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: closed (30 June 2021) | Viewed by 6903
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.
Dr. Raja Sengupta
Dr. Liliana Perez
Manuscript Submission Information
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- geospatial big data
- machine learning
- exploratory data analysis
- classification and regression trees
- deep learning
- neural networks
- self-organizing maps