Search

The simplification operation has always been a challenging research problem in the field of cartography generalization and expression. Continuous tessellation area data of land cover, whose source data come from high-resolution remote sensing satellite sensors, are an important type of remote sensing application data to express the spatial distribution information of natural and artificial objects on Earth's surface. In this paper, a novel and practical simplification method for continuous tessellation area data is proposed. The first step is area discretization, in which a general topological structure containing semantic features is established, and land patches are divided into multiple arcs on the basis of this topological structure. The second step is arc simplification, in which arcs are classified into artificial and natural types according to their semantic and morphological features, and corresponding simplification algorithms are assigned. The final step is area reconstruction, based on the semantic and topological information, and simplified arcs are reconstructed to patch polygons. The geography and national conditions census data of a city in Guizhou Province, China, are used to verify the rationality and effectiveness of the method. Experimental results showed that the method proposed in this article is suitable for the simplification processing of artificial and natural land features. The simplification results can maintain the overall shape of the patches and reduce the complexity of the arcs effectively, and the semantic information of each patch after simplification is also correct. [ABSTRACT FROM AUTHOR]

Landslide triggered by earthquake or rainstorm often results in serious property damage and human casualties. It is, therefore, necessary to establish an emergency management system to facilitate the processes of damage assessment and decision-making. This paper has presented an integrated approach for mapping and analyzing spatial features of a landslide from remote sensing images and Digital Elevation Models (DEMs). Several image interpretation tools have been provided for analyzing the spatial distribution and characteristics of the landslide on different dimensions: (1D) terrain variation analysis along the mass movement direction and (3D) morphological analysis. In addition, the results of image interpretation can be further discussed and adjusted on an online cooperating platform, which was built to improve the coordination of all players involved in different phases of emergency management, e.g., hazard experts, emergency managers, and first response organizations. A mobile-based application has also been developed to enhance the data exchange and on-site investigation. Our pilot study of Guanling landslide shows that the presented approach has the potential to facilitate the phases of landslide monitoring and information management, e.g., hazard assessment, emergency preparedness, planning mitigation, and response. [ABSTRACT FROM AUTHOR]