Very-High-Resolution SAR Images and Linked Open Data Analytics Based on Ontologies

In this paper, we deal with the integration of multiple sources of information such as Earth observation (EO) synthetic aperture radar (SAR) images and their metadata, semantic descriptors of the image content, as well as other publicly available geospatial data sources expressed as linked open data for posing complex queries in order to support geospatial data analytics. Our approach lays the foundations for the development of richer tools and applications that focus on EO image analytics using ontologies and linked open data. We introduce a system architecture where a common satellite image product is transformed from its initial format into to actionable intelligence information, which includes image descriptors, metadata, image tiles, and semantic labels resulting in an EO-data model. We also create a SAR image ontology based on our EO-data model and a two-level taxonomy classification scheme of the image content. We demonstrate our approach by linking high-resolution TerraSAR-X images with information from CORINE Land Cover (CLC), Urban Atlas (UA), GeoNames, and OpenStreetMap (OSM), which are represented in the standard triple model of the resource description frameworks (RDFs)

Mobile 2D and 3D Spatial Query Techniques for the Geospatial Web

The increasing availability of abundant geographically referenced information in the Geospatial Web provides a variety of opportunities for developing value-added LBS applications. However, large data volumes of the Geospatial Web and small mobile device displays impose a data visualization problem, as the amount of searchable information overwhelms the display when too many query results are returned. Excessive returned results clutter the mobile display, making it harder for users to prioritize information and causes confusion and usability problems. Mobile Spatial Interaction (MSI) research into this “information overload” problem is ongoing where map personalization and other semantic based filtering mechanisms are essential to de-clutter and adapt the exploration of the real-world to the processing/display limitations of mobile devices. In this thesis, we propose that another way to filter this information is to intelligently refine the search space. 3DQ (3-Dimensional Query) is our novel MSI prototype for information discovery on today’s location and orientation-aware smartphones within 3D Geospatial Web environments. Our application incorporates human interactions (interpreted from embedded sensors) in the geospatial query process by determining the shape of their actual visibility space as a query “window” in a spatial database, e.g. Isovist in 2D and Threat Dome in 3D. This effectively applies hidden query removal (HQR) functionality in 360º 3D that takes into account both the horizontal and vertical dimensions when calculating the 3D search space, significantly reducing display clutter and information overload on mobile devices. The effect is a more accurate and expected search result for mobile LBS applications by returning information on only those objects visible within a user’s 3D field-of-view

Approximate Query Answering Based on Topological Neighborhood and Semantic Similarity in OpenStreetMap

In this paper we focus on a pictorial query language, referred to as Geographical Pictorial Query Language (GeoPQL), and we revise its formal semantics by considering the polygon-polyline, polyline-polyline, and polygon-polygon topological relationships. This work proposes the Approximate Answering Engine (AAE) within a Distributed System, referred to as GeoPQLJSON (GeoPQLJ). The AAE provides approximate answers to query with empty results by following two directions: the Operator Conceptual Neighborhood (OCN) graph, and the OpenStreetMap (OSM) attribute hierarchy, giving maximum flexibility to the user choices. According to the former, the geo-operators of the queries can be replaced with the ones labeling the adjacent nodes of the OCN graph. By following the latter, the system evaluates the OSM attribute semantic similarity according to the information content approach, and proposes possible attribute replacements to the user. Note that the presence of OSM attributes allows the quick and direct access to large amount of geographical data, without requiring in our case the use of the topological elements. The functionalities of the Distributed GeoPQLJ System are illustrated by several query examples

Temporal and Spatial Expansion of Urban LOD for Solving Illegally Parked Bicycles in Tokyo

The illegal parking of bicycles is a serious urban problem in Tokyo. The purpose of this study was to sustainably build Linked Open Data (LOD) to assist in solving the problem of illegally parked bicycles (IPBs) by raising social awareness, in cooperation with the Office for Youth Affairs and Public Safety of the Tokyo Metropolitan Government (Tokyo Bureau). We first extracted information on the problem factors and designed LOD schema for IPBs. Then we collected pieces of data from the Social Networking Service (SNS) and the websites of municipalities to build the illegally parked bicycle LOD (IPBLOD) with more than 200,000 triples. We then estimated the temporal missing data in the LOD based on the causal relations from the problem factors and estimated spatial missing data based on geospatial features. As a result, the number of IPBs can be inferred with about 70% accuracy, and places where bicycles might be illegally parked are estimated with about 31% accuracy. Then we published the complemented LOD and a Web application to visualize the distribution of IPBs in the city. Finally, we applied IPBLOD to large social activity in order to raise social awareness of the IPB issues and to remove IPBs, in cooperation with the Tokyo Bureau

Mobile 2D and 3D Spatial Query Techniques for the Geospatial Web

The increasing availability of abundant geographically referenced information in the Geospatial Web provides a variety of opportunities for developing value-added LBS applications. However, large data volumes of the Geospatial Web and small mobile device displays impose a data visualization problem, as the amount of searchable information overwhelms the display when too many query results are returned. Excessive returned results clutter the mobile display, making it harder for users to prioritize information and causes confusion and usability problems. Mobile Spatial Interaction (MSI) research into this “information overload” problem is ongoing where map personalization and other semantic based filtering mechanisms are essential to de-clutter and adapt the exploration of the real-world to the processing/display limitations of mobile devices. In this thesis, we propose that another way to filter this information is to intelligently refine the search space. 3DQ (3-Dimensional Query) is our novel MSI prototype for information discovery on today’s location and orientation-aware smartphones within 3D Geospatial Web environments. Our application incorporates human interactions (interpreted from embedded sensors) in the geospatial query process by determining the shape of their actual visibility space as a query “window” in a spatial database, e.g. Isovist in 2D and Threat Dome in 3D. This effectively applies hidden query removal (HQR) functionality in 360º 3D that takes into account both the horizontal and vertical dimensions when calculating the 3D search space, significantly reducing display clutter and information overload on mobile devices. The effect is a more accurate and expected search result for mobile LBS applications by returning information on only those objects visible within a user’s 3D field-of-view. ii

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

OnGIS: Semantic Query Broker for Heterogeneous Geospatial Data Sources

Querying geospatial data from multiple heterogeneous sources backed by different management technologies poses an interesting problem in the data integration and in the subsequent result interpretation. This paper proposes broker techniques for answering a user's complex spatial query: finding relevant data sources (from a catalogue of data sources) capable of answering the query, eventually splitting the query and finding relevant data sources for the query parts, when no single source suffices. For the purpose, we describe each source with a set of prototypical queries that are algorithmically arranged into a lattice, which makes searching efficient. The proposed algorithms leverage GeoSPARQL query containment enhanced with OWL 2 QL semantics. A prototype is implemented in a system called OnGIS