Urban Environment Modelling Using Geospatial Technique, Artificial Intelligence and Aerial/Satellite Imagery – Challenges and Future Trends
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".
Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 592
Special Issue Editors
2. Australian Artificial Intelligence Institute (AAII), Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, NSW 2007, Australia
Interests: GIS and remote sensing; artificial intelligence; airborne/spaceborne image processing; 2D/3D geospatial modeling; emergency management; environmental urban planning
2. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, NSW 2007, Australia
Interests: risk engineering; computational intelligence; decision support systems; land use and environmental planning
2. Australian Artificial Intelligence Institute (AAII), Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, NSW 2007, Australia
Interests: artificial intelligence; cloud/fog computing; optimization
Special Issue Information
Dear Colleagues,
Artificial Intelligence (AI) technology has leveraged the current geospatial systems, including remote sensing known as Geospatial AI, in a novel robust way to provide an automatic inspection workflow. This includes image acquisition from the sensor, digital image pre-processing, training, and testing techniques, validation, and knowledge extraction.
Geospatial AI technologies have begun to shape the industry in all its disciplines as cities continue to evolve to adapt to contemporary issues such as climate change resilience, natural resource management, and urban planning.
In the future, Geospatial AI will change the way we look at our Earth. It will enhance our ability to observe and interpret data, particularly through the adoption of a single platform that supports geospatial data in all its forms.
In this Special Issue, we are keen to publish those scientific manuscripts that managed to deploy evidence-based Geospatial AI to address any sort of location-based applications from spaceborne/aerial borne image classification to spatial decision support systems. The proposed methodology should be defensible, scalable, reliable, and pierceable to improve decision-making performance.
Dr. Hossein M. Rizeei
Dr. Mohsen Naderpour
Dr. Fahimeh Ramezani
Guest Editors
Manuscript Submission Information
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Keywords
- geospatial
- artificial intelligence
- geospatial information systems (GIS)
- urban environment modeling
- image processing
- deep learning
- optimization
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