Automatic, Multiview, Coplanar Extraction for CityGML Building Model Texture Mapping

Most 3D CityGML building models in street-view maps (e.g., Google, Baidu) lack texture information, which is generally used to reconstruct real-scene 3D models by photogrammetric techniques, such as unmanned aerial vehicle (UAV) mapping. However, due to its simplified building model and inaccurate location information, the commonly used photogrammetric method using a single data source cannot satisfy the requirement of texture mapping for the CityGML building model. Furthermore, a single data source usually suffers from several problems, such as object occlusion. We proposed a novel approach to achieve CityGML building model texture mapping by multiview coplanar extraction from UAV remotely sensed or terrestrial images to alleviate these problems. We utilized a deep convolutional neural network to filter out object occlusion (e.g., pedestrians, vehicles, and trees) and obtain building-texture distribution. Point-line-based features are extracted to characterize multiview coplanar textures in 2D space under the constraint of a homography matrix, and geometric topology is subsequently conducted to optimize the boundary of textures by using a strategy combining Hough-transform and iterative least-squares methods. Experimental results show that the proposed approach enables texture mapping for building façades to use 2D terrestrial images without the requirement of exterior orientation information; that is, different from the photogrammetric method, a collinear equation is not an essential part to capture texture information. In addition, the proposed approach can significantly eliminate blurred and distorted textures of building models, so it is suitable for automatic and rapid texture updates.

Target integrity assessment based on image and track information

The rapid and accurate integrity assessment of targets can provide important guarantee and reference for the subsequent decision of the implementer. The current research on target integrity assessment mainly adopts single data source or complex probability model, which leads to inability to balance the needs of accuracy and real-time. In order to solve this problem, a new target integrity assessment method based on image and track information is proposed in this paper. Image texture, corner points and track parameters before and after the execution of the target are used to transform the integrity assessment issue into a classification issue, and a comprehensive assessment result is made by combining various classification results. The experimental results show that the assessment results based on both image and track information reached 97.5%, higher than the evaluation results from a single data source, and the evaluation time was controlled in milliseconds, which not only improved the accuracy rate but also ensured the real-time assessment.

Event detection of different English data sources based on transfer learning

The methods of multi-modal English event detection under a single data source and isomorphic event detection of different English data sources based on transfer learning still need to be improved. In order to improve the efficiency of English and data source time detection, based on the transfer learning algorithm, this paper proposes multi-modal event detection under a single data source and isomorphic event detection based on transfer learning for different data sources. Moreover, by stacking multiple classification models, this paper makes each feature merge with each other, and conducts confrontation training through the difference between the two classifiers to further make the distribution of different source data similar. In addition, in order to verify the algorithm proposed in this paper, a multi-source English event detection data set is collected through a data collection method. Finally, this paper uses the data set to verify the method proposed in this paper and compare it with the current most mainstream transfer learning methods. Through experimental analysis, convergence analysis, visual analysis and parameter evaluation, the effectiveness of the algorithm proposed in this paper is demonstrated.

Corporate crime in Australia: the extent of the problem

Government and public concern about corporate wrongdoing in Australia is arguably at an all-time high. However, the extent and nature of corporate crime is largely unknown; it is concealed by regulatory agency reporting practices and the absence of a single data source which combines data across all regulators. This study addresses the problem by examining corporate offending by 33 of the country’s top companies, and their wholly owned subsidiaries, over a five-year period. The results indicate that corporate offending is patterned and unevenly distributed across the business community. Drawing on the findings and on their experience of conducting the research, the authors make recommendations for improving policy and practice, including the establishment of a national database of corporate offending.

Efficient Capacity-Constrained Multicast in RC-Based P2P Networks

Application level multicast is independent of router infrastructure unlike router-based IP multicast. The existing DHT-based application level multicast protocols work efficiently as long as there is almost no churn; otherwise, their performances start degrading drastically, because DHT – based architecture cannot handle churn effectively. Besides, most of DHT-based multicast protocols consider single data source and do not consider peer heterogeneity. In this work, we have considered an existing non-DHT based P2P architecture, viz., Residue Class based (RC-based) architecture which has already been shown to perform much better than some well-known DHT-based architectures from the viewpoints of speed of unicast communication and churn handling. We have presented a highly efficient capacity-constrained and any source multicast protocol suitable for the RC-based P2P architecture as mentioned above.

MASHUP SCHEME DESIGN OF MAP TILES USING LIGHTWEIGHT OPEN SOURCE WEBGIS PLATFORM

To address the difficulty involved when using existing commercial Geographic Information System platforms to integrate multi-source image data fusion, this research proposes the loading of multi-source local tile data based on CesiumJS and examines the tile data organization mechanisms and spatial reference differences of the CesiumJS platform, as well as various tile data sources, such as Google maps, Map World, and Bing maps. Two types of tile data loading schemes have been designed for the mashup of tiles, the single data source loading scheme and the multi-data source loading scheme. The multi-sources of digital map tiles used in this paper cover two different but mainstream spatial references, the WGS84 coordinate system and the Web Mercator coordinate system. According to the experimental results, the single data source loading scheme and the multi-data source loading scheme with the same spatial coordinate system showed favorable visualization effects; however, the multi-data source loading scheme was prone to lead to tile image deformation when loading multi-source tile data with different spatial references. The resulting method provides a low cost and highly flexible solution for small and medium-scale GIS programs and has a certain potential for practical application values. The problem of deformation during the transition of different spatial references is an important topic for further research.

We Know the Yin—But Where Is the Yang? Toward a Balanced Approach on Common Source Bias in Public Administration Scholarship

Surveys have long been a dominant instrument for data collection in public administration. However, it has become widely accepted in the last decade that the usage of a self-reported instrument to measure both the independent and dependent variables results in common source bias (CSB). In turn, CSB is argued to inflate correlations between variables, resulting in biased findings. Subsequently, a narrow blinkered approach on the usage of surveys as single data source has emerged. In this article, we argue that this approach has resulted in an unbalanced perspective on CSB. We argue that claims on CSB are exaggerated, draw upon selective evidence, and project what should be tentative inferences as certainty over large domains of inquiry. We also discuss the perceptual nature of some variables and measurement validity concerns in using archival data. In conclusion, we present a flowchart that public administration scholars can use to analyze CSB concerns.

Comparing interview and trade data in assessing changes in the seahorse Hippocampus spp. trade following CITES listing

Concerns regarding the sustainability of the seahorse Hippocampus spp. trade led to their listing on CITES Appendix II in 2002, with implementation in 2004. In 2007 we interviewed wholesale traders of seahorses in Hong Kong, China, seeking indications of the effects of the CITES listing on the seahorse trade. We cross-validated traders’ perspectives with government trade statistics (1998–2007) from Hong Kong and Taiwan. We also compared these data with trade statistics for pipefish, which are related species with similar medicinal uses but are not CITES-listed. Both the interviews and government statistics indicated reduced volumes of seahorses traded through Hong Kong, changes in source countries, and price increases post-implementation. Traders suggested that these changes were largely a result of the CITES listing. However, data indicate that other factors such as shifts in domestic policies and local demand may also have affected the trade. By cross-validating the perspectives of local stakeholders with trade statistics in a wildlife trading hub we were able to explore hypotheses on the local and global impacts of CITES. Such approaches are especially important for CITES-listed species because often there is no single data source that is complete and wholly reliable.