AI and Remote Sensing for Coastal Erosion and Habitat Monitoring

The aim of this workshop is to equip participants with practical skills in applying Artificial Intelligence and Remote Sensing techniques for monitoring coastal erosion, marine habitats, and biodiversity. It seeks to bridge the gap between environmental science and data-driven technologies by enabling participants to develop AI-based models for coastal resilience and sustainable marine resource management.

• To introduce the fundamentals of remote sensing and satellite data for coastal and marine applications.
• To develop practical skills in applying machine learning and computer vision for shoreline detection and habitat mapping.
• To enable participants to build predictive models for coastal erosion and habitat loss assessment.
• To demonstrate AI applications in coral reef monitoring, marine biodiversity analysis, and sustainable fisheries management.
• To promote data-driven decision-making for coastal resilience and environmental sustainability.

• Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
• Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
• University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
• Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
• Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.

• Understand the principles of AI-driven remote sensing for coastal and marine monitoring.
• Apply machine learning techniques for shoreline detection and habitat classification.
• Develop basic predictive models to assess coastal erosion and habitat loss.
• Analyze marine biodiversity and coral reef health using computer vision approaches.
• Interpret satellite-derived environmental data to support sustainable coastal management decisions.