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15 June 2021
European Journal of Remote Sensing
Special Issue Editor(s)
Dr Tu Nguyen,
Purdue University Fort Wayne
Dr Nam P. Nguyen,
Dr Claudio Savaglio,
University of Calabria
Dr Ying Zhang,
University of Electronic Science and Technology of China
Dr Braulio Dumba,
IBM Thomas J. Watson Research Center
Future of Urban Remote Sensing and New Sensors
Remote sensing plays a prominent role in studying earth systems, where urban areas are not an exception. Presently, cities are growing rapidly, and there is an increasing need for state-of-the-art land topography, land use maps, and other spatial data related information’s. This information is most vital as they form the key source of land use planning, natural resource management, and various other urban planning and management processes. In general, urban remote sensing uses high-resolution satellite imagery to derive the most accurate and consistent information on urban attributes. Considering the recent events, the outbreak of COVID-19 had profoundly changed the way people live, work, and do their everyday activities. On the other side, the urban population is increasing tremendously, where one-third of residential areas in the urban sectors remain polluted and unhealthy with poor sanitation facilities. It is hard that disruptors to urban development were already active, and it is more likely to be heightened from a post-COVID perspective. As a result, the impacts could be dramatic if left unaddressed.
Urban remote sensing acquires information about urban areas and objects through earth-orbiting satellite sensors. The data extracted from urban remote sensing has profound applications and is widely used to analyze the urban environments. The medium resolution satellite imagery data covers a vast amount of the geographical area and forms the worthwhile assessment tool. It enables policy makers to identify areas of infringement and environmental sensitivity. Similarly, high-resolution spatial data assists in settlement categorization, infrastructure and service endowment mapping, green infrastructure improvement, and regional planning accomplishments. Whereas the panchromatic imagery greatly contributes to the development of powerful tools for data analysis. It also helps in crime mapping, transportation analysis, disaster management, public safety, and asset management processes. Hence, it is evident that urban remote sensing and advances in sensor technologies empower sustainable urban planning and pave the way for sustainable development if used appropriately.
This special issue is designed to focus on the future of urban remote sensing and new sensors. It aims to cover the major areas of urban remote sensing from future aspects. First, it aims to explore advances in high-resolution satellite imagery and other alternative sources for urban feature extraction. Next, it emphasizes innovative image processing algorithms and sensor techniques to derive accurate and consistent information from remote sensor data. Finally, it intends to uncover state-of-the-art data analytics techniques and predictive modeling methods. Achieving these three objectives are most crucial as it greatly assists in dealing with the emerging demands of urban planning processes.
List of topics for the special section include, but are not limited to the following:
- New data & sensors for urban remote sensing from future perspective
- Advances in image processing for future urban remote sensing and applications
- Novel algorithms and techniques for interpretation of remotely sensed data
- Advances in high-resolution satellite imagery for future urban remote sensing
- Image processing and classification models for urban remote sensing
- Trends in big data and deep learning for urban monitoring
- Advances in remote sensing and sensor technologies for modelling smart, resilient, green, and equitable cities
- IoT-enabled urban remote sensing techniques for smart cities
- Advances in artificial intelligence and federated learning for urban remote sensing
- Frontiers in pattern recognition and knowledge discovery for urban remote sensing applications
- Predictive modelling algorithms for urban remote sensing from future aspects
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