Dear Colleagues,

Semantic analysis of digital data is a computational attempt to understand those data in a way that a human being would. This is an exceedingly difficult task, given the fact that the biological processes humans use for visual information processing are not fully understood and researchers cannot therefore simply mimic them. Throughout the history of artificial intelligence, a number of models, techniques, and systems have been proposed for semantic data analysis, including expert systems, heuristic data analysis, feature-based data analysis, and most recently convolutional neural networks and deep learning. Using semantic techniques in the protection and monitoring of the environment becomes more important in the era of the rapid climate and socioeconomic changes we are witnessing. In the meantime, the ever-increasing prevalence of large media data from various novel sources (e.g., social media, surveillance, medicine, and environmental sciences) has opened up incredible opportunities for researchers and practitioners.

The purpose of this Special Issue is to present recent advances in the semantic analysis of digital sensors and visual and other information that can be used in the protection and monitoring of natural (e.g., forests, sea, rivers) or artificial (e.g., buildings, roads) environments. This Special Issue aims to enable leading researchers all over the world to discuss their current work and recent advances in novel areas. This Special Issue will consider original research papers in the field, covering new theories, algorithms, and systems, as well as new implementations and applications incorporating state-of-the-art techniques for semantic analysis of visual data. Review articles and works on performance evaluation and benchmark datasets are also welcome.

The list of possible topics for this Special Issue includes but is not limited to:

• Computer vision
• Deep learning
• Heuristic analysis
• Image classification
• Image segmentation
• Object recognition
• Object tracking
• Semantic models
• Visual understanding
• Sensor data processing
• Reinforcement learning
• Large-scale benchmarking
• Empirical evaluation and
• Empirical evaluation over environment changes

Dr. Ljiljana Seric
Prof. Dr. Jialie Shen
Dr. Maja Braović
Guest Editors

Manuscript Submission Information

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• computer vision
• deep learning
• heuristic analysis
• image classification
• image segmentation
• object recognition
• object tracking
• semantic models
• visual understanding
• sensor data processing
• Reinforcement learning
• Large-scale benchmarking
• Empirical evaluation and
• Empirical evaluation over environment changes