. The application of paper's production enhanced efficiency and effect of automatic cartographic generalization.The main achievements in this paper are as follows:1. Developing several algorithms which can be applied to automatic map generalization and digital cartography.2. Exploring the strategy of map generalization for habitation.3. Developing an ANN model of point feature selection based on vector-grid uniform data model.4. Getting the data of streets by mathematical morphology, and aggregating districts automatically based on street net generalization.5. Simplifying building polygon by pattern recognition assisted by Neural Network and mathematical morphology.6. Experimenting with new methods of transforming feature's graphic grade automatically and settling the conflict between habitation feature and road.