430 research outputs found
Geospatial Semantics
Geospatial semantics is a broad field that involves a variety of research
areas. The term semantics refers to the meaning of things, and is in contrast
with the term syntactics. Accordingly, studies on geospatial semantics usually
focus on understanding the meaning of geographic entities as well as their
counterparts in the cognitive and digital world, such as cognitive geographic
concepts and digital gazetteers. Geospatial semantics can also facilitate the
design of geographic information systems (GIS) by enhancing the
interoperability of distributed systems and developing more intelligent
interfaces for user interactions. During the past years, a lot of research has
been conducted, approaching geospatial semantics from different perspectives,
using a variety of methods, and targeting different problems. Meanwhile, the
arrival of big geo data, especially the large amount of unstructured text data
on the Web, and the fast development of natural language processing methods
enable new research directions in geospatial semantics. This chapter,
therefore, provides a systematic review on the existing geospatial semantic
research. Six major research areas are identified and discussed, including
semantic interoperability, digital gazetteers, geographic information
retrieval, geospatial Semantic Web, place semantics, and cognitive geographic
concepts.Comment: Yingjie Hu (2017). Geospatial Semantics. In Bo Huang, Thomas J. Cova,
and Ming-Hsiang Tsou et al. (Eds): Comprehensive Geographic Information
Systems, Elsevier. Oxford, U
Geospatial Semantics for Topographic Data
U.S. Geological SurveyPlatinum Sponsors
Gold Sponsors
KU Department of Geography
KU Institute for Policy & Social Research
KU Libraries GIS and Data Services
State of Kansas Data Access and Support Center (DASC)
Silver Sponsors
Bartlett & West
KansasView
Kansas Biological Survey
U.S. Geological Survey (USGS)
Bronze Sponsors
AECOM
Black & Veatch
City of Lawrence - Utilities Department
ESRI
Global Information Systems
KU Center for Global & International Studies
KU Center for Remote Sensing of Ice Sheets (CReSIS)
KU Environmental Studies Program
Lucity, Inc.
Wilson & Company, Engineers and Architect
Towards Semantic Integration of Heterogeneous Sensor Data with Indigenous Knowledge for Drought Forecasting
In the Internet of Things (IoT) domain, various heterogeneous ubiquitous
devices would be able to connect and communicate with each other seamlessly,
irrespective of the domain. Semantic representation of data through detailed
standardized annotation has shown to improve the integration of the
interconnected heterogeneous devices. However, the semantic representation of
these heterogeneous data sources for environmental monitoring systems is not
yet well supported. To achieve the maximum benefits of IoT for drought
forecasting, a dedicated semantic middleware solution is required. This
research proposes a middleware that semantically represents and integrates
heterogeneous data sources with indigenous knowledge based on a unified
ontology for an accurate IoT-based drought early warning system (DEWS).Comment: 5 pages, 3 figures, In Proceedings of the Doctoral Symposium of the
16th International Middleware Conference (Middleware Doct Symposium 2015),
Ivan Beschastnikh and Wouter Joosen (Eds.). ACM, New York, NY, US
Geospatial information infrastructures
Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Geospatial information infrastructures (GIIs) provide the technological, semantic,organizationalandlegalstructurethatallowforthediscovery,sharing,and use of geospatial information (GI). In this chapter, we introduce the overall concept and surrounding notions such as geographic information systems (GIS) and spatial datainfrastructures(SDI).WeoutlinethehistoryofGIIsintermsoftheorganizational andtechnologicaldevelopmentsaswellasthecurrentstate-of-art,andreflectonsome of the central challenges and possible future trajectories. We focus on the tension betweenincreasedneedsforstandardizationandtheever-acceleratingtechnological changes. We conclude that GIIs evolved as a strong underpinning contribution to implementation of the Digital Earth vision. In the future, these infrastructures are challengedtobecomeflexibleandrobustenoughtoabsorbandembracetechnological transformationsandtheaccompanyingsocietalandorganizationalimplications.With this contribution, we present the reader a comprehensive overview of the field and a solid basis for reflections about future developments
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An Ontology for Grounding Vague Geographic Terms
Many geographic terms, such as “river” and “lake”, are vague, with no clear boundaries of application. In particular, the spatial extent of such features is often vaguely carved out of a continuously varying observable domain. We present a means of defining vague terms using standpoint semantics, a refinement of the
philosophical idea of supervaluation semantics. Such definitions can be grounded in actual data by geometric analysis and segmentation of the data set. The issues
raised by this process with regard to the nature of boundaries and domains of logical quantification are discussed. We describe a prototype implementation of a system capable of segmenting attributed polygon data into geographically significant regions and evaluating queries involving vague geographic feature terms
Applying spatial reasoning to topographical data with a grounded geographical ontology
Grounding an ontology upon geographical data has been pro-
posed as a method of handling the vagueness in the domain more effectively. In order to do this, we require methods of reasoning about the spatial relations between the regions within the data. This stage can be computationally expensive, as we require information on the location of
points in relation to each other. This paper illustrates how using knowledge about regions allows us to reduce the computation required in an efficient and easy to understand manner. Further, we show how this system can be implemented in co-ordination with segmented data to reason abou
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