ACAIMSF 2023 : Advanced Computational and Artificial Intelligence Methods in Smart Forestry
Call For Papers
Special Session on Advanced Computational and Artificial Intelligence Methods in Smart Forestry is a part of 7th European Conference on Electrical Engineering & Computer Science (ELECS 2023)
Bern, Switzerland, December 21-23, 2023.
The submission of manuscripts to Special Session on Advanced Computational and Artificial Intelligence Methods in Smart Forestry should be done through EasyChair:
To provide a platform for researchers, practitioners, and policymakers to discuss, share, and promote the latest advancements in computational techniques and AI applications tailored for forestry. This special issue aims to shed light on the intersection of AI, data science, and the intricate dynamics of forest ecosystems, paving the way for more informed forest management, conservation, and industry practices. This conference special issue seeks to combine the collective expertise of multiple disciplines, ensuring a comprehensive perspective on the potential of AI and advanced computational techniques in revolutionizing forestry for the betterment of both humanity and the planet.
Remote Sensing and AI: Utilization of machine learning techniques for interpreting satellite imagery, LiDAR data, and drone footage in forest monitoring and assessment.
Predictive Modeling: Employing deep learning and other AI models to forecast forest growth, disease outbreaks, pest infestations, and the effects of climate change.
Automated Wildlife Tracking: Leveraging AI in the identification and monitoring of wildlife populations within forested areas, using sensors, audio recordings, and cameras.
Forest Fire Detection and Prediction: Utilizing machine learning algorithms for early detection of forest fires, prediction of fire spread, and strategizing containment.
Sustainable Forest Management: AI-assisted tools for monitoring tree growth, calculating sustainable harvest rates, and ensuring reforestation success.
Bioacoustic Monitoring: Using neural networks and other AI methods to interpret and catalog forest sounds, aiding in biodiversity assessments and ecosystem health monitoring.
Timber Traceability and Supply Chain Monitoring: Implementing blockchain and AI for ensuring sustainable and transparent forestry practices.
Smart IoT Devices in Forestry: Deployment and data interpretation from smart sensors, RFID tags, and other IoT devices for real-time forest monitoring.
Forest Health and Disease Analysis: AI-driven diagnostic tools for detecting pathogens, diseases, and other stressors in forest ecosystems.
AI-Driven Conservation Efforts: Strategies and applications for using AI to boost conservation efforts, including protection of endangered species and habitats.
Virtual and Augmented Reality in Forestry Education: Innovative ways to leverage AI in producing immersive educational experiences for forestry students and professionals.
Forest Biomimicry and AI: Learning from forest structures and processes to enhance AI algorithms and architectures.
Data Integration and Interoperability: Challenges and solutions in merging heterogeneous datasets for comprehensive forest analyses.
Dr. Robertas Damasevicius, Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland. Email: email@example.com
Prof. Dr. Gintautas Mozgeris, Agriculture Academy, Faculty of Forest Sciences and Ecology, Department of Forest Sciences, Vytautas Magnus University, Akademija, Lithuania. Email: firstname.lastname@example.org
Dr. Rytis Maskeliunas, Department of Multimedia Technologies, Kaunas University of Technology, 51311 Kaunas, Lithuania. Email: email@example.com