Special Issue "Geospatial Big Data and Machine Learning Opportunities and Prospects"
A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).
Deadline for manuscript submissions: 31 March 2021.
Interests: agent-based models (ABMs); sensor networks; spatial decision support systems; machine learning of movement behaviors
Interests: complex systems; Agent-Based modeling; GIS; GIScience; artificial intelligence; machine learning; landscape ecology; forestry; spatial analysis
The increasing availability of large, dynamic data sets creates tremendous opportunities and challenges for empirical science. As an editorial in Nature pointed out, “Big Data” is relevant not only because it is big, but it is also complex. The analysis and use of such data is beyond the comprehension of most individuals using traditional tools. New and innovative methods are required to usefully utilize the torrent of information available to scientists today. Moreover, there are many suggestions that prove that many forms of Big Data have a spatial component (e.g., GPS data). This is particularly true when the information is gathered from spatially distributed sensors connected to the internet and communicating with one another, also referred to as the “Internet of Things”.
Further, the growth in Big Data has been accompanied by new computational methods that include the use of “machine learning” methodologies to process and make sense of such large datasets. Machine learning algorithms can be applied to geospatial Big Data for a variety of reasons, including enhancing our understanding of causal dynamics in systems, capturing those processes, and predicting system states. Although much of current geospatial research relies on simple models with relatively little data assimilation, the emerging intermarriage of geospatial Big Data and machine learning seeks to represent real systems with some fidelity, and can carry significant data and computational demands. The above changes in the computational landscape present both an opportunity and a challenge for the next generation of GIScience research, with some scholars already engaged in exploratory research with this new frontier. Better integration of geospatial Big Data with machine learning algorithms presents opportunities to scale geospatial data analysis over larger geographic extents, represent dynamic system behaviors in near real-time, and use model predictions to anticipate and control networked devices and sensors.
Assoc. Prof. Dr. Raja Sengupta
Assoc. Prof. Liliana Perez
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.
- geospatial big data
- machine learning
- exploratory data analysis
- classification and regression trees
- deep learning
- neural networks
- self-organizing maps