Geospatial Big Data and AI/Deep Learning for the Sustainable Planet
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 12520
Special Issue Editors
Interests: city science; big data analytics; sustainable urban planing; city structure and dynamics; the livingness of space
Special Issues, Collections and Topics in MDPI journals
Interests: big geospatial data; GeoAI; high-performance GeoComputation; spatiotemporal modeling; land-use and land-cover change; urban informatices
Special Issues, Collections and Topics in MDPI journals
Interests: big geospatial data analytics; data mining and knowledge discovery; human mobility; spatio-temporal dynamics; citizen science; digital twins for smart cities
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Geospatial big data and AI/deep learning provide a new means for researching the Earth’s surface at a variety of scales, for example, land use changes or geographic forms and processes in general. Different from conventional geospatial small data, geospatial big data is characterized by its higher resolution in both space and time and individual-based rather than aggregated and can therefore be studied in its entirety rather than samples. It is these unique properties of big data that have further empowered AI or deep learning in various fields. Recently, the living structure has emerged as a new paradigm not only for better understanding geographic forms and processes (or city structure and dynamics in particular), but also for effectively transforming cities and communities towards a sustainable society or a sustainable planet in general. The notion of living structure is defined as a mathematical structure in which there are numerous recursively defined substructures at different levels of hierarchy, and it can be used to characterize the livingness of space: the more substructures the more living, and the higher hierarchy of the substructures the more living. This outlined research on living structure is not intended to delineate the scope of the Special Issue, but servers as an example of diverse research for the Special Issue papers or submissions. Any submissions that touch one or two of the keywords listed below will be welcome.
Prof. Dr. Bin Jiang
Prof. Dr. Qingfeng (Gene) Guan
Prof. Dr. Songnian Li
Guest Editors
Manuscript Submission Information
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Keywords
- remote sensing big data
- social media big data
- nighttime imagery
- artificial intelligence
- deep learning
- land use changes
- climate change
- sustainability
- living structure
- city structure and dynamics
- head/tail breaks
- natural cities
- scaling law
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