Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services

One of the most widely-implemented service standards provided by the Open Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS). WMS is widely employed globally, but there is limited knowledge of the global distribution, adoption status or the service quality of these online WMS resources. To fill this void, we investigated global WMSs resources and performed distributed performance monitoring of these services. This paper explicates a distributed monitoring framework that was used to monitor 46,296 WMSs continuously for over one year and a crawling method to discover these WMSs. We analyzed server locations, provider types, themes, the spatiotemporal coverage of map layers and the service versions for 41,703 valid WMSs. Furthermore, we appraised the stability and performance of basic operations for 1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major reasons for request errors and performance issues, as well as the relationship between service response times and the spatiotemporal distribution of client monitoring sites. This paper will help service providers, end users and developers of standards to grasp the status of global WMS resources, as well as to understand the adoption status of OGC standards. The conclusions drawn in this paper can benefit geospatial resource discovery, service performance evaluation and guide service performance improvements.Comment: 24 pages; 15 figure

BUILDING BOUNDARY EXTRACTION FROM LIDAR DATA USING A LOCAL ESTIMATED PARAMETER FOR ALPHA SHAPE ALGORITHM

The α-shape algorithm is a very common option to extract building boundaries from LiDAR data. This algorithm is normally executed in 2D space considering a parameter α as a binary classifier which controls the distinctiveness of points whether or not they belong to the object boundary. For point cloud data, this parameter is directly related to the local point density and the level of detail of building boundaries. Studies that have explored this concept usually consider a unique parameter α to extract all buildings in the dataset. However, the point density can have a considerable variation along the point cloud and, in this case, the use a global parameter may not be the best choice. Alternatively, this paper proposes a data-driven method that estimates a local parameter for each building. The method evaluation considered six test areas with different levels of complexity, selected from a LiDAR dataset acquired over the city of Presidente Prudente/Brazil. From the qualitative and quantitative analysis, it could be seen that the proposed method generated better results than when a global parameter is used. The proposed method was also able to withstand density variation among the LiDAR data, having a positional accuracy around 0.22 m, against 0.40 m of global parameter