Geo-Enriched Data Modeling & Mining
A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).
Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 12213
Interests: spatial data science; geographic information systems; data mining and machine learning; spatial index structures and efficient algorithms; uncertain data; geospatial simulation; location-based social networks
Special Issues, Collections and Topics in MDPI journals
Both of the current trends in technology such as smartphones, general mobile devices, stationary sensors and satellites as well as a new user mentality of utilizing this technology to voluntarily share information produce a huge flood of geospatial data. This data is enriched by multiple additional sources or contexts such as social information, text, multimedia data, and scientific measurements, called geo-enriched data. This data flood provides a tremendous potential of discovering new and possibly useful knowledge. The novel research challenge is to model, share, search, and mine this wealth of geo-enriched data. The focus of this Special Issue is to analyze what has been achieved so far and how to further exploit the enormous potential of this data flood. The ultimate goal of this Special Issue is to develop a general framework of methods for modeling, searching and mining enriched geospatial data in order to fuel an advanced analysis of big data applications beyond the current research frontiers. Furthermore, this Special Issue intends to compile an interdisciplinary research collection in the fields of databases, data science, and geoinformation science.
This Special Issue is dedicated to giving an overview of state-of-the-art solutions, techniques and applications on modeling, managing, searching and mining geo‐enriched data, such spatio-textual, spatio-temporal, spatio-social, geo-social network, mobile and wireless data.
We call for original papers from researchers around the world that focus on topics including, but not limited to, the following:
Dr. Andreas Züfle
Dr. Joon-Seok Kim
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 submissions that pass pre-check are 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 1700 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 Spatial Data
- Crowdsourcing Computing Resources
- Data Extraction Techniques including NLP
- Data Mining on Geo-Enriched data
- Geo-Multimedia DataM Geo-Social Data
- Geo-Textual Data
- Indexing Geo-Enriched data
- Location-Based Social Networks
- Spatial Data Models and Representation
- Spatial Privacy and Confidentiality
- Spatial Reasoning and Analysis
- Spatial Recommendation Systems
- Temporal Geo-Enriched Data
- Uncertainty in Spatial and Spatio-Temporal Data
- Visualization of Geo-Enriched Data