About this Research Topic
In the domain of environmental science, the integration of deep learning with remote sensing technologies marks a significant advancement. Remote sensing platforms—including satellites, drones, and ground-based sensors—generate vast quantities of data that are key to understanding and managing environmental dynamics. The profound capability of deep learning in processing and pattern recognition facilitates the transformation of this extensive data into valuable insights, crucial for pushing the boundaries of environmental science and policy. Despite these advancements, challenges in optimal data utilization and processing precision persist, requiring ongoing research and methodological innovation.
This Research Topic aims to showcase the latest innovations in deep learning algorithms customized for remote sensing data and explore new applications in environmental monitoring, conservation, and management. The emphasis is on overcoming existing limitations and unlocking new potentials that these technologies offer for environmental science.
To gather further insight we welcome articles addressing, but not limited to, the following themes:
• Detection of environmental changes like deforestation and urban expansion through object detection, using deep learning integrated with remote sensing;
• Classification of land cover, and analysis of ecosystems, water bodies, and vegetation for enhanced environmental monitoring;
• Monitoring dynamic environmental phenomena, including wildfire propagation, species migration, and ice movements through innovative data analysis techniques;
• Multi-temporal remote sensing data analysis for tracking climate change, land-use alterations, and natural catastrophes using advanced deep learning methods;
• High-resolution imagery segmentation to distinguish diverse environmental factors such as forests, wetlands, and urban territories;
• Approaches to biodiversity conservation and management within protected areas through the application of deep learning on remote sensing data;
• Synthesis of multi-spectral data sources (optical, radar, thermal, LiDAR) to refine environmental monitoring and heighten the accuracy of deep learning models.
This Research Topic aims to showcase the latest innovations in deep learning algorithms customized for remote sensing data and explore new applications in environmental monitoring, conservation, and management. The emphasis is on overcoming existing limitations and unlocking new potentials that these technologies offer for environmental science.
To gather further insight we welcome articles addressing, but not limited to, the following themes:
• Detection of environmental changes like deforestation and urban expansion through object detection, using deep learning integrated with remote sensing;
• Classification of land cover, and analysis of ecosystems, water bodies, and vegetation for enhanced environmental monitoring;
• Monitoring dynamic environmental phenomena, including wildfire propagation, species migration, and ice movements through innovative data analysis techniques;
• Multi-temporal remote sensing data analysis for tracking climate change, land-use alterations, and natural catastrophes using advanced deep learning methods;
• High-resolution imagery segmentation to distinguish diverse environmental factors such as forests, wetlands, and urban territories;
• Approaches to biodiversity conservation and management within protected areas through the application of deep learning on remote sensing data;
• Synthesis of multi-spectral data sources (optical, radar, thermal, LiDAR) to refine environmental monitoring and heighten the accuracy of deep learning models.
Keywords: deep learning, remote sensing, environmental monitoring, object detection classification
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
