Intelligent Remote Sensing and Sustainable Management of Landscape and Green Areas
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".
Deadline for manuscript submissions: closed (26 October 2023) | Viewed by 1999
Special Issue Editors
Interests: vegetation remote sensing; forest structure; soil and water conservation; geographic information system
Interests: computer vision; machine learning; geographic information system; remote sensing
Interests: digital elevation model and digital terrain analysis; deep learning; geographic information system
Special Issue Information
Dear Colleagues,
With the increase in earth-observing satellites, the volume of timely remote sensing data at large scales is rapidly growing. It is a challenge to mine the potential information in massive remote sensing data automatically and accurately. In the past few years, artificial intelligence technology, especially deep learning, has achieved great success in the automatic recognition, classification, and extraction of images and videos; it can fulfil the needs of remote sensing images. Although much architecture with an outstanding performance has been proposed in the last few years to address RS problems, there is much room for improvement.
This Special Issue aims to collect the latest developments and applications of both basic and applied research on deep learning applied in remote sensing, with particular attention to the structure and performance of deep learning models suited to remote sensing data, and the mining of potential natural rules in remote sensing data. Research can focus on, but is not limited to, landscape- or green-areas-related image fusion, image registration, scene classification, object detection, LULC classification, image segmentation, object-based image analysis (OBIA), and so on. The application research of artificial intelligence methods such as machine learning/deep learning and remote sensing technologies such as optical remote sensing, LiDAR, UAV, and InSAR are particularly welcome. Original research articles and reviews are welcome.
I/We look forward to receiving your contributions.
Dr. Zhu-Jun Gu
Dr. Jiangfan Feng
Dr. Ying Zhu
Dr. Hui Xiao
Guest Editors
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Keywords
- vegetation
- green area
- deep learning
- AI
- remote sensing
- image fusion
- scene classification
- image segmentation
- object-based image analysis
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