AI-Driven Satellite Data for Global Environment Monitoring (Second Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 30 December 2024 | Viewed by 96
Special Issue Editor
Interests: artificial intelligence; semantic segmentation; remote sensing of disaster; applications in agriculture, forest, hydrology, and meteorology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This is the 2nd edition of the Special Issue “AI-Driven Satellite Data for Global Environment Monitoring”.
The acceleration of environmental changes on Earth may significantly affect the global atmosphere, oceans, agriculture, forests, and water. Indeed, the Earth belongs to our descendants, not to us, and so we must deliver a safe and clean Earth to them. Satellite remote sensing data are essential material for the spatially and temporally continuous observation of the Earth. Moreover, recent technological developments ensure higher resolution and broader coverage to monitor disasters, meteorology, air quality, vegetation, hydrology, and polar regions. AI is a powerful tool for creating high-quality satellite images and for observation of the Earth’s environmental phenomena using advanced computing power. In addition to the classical algorithms, various state-of-the-art models can help improve AI-driven satellite data for global environmental monitoring. We invite colleagues’ insights and contributions to various research areas involving remote sensing combined with an AI approach. Papers can be focused on, but are not limited to, the following:
- Deep learning-based object detection from satellite images for the environmental monitoring of Earth;
- Semantic segmentation of satellite images for the environmental monitoring of Earth;
- Super-resolution techniques for the environmental monitoring of Earth;
- AI-based spatiotemporal image fusion for the environmental monitoring of Earth;
- AI-based change detection for the environmental monitoring of Earth;
- Satellite-based disaster management using AI models;
- AI-based retrieval algorithm for the satellite products in atmosphere, meteorology, ocean, and air quality;
- AI-based retrieval algorithm for the satellite products in agriculture, forests, hydrology, and ecology;
- AI-driven novel methods for Earth’s environmental monitoring with satellite images.
Prof. Dr. Yang-Won Lee
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence
- semantic segmentation
- remote sensing of disaster
- applications in agriculture, forest, hydrology, and meteorology
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