Remote Sensing Information and Sustainable Urban Ecology
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".
Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 693
Special Issue Editors
Interests: space intelligent remote sensing; multi-mode hyperspectral remote sensing; intelligent application of remote sensing big data
Special Issues, Collections and Topics in MDPI journals
Interests: spatio-temporal data analytics; unified satellite image fusion; spatial statistics for land use/land cover change modeling; multi-objective optimization for sustainable land use planning
Special Issues, Collections and Topics in MDPI journals
2. College of Information and Communication Engineering, Harbin Engineering University, Harbin, China
Interests: remote sensing image processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Today, our cities house more than 50% of the global population and are responsible for 70% of global energy consumption and 80% of global CO2 emissions. Therefore, cities face major challenges and various environmental, societal, and economic problems that must be addressed in order to keep pace with the increasing sustainability requirements. Remote sensing information plays a significant role in developing advanced analytics solutions for sustainable urban ecology. Since advances in remote sensing technologies, cities have generated a huge number of data that offer richer services for citizens. Currently, a myriad of remote sensing sensors are deployed in cities throughout the world, generating various types of remote sensing information, such as optical data, multispectral data, hyperspectral data, lidar data, SAR data, etc. This Special Issue aims to survey state-of-the-art methods, technologies, and systems in remote sensing information, as well as algorithms and methods for the extraction of information from data by AI, computational, and statistical approaches.
Dr. Peng Wang
Prof. Dr. Bo Huang
Prof. Dr. Liguo Wang
Guest Editors
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Keywords
- fusion and resolution enhancement for urban remote sensing data
- land cover mapping and classification for urban remote sensing data
- endmember extraction and unmixing for urban remote sensing data
- dimensionality reduction and band selection for urban remote sensing data
- classification and segmentation for urban remote sensing data
- change detection and time-series analysis for urban remote sensing data
- artificial intelligence for remote sensing information in sustainable urban ecology
- deep learning for remote sensing information in sustainable urban ecology
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