A framework for utility data integration in the UK

In this paper we investigate various factors which prevent utility knowledge from being fully exploited and suggest that integration techniques can be applied to improve the quality of utility records. The paper suggests a framework which supports knowledge and data integration. The framework supports utility integration at two levels: the schema and data level. Schema level integration ensures that a single, integrated geospatial data set is available for utility enquiries. Data level integration improves utility data quality by reducing inconsistency, duplication and conflicts. Moreover, the framework is designed to preserve autonomy and distribution of utility data. The ultimate aim of the research is to produce an integrated representation of underground utility infrastructure in order to gain more accurate knowledge of the buried services. It is hoped that this approach will enable us to understand various problems associated with utility data, and to suggest some potential techniques for resolving them

Map Conflation using Piecewise Linear Rubber-Sheeting Transformation between Layout and As-Built Plans in Kumasi Metropolis.

Context and backgroundAccurately integrating different geospatial data sets remain a challenging task because diverse geospatial data may have different accuracy levels and formats. Surveyors may typically create several arbitrary coordinate systems at local scales, which could lead to a variety of coordinate datasets causing such data to remain unconsolidated and in-homogeneous.Methodology:In this study, a piecewise rubber-sheeting conflation or geometric correction approach is used to accomplish transformations between such a pair of data for accurate data integration. Rubber-sheeting or piecewise linear homeomorphism is necessary because the different plans’ data would rarely match up correctly due to various reasons, such as the method of setting out from the design to the ground situation, and/or the non-accommodation of existing developments in the design.  Results:The conflation in ArcGIS using rubber sheet transformation achieved integration to a mean displacement error of 1.58 feet (0.48 meters.) from an initial mean displacement error of 71.46 feet (21.78 meters) an improvement of almost 98%. It is recommended that the rubber sheet technique gave a near exact point matching transformation and could be used to integrate zone plans with As-built surveys to address the challenges in correcting zonal plans in land records.  It is further recommended to investigate the incorporation of the use of textual information recognition and address geocoding to enable the use of on-site road names and plot numbers to detect points for matching

Partial masquerading and background matching in two Asian box turtle species (<i>Cuora</i> spp.)

Animals living in heterogeneous natural environments adopt different camouflage strategies against different backgrounds, and behavioral adaptation is crucial for their survival. However, studies of camouflage strategies have not always quantified the effect of multiple strategies used together. In the present study, we used a human visual model to quantify similarities in color and shape between the carapace patterns of two Cuora species and their preferred habitats. Our results showed that the color of the middle stripe on the carapace of Cuora galbinifrons (Indochinese box turtle) was significantly similar to the color of their preferred substrates. Meanwhile, the middle stripe on the carapace of C. mouhotii (keeled box turtle) contrasted more with their preferred substrates, and the side stripe matched most closely with the environment. Furthermore, the carapace side stripe of C. galbinifrons and the carapace middle stripe of C. mouhotii highly contrasted with their preferred substrates. We quantified the similarity in shape between the high-contrast stripes of both Cuora species and leaves from their habitats. The carapace middle stripe of C. mouhotii was most similar in shape to leaves from the broad-leaves substrate, and the carapace side stripe of C. galbinifrons was the most similar in shape to leaves from the bamboo-leaves substrate. We determined that these species adopt partial masquerading when their entire carapace is exposed and partially match their background when they semi-cover themselves in leaf litter. To the best of our knowledge, this is the first study to demonstrate that partial masquerading and background matching improve the camouflage effect of Asian box turtles in their preferred habitats. This is a novel study focusing on the influence of the shape and color of individual carapace segments on reducing detectability and recognition.</p

A Geospatial Cyberinfrastructure for Urban Economic Analysis and Spatial Decision-Making

abstract: Urban economic modeling and effective spatial planning are critical tools towards achieving urban sustainability. However, in practice, many technical obstacles, such as information islands, poor documentation of data and lack of software platforms to facilitate virtual collaboration, are challenging the effectiveness of decision-making processes. In this paper, we report on our efforts to design and develop a geospatial cyberinfrastructure (GCI) for urban economic analysis and simulation. This GCI provides an operational graphic user interface, built upon a service-oriented architecture to allow (1) widespread sharing and seamless integration of distributed geospatial data; (2) an effective way to address the uncertainty and positional errors encountered in fusing data from diverse sources; (3) the decomposition of complex planning questions into atomic spatial analysis tasks and the generation of a web service chain to tackle such complex problems; and (4) capturing and representing provenance of geospatial data to trace its flow in the modeling task. The Greater Los Angeles Region serves as the test bed. We expect this work to contribute to effective spatial policy analysis and decision-making through the adoption of advanced GCI and to broaden the application coverage of GCI to include urban economic simulations

Semantic and geometric enrichment of 3D geo-spatial models with captioned photos and labelled illustrations

There are many 3D digital models of buildings with cultural heritage interest, but most of them lack semantic annotation that could be used to inform users of mobile and desktop applications about their origins and architectural features. We describe methods in an ongoing project for enriching 3D models with generic annotation, derived from examples of images of building components and from labelled plans and diagrams, and with object-specific descriptions obtained from photo captions. This is the first stage of research that aims to annotate 3D models with facts extracted from the text of authoritative architectural guides

A hidden Markov model for matching spatial networks

Datasets of the same geographic space at different scales and temporalities are increasingly abundant, paving the way for new scientific research. These datasets require data integration, which implies linking homologous entities in a process called data matching that remains a challenging task, despite a quite substantial literature, because of data imperfections and heterogeneities. In this paper, we present an approach for matching spatial networks based on a hidden Markov model (HMM) that takes full benefit of the underlying topology of networks. The approach is assessed using four heterogeneous datasets (streets, roads, railway, and hydrographic networks), showing that the HMM algorithm is robust in regards to data heterogeneities and imperfections (geometric discrepancies and differences in level of details) and adaptable to match any type of spatial networks. It also has the advantage of requiring no mandatory parameters, as proven by a sensitivity exploration, except a distance threshold that filters potential matching candidates in order to speed-up the process. Finally, a comparison with a commonly cited approach highlights good matching accuracy and completeness

Reconstructing historical 3D city models

Historical maps are increasingly used for studying how cities have evolved over time, and their applications are multiple: understanding past outbreaks, urban morphology, economy, etc. However, these maps are usually scans of older paper maps, and they are therefore restricted to two dimensions. We investigate in this paper how historical maps can be ‘augmented’ with the third dimension so that buildings have heights, volumes, and roof shapes. The resulting 3D city models, also known as digital twins, have several benefits in practice since it is known that some spatial analyses are only possible in 3D: visibility studies, wind flow analyses, population estimation, etc. At this moment, reconstructing historical models is (mostly) a manual and very time-consuming operation, and it is plagued by inaccuracies in the 2D maps. In this paper, we present a new methodology to reconstruct 3D buildings from historical maps, we developed it with the aim of automating the process as much as possible, and we discuss the engineering decisions we made when implementing it. Our methodology uses extra datasets for height extraction, reuses the 3D models of buildings that still exist, and infers other buildings with procedural modelling. We have implemented and tested our methodology with real-world historical maps of European cities for different times between 1700 and 2000