Applications of Discrete and Computational Geometry to Geoprocessing

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 3722

Special Issue Editor

Department of Industrial and Information Engineering and Economics, University of L'Aquila, 67100 L'Aquila, Italy
Interests: spatial databases; spatial query languages; mathematical modeling of spatial information; computational geometry; spatio-temporal reasoning; wualitative modeling of geographical information; indoor and outdoor navigation; volunteered geographic information
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Special Issue Information

Dear Colleagues,

Discrete and computational geometry represents a core knowledge for dealing with discretized geometric objects, such as finite sets of points, lines, triangles, and polygons. Geoprocessing with vector datasets is based on algorithms and data modes that have been developed in computational geometry. Convex hulls, buffers, Delaunay triangulations, and polygon overlays are common geometric operations that are integrated into spatial database management systems (SDBMS), geographical information systems (GIS), and various libraries and tools for geospatial analysis, like GDAL/OGR or the Java Topology Suite (JTS). In most cases, users of such systems are not aware of the actual algorithms underlying geometric operations, which are seen as out of the box components.

As discrete and computational geometry is a young discipline, there are still open issues and new and more efficient algorithms are discovered every day. Often, new development and customizations of general algorithms to specific new subfields of geospatial science are needed, such as transport and mobility, smart cities, agriculture, public health, disaster management, and social networks.

In this Special Issue, we bring together researchers and developers in computational geometry and geographic information science to pin down the state of the art and define new challenges and prospects from the synergy between the two fields. Submissions are encouraged across a range of topics, including but not limited to the following:

  • Data structures and algorithms for discrete and computational geometry with a direct application to geospatial data
  • Integration of computational geometry findings into specific application domains, such as indoor navigation, precision agriculture, 3D urban modeling, emergency management, and disease control and prevention;
  • Incorporation of discrete and computational geometry into geoinformation science curricula
  • Visionary and open-ended contributions to defining new research problems and solutions in the above fields

 

Prof. Eliseo Clementini
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

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Published Papers (1 paper)

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21 pages, 16896 KiB  
Article
A Decentralized Semantic Reasoning Approach for the Detection and Representation of Continuous Spatial Dynamic Phenomena in Wireless Sensor Networks
by Roger Cesarié Ntankouo Njila, Mir Abolfazl Mostafavi and Jean Brodeur
ISPRS Int. J. Geo-Inf. 2021, 10(3), 182; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10030182 - 19 Mar 2021
Cited by 4 | Viewed by 2976
Abstract
In this paper, we propose a decentralized semantic reasoning approach for modeling vague spatial objects from sensor network data describing vague shape phenomena, such as forest fire, air pollution, traffic noise, etc. This is a challenging problem as it necessitates appropriate aggregation of [...] Read more.
In this paper, we propose a decentralized semantic reasoning approach for modeling vague spatial objects from sensor network data describing vague shape phenomena, such as forest fire, air pollution, traffic noise, etc. This is a challenging problem as it necessitates appropriate aggregation of sensor data and their update with respect to the evolution of the state of the phenomena to be represented. Sensor data are generally poorly provided in terms of semantic information. Hence, the proposed approach starts with building a knowledge base integrating sensor and domain ontologies and then uses fuzzy rules to extract three-valued spatial qualitative information expressing the relative position of each sensor with respect to the monitored phenomenon’s extent. The observed phenomena are modeled using a fuzzy-crisp type spatial object made of a kernel and a conjecture part, which is a more realistic spatial representation for such vague shape environmental phenomena. The second step of our approach uses decentralized computing techniques to infer boundary detection and vertices for the kernel and conjecture parts of spatial objects using fuzzy IF-THEN rules. Finally, we present a case study for urban noise pollution monitoring by a sensor network, which is implemented in Netlogo to illustrate the validity of the proposed approach. Full article
(This article belongs to the Special Issue Applications of Discrete and Computational Geometry to Geoprocessing)
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