GEOINTERPRET: AN ONTOLOGICAL ENGINEERING METHODOLOGY FOR AUTOMATED INTERPRETATION OF GEOSPATIAL QUERIES

Despite advances in GIS technology, solving geospatial problems using current GIS platforms involves complex tasks requiring specialized skills and knowledge that are attainable through formal training and experience in implementing GIS projects. These requisite skills and knowledge include: understanding domain-specific geospatial problems; understanding GIS representation of real-world objects, concepts, and activities; knowing how to identify, locate, retrieve, and integrate geospatial data sets into GIS projects; knowing specific geoprocessing capabilities available on specific GIS platforms; and skills in utilizing geoprocessing tools in GIS with appropriate data sets to solve problems effectively and efficiently. Users interested in solving application-domain problems often lack such skills and knowledge and resort to GIS experts (this is especially true for applications dealing with diverse geospatial data sets and complex problems). Therefore, there is a gap between users' knowledge about geoprocessing and GIS tools and the GIS knowledge and skills needed to solve geospatial problems. To fill this gap, a new approach that automates the tasks involved in geospatial problem solving is needed. Of these tasks, the most important is geospatial query (usually expressed in application-specific concepts and terminologies) interpretation and mapping to geoprocessing operations implementable by GIS. The goal of this research is to develop an ontological engineering methodology, called GeoInterpret, to automate the task of geospatial query interpretation and mapping. This methodology encompasses: a conceptualization of geospatial queries; a multiple-ontology approach for representing knowledge needed to solve geospatial queries; a set of techniques for mapping elements between different ontologies; and a set of algorithms for geospatial query interpretation, mapping, and geoprocessing workflow composition. A proof of concept was developed to demonstrate the working of GeoInterpret

Characterizing degradation gradients through land cover change analysis in rural Eastern Cape, South Africa

CITATION: Munch, Z., et al. 2017. Characterizing degradation gradients through land cover change analysis in rural Eastern Cape, South Africa. Geosciences, 7(1):7, doi:10.3390/geosciences7010007.The original publication is available at http://www.mdpi.comLand cover change analysis was performed for three catchments in the rural Eastern Cape, South Africa, for two time steps (2000 and 2014), to characterize landscape conversion trajectories for sustained landscape health. Land cover maps were derived: (1) from existing data (2000); and (2) through object-based image analysis (2014) of Landsat 8 imagery. Land cover change analysis was facilitated using land cover labels developed to identify landscape change trajectories. Land cover labels assigned to each intersection of the land cover maps at the two time steps provide a thematic representation of the spatial distribution of change. While land use patterns are characterized by high persistence (77%), the expansion of urban areas and agriculture has occurred predominantly at the expense of grassland. The persistence and intensification of natural or invaded wooded areas were identified as a degradation gradient within the landscape, which amounted to almost 10% of the study area. The challenge remains to determine significant signals in the landscape that are not artefacts of error in the underlying input data or scale of analysis. Systematic change analysis and accurate uncertainty reporting can potentially address these issues to produce authentic output for further modelling.http://www.mdpi.com/2076-3263/7/1/7Publisher's versio

A conceptual framework and a risk management approach for interoperability between geospatial datacubes

De nos jours, nous observons un intérêt grandissant pour les bases de données géospatiales multidimensionnelles. Ces bases de données sont développées pour faciliter la prise de décisions stratégiques des organisations, et plus spécifiquement lorsqu’il s’agit de données de différentes époques et de différents niveaux de granularité. Cependant, les utilisateurs peuvent avoir besoin d’utiliser plusieurs bases de données géospatiales multidimensionnelles. Ces bases de données peuvent être sémantiquement hétérogènes et caractérisées par différent degrés de pertinence par rapport au contexte d’utilisation. Résoudre les problèmes sémantiques liés à l’hétérogénéité et à la différence de pertinence d’une manière transparente aux utilisateurs a été l’objectif principal de l’interopérabilité au cours des quinze dernières années. Dans ce contexte, différentes solutions ont été proposées pour traiter l’interopérabilité. Cependant, ces solutions ont adopté une approche non systématique. De plus, aucune solution pour résoudre des problèmes sémantiques spécifiques liés à l’interopérabilité entre les bases de données géospatiales multidimensionnelles n’a été trouvée. Dans cette thèse, nous supposons qu’il est possible de définir une approche qui traite ces problèmes sémantiques pour assurer l’interopérabilité entre les bases de données géospatiales multidimensionnelles. Ainsi, nous définissons tout d’abord l’interopérabilité entre ces bases de données. Ensuite, nous définissons et classifions les problèmes d’hétérogénéité sémantique qui peuvent se produire au cours d’une telle interopérabilité de différentes bases de données géospatiales multidimensionnelles. Afin de résoudre ces problèmes d’hétérogénéité sémantique, nous proposons un cadre conceptuel qui se base sur la communication humaine. Dans ce cadre, une communication s’établit entre deux agents système représentant les bases de données géospatiales multidimensionnelles impliquées dans un processus d’interopérabilité. Cette communication vise à échanger de l’information sur le contenu de ces bases. Ensuite, dans l’intention d’aider les agents à prendre des décisions appropriées au cours du processus d’interopérabilité, nous évaluons un ensemble d’indicateurs de la qualité externe (fitness-for-use) des schémas et du contexte de production (ex., les métadonnées). Finalement, nous mettons en œuvre l’approche afin de montrer sa faisabilité.Today, we observe wide use of geospatial databases that are implemented in many forms (e.g., transactional centralized systems, distributed databases, multidimensional datacubes). Among those possibilities, the multidimensional datacube is more appropriate to support interactive analysis and to guide the organization’s strategic decisions, especially when different epochs and levels of information granularity are involved. However, one may need to use several geospatial multidimensional datacubes which may be semantically heterogeneous and having different degrees of appropriateness to the context of use. Overcoming the semantic problems related to the semantic heterogeneity and to the difference in the appropriateness to the context of use in a manner that is transparent to users has been the principal aim of interoperability for the last fifteen years. However, in spite of successful initiatives, today's solutions have evolved in a non systematic way. Moreover, no solution has been found to address specific semantic problems related to interoperability between geospatial datacubes. In this thesis, we suppose that it is possible to define an approach that addresses these semantic problems to support interoperability between geospatial datacubes. For that, we first describe interoperability between geospatial datacubes. Then, we define and categorize the semantic heterogeneity problems that may occur during the interoperability process of different geospatial datacubes. In order to resolve semantic heterogeneity between geospatial datacubes, we propose a conceptual framework that is essentially based on human communication. In this framework, software agents representing geospatial datacubes involved in the interoperability process communicate together. Such communication aims at exchanging information about the content of geospatial datacubes. Then, in order to help agents to make appropriate decisions during the interoperability process, we evaluate a set of indicators of the external quality (fitness-for-use) of geospatial datacube schemas and of production context (e.g., metadata). Finally, we implement the proposed approach to show its feasibility

Across Space and Time. Papers from the 41st Conference on Computer Applications and Quantitative Methods in Archaeology, Perth, 25-28 March 2013

This volume presents a selection of the best papers presented at the forty-first annual Conference on Computer Applications and Quantitative Methods in Archaeology. The theme for the conference was "Across Space and Time", and the papers explore a multitude of topics related to that concept, including databases, the semantic Web, geographical information systems, data collection and management, and more

Across Space and Time Papers from the 41st Conference on Computer Applications and Quantitative Methods in Archaeology, Perth, 25-28 March 2013

The present volume includes 50 selected peer-reviewed papers presented at the 41st Computer Applications and Quantitative Methods in Archaeology Across Space and Time (CAA2013) conference held in Perth (Western Australia) in March 2013 at the University Club of Western Australia and hosted by the recently established CAA Australia National Chapter. It also hosts a paper presented at the 40th Computer Applications and Quantitative Methods in Archaeology (CAA2012) conference held in Southampton

Optical Sensors

This book is a compilation of works presenting recent developments and practical applications in optical sensor technology. It contains 10 chapters that encompass contributions from various individuals and research groups working in the area of optical sensing. It provides the reader with a broad overview and sampling of the innovative research on optical sensors in the world