Mapping across Space and Time: A Perspective of Multisource Geospatial Data Matching and Integration

A special issue of Informatics (ISSN 2227-9709). This special issue belongs to the section "Big Data Mining and Analytics".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 2634

Special Issue Editors

School of Human Settlements and Civil Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: automatic matching and fusion of multi-source geospatial data; application of GIS, RS and machine learning technology in environmental science
Quanzhou Institute of Equipment Manufacturing, Haixi Institute, Chinese Academy of Sciences, Quanzhou 362216, China
Interests: photogrammetry and remote sensing technology; intelligent spatial information processing and application; point cloud processing and application
Special Issues, Collections and Topics in MDPI journals
School of Geospatial Information, Information Engineering University, Zhengzhou 450002, China
Interests: GeoAI; crowdsourced mapping; cognitive mapping for unmanned system; spatial data mining; location-based service
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the last decade, the production of geographic information has radically changed. With the availability of cheap location technologies, more and more geospatial datasets have become available, which not only consist of the traditional geospatial data from various official Bureaus or professional surveying and mapping corporations, but also the volunteered geographic information (VGI) and crowdsourcing data collected by the widespread of collaborative mapping activities. The continuously growing demand for geospatial services requires an emphasized study on geo-information from various sources covering the same geographic space. Multi-sourced data matching and integration, which aims at establishing logical connections between corresponding objects and combining two comparable geospatial datasets, is one of the most primary and significant measures that helps make different datasets interoperable. In general, geospatial dataset conflation is a complex process that may utilize work from a broad range of disciplines, including GIScience, image processing and feature extraction, data quality assessment, feasibility analysis of VGI, Crowdsourced or Social Sensed mobility data.

In this context, we propose a Special Issue that targets mapping across space and time with a perspective of multisource geospatial data matching and integration.

Our interest is in papers that cover a broad spectrum of topics, including, but not limited to, the following:

  • Methods and Applications of Multi-Source Data Matching and Integration;
  • Feature Extraction, Classification and Object Detection from Multiple Remote Sensing Sources;
  • VGI Quality Assessment;
  • Crowdsourcing Data for Routing and Navigational Purposes;
  • Social Sensing from Mobility Big Data;
  • Tracking Social Dynamics in Historical Cartographic Documents. 

Prof. Dr. Meng Zhang
Dr. Li Fang
Dr. Jian Yang
Guest Editors

Manuscript Submission Information

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Keywords

  • data integration
  • feature extraction
  • multi-source data
  • VGI
  • crowdsourced data
  • social sensing

Published Papers (1 paper)

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Review

17 pages, 324 KiB  
Review
Digital Weather Information in an Embodied World
by Alan E. Stewart and Matthew J. Bolton
Informatics 2023, 10(1), 13; https://doi.org/10.3390/informatics10010013 - 24 Jan 2023
Viewed by 1950
Abstract
We review the emergence of digital weather information, the history of human embodied knowing about weather, and two perspectives on cognition, one of which is symbolic (amodal, abstract, and arbitrary) and the other being embodied (embodied, extended, embedded, and enacted) to address the [...] Read more.
We review the emergence of digital weather information, the history of human embodied knowing about weather, and two perspectives on cognition, one of which is symbolic (amodal, abstract, and arbitrary) and the other being embodied (embodied, extended, embedded, and enacted) to address the question: Beyond the general weather information they provide, to what extent can digital devices be used in an embodied way to extend a person’s pick-up of weather information? This is an interesting question to examine because human weather information and knowledge has a long past in our evolutionary history. Our human ancestors had to pick-up immediate information from the environment (including the weather) to survive. Digital weather information and knowing has a comparatively short past and a promising future. After reviewing these relevant topics, we concluded that, with the possible exception of weather radar apps, nothing currently exists in the form of digital products than can extend the immediate sensory reach of people to alert them about just-about-to-occur weather—at least not in the embodied forms of information. We believe that people who are weather salient (i.e., have a strong psychological attunement to the weather) may be in the best position going forward to integrate digital weather knowing with that which is embodied. Full article
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