Special Issue "Information Fusion Based on GIS"

Special Issue Editors

Prof. Christine Pohl
E-Mail Website
Guest Editor
Dr. Christine Pohl CONSULTING, Osnabrueck, Germany
Interests: Remote Sensing, Image Fusion, Geoscience, Earth Observation, optical and radar image processing, geocoding
Prof. (Em.) Dr. J.L. van Genderen
E-Mail Website
Guest Editor
Department of Earth Observation Science,Faculty of Geoinformation Science and Earth Observation(ITC),University of Twente, P.O.Box 217,7500 AE Enschede, The Netherlands
Interests: image and data fusion, integration, GIS, international technology transfer

Special Issue Information

Dear Colleagues,

In recent years, the amount of data and information with reference to geographic location has increased drastically. Especially with the availability of open source software and free data, the integration of multiple sources of information has become an important issue.

The Special Issue aims at promoting new and innovative studies, experiences, and models to improve the quality of information derived from many different sources.

Accurate and up-to-date information on our environment are crucial to a sustainable living on the Earth. Climate change, environmental processes, natural resource management, urban development, and other important research themes require temporal monitoring and accurate information. The information originates from different dates, different data sources, different interpretations, and different analysis methods. Therefore, the unification and intelligent combination of geo-located information plays a pivotal role for decision-makers, planners, and citizens.

This Special Issue intends to provide an overview of current research and state-of-the-art processing of information in the context of geographic information systems (GIS). Big data aspects as well as multimodal data acquisition and integration, data and information fusion approaches, and the quality and quantity of topics are some of the possible themes to be covered in this Issue. Authors are welcome to submit manuscripts covering, for example, the following topics in the context of GIS:

- Data integration;
- Multimodal information sources;
- Remote sensing data and information fusion;
- Object-oriented analysis of remote sensing data for GIS-ready information;
- Information management in a GIS;
- Information actuality;
- Information quality;
- Human-oriented geographic information presentation;
- Big data;
- Information semantics;
- Information analysis.

Prof. Christine Pohl
Prof. (Em.) Dr. J.L. van Genderen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Big data
  • Information extraction
  • Compatibility of information
  • Geo crowdsourced data
  • Geographical visualization
  • Information fusion
  • Information quality
  • Information validity
  • Querying spatiotemporal information.

Published Papers (1 paper)

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Open AccessArticle
Point of Interest Matching between Different Geospatial Datasets
ISPRS Int. J. Geo-Inf. 2019, 8(10), 435; https://doi.org/10.3390/ijgi8100435 - 01 Oct 2019
Point of interest (POI) matching finds POI pairs that refer to the same real-world entity, which is the core issue in geospatial data integration. To address the low accuracy of geospatial entity matching using a single feature attribute, this study proposes a method [...] Read more.
Point of interest (POI) matching finds POI pairs that refer to the same real-world entity, which is the core issue in geospatial data integration. To address the low accuracy of geospatial entity matching using a single feature attribute, this study proposes a method that combines the D–S (Dempster–Shafer) evidence theory and a multiattribute matching strategy. During POI data preprocessing, this method calculates the spatial similarity, name similarity, address similarity, and category similarity between pairs from different geospatial datasets, using the multiattribute matching strategy. The similarity calculation results of these four types of feature attributes were used as independent evidence to construct the basic probability distribution. A multiattribute model was separately constructed using the improved combination rule of the D–S evidence theory, and a series of decision thresholds were set to give the final entity matching results. We tested our method with a dataset containing Baidu POIs and Gaode POIs from Beijing. The results showed the following—(1) the multiattribute matching model based on improved DS evidence theory had good performance in terms of precision, recall, and F1 for entity-matching from different datasets; (2) among all models, the model combining the spatial, name, and category (SNC) attributes obtained the best performance in the POI entity matching process; and (3) the method could effectively address the low precision of entity matching using a single feature attribute. Full article
(This article belongs to the Special Issue Information Fusion Based on GIS)
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