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With the urgent demand on urban synthetic aperture radar (SAR) image interpretation, this article deals with detecting buildings from a single high-resolution SAR image. Based on our previous work in building detection from SAR images, aiming at extracting buildings with their whole and accurate boundaries from the built-up area, a general framework using the marker-controlled watershed transform is introduced to combine both building characteristics and contextual information. First, the characteristics of the buildings and their surroundings are extracted as markers by the target detection techniques. Second, the edge strength image of the SAR image is computed using the ratio of exponentially weighted averages detector. The marker-controlled watershed transform is implemented with the markers and the edge strength image to segment buildings from the background. Finally, to remove false alarms, building features are considered. Especially, a shape analysis method, called direction correlation analysis, is designed to keep linear or L-shaped objects. We apply the proposed method to highresolution SAR images of different scenes and the results validate that the new method is effective with high detection rate, low false-alarm rate, and good localization performance. Furthermore, comparison between the new method and our previous method reveals that introducing contextual information plays an important role in improve building detection performance.

Over the past several years, volunteered geographic information (VGI) has expanded rapidly. VGI collection has been proven to serve as a highly successful means of acquiring timely and detailed global spatial data. However, VGI includes several special properties. For example, the contributor's reputation affects the quality of objects edited, and a geographic object may have multiple versions. The existing spatio-temporal data model cannot describe the unique properties of VGI. Therefore, a spatio-temporal VGI model considering trust-related information is presented in this paper. In this model, central elements of the VGI environment, e.g., geographic entity, entity state, state version, contributor, reputation, geographic event, and edit event, and their interaction mechanisms are analysed. Major VGI objects and relations are determined using the object-oriented method and trust-related operations, and their relationships are analysed, and nine linkage rules among trust-related operations are found to maintain the consistency of corresponding data. A prototype system for the spatio-temporal VGI model is presented, and the effectiveness of the model is verified.

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Spatial data are fundamental for borderland analyses of geography, natural resources, demography, politics, economy, and culture. As the spatial data used in borderland research usually cover the borderland regions of several neighboring countries, it is difficult for anyone research institution of government to collect them. Volunteered Geographic Information (VGI) is a highly successful method for acquiring timely and detailed global spatial data at a very low cost. Therefore, VGI is a reasonable source of borderland spatial data. OpenStreetMap (OSM) is known as the most successful VGI resource. However, OSM's data model is far different from the traditional geographic information model. Thus, the OSM data must be converted in the scientist's customized data model. Because the real world changes rapidly, the converted data must be updated incrementally. Therefore, this paper presents a method used to dynamically integrate OSM data into the borderland database. In this method, a basic transformation rule base is formed by comparing the OSM Map Feature description document and the destination model definitions. Using the basic rules, the main features can be automatically converted to the destination model. A human-computer interaction model transformation and a rule/automatic-remember mechanism are developed to interactively transfer the unusual features that cannot be transferred by the basic rules to the target model and to remember the reusable rules automatically. To keep the borderland database current, the global OsmChange daily diff file is used to extract the change-only information for the research region. To extract the changed objects in the region under study, the relationship between the changed object and the research region is analyzed considering the evolution of the OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 1708 involved objects. In addition, five rules are determined to select the objects and integrate the changed objects with multi-versions over time. The objects' change-type evolution is analyzed, and seven rules are used to determine the change-type of the changed objects. Based on these rules and algorithms, we programmed an automatic (or semi-automatic) integrating and updating prototype system for the borderland database. The developed system was intensively tested using OSM data for Vietnam and Pakistan as the experimental data.