Loading...
Dr. Shaileshkumar Abasaheb Wagh is Head and Associate Professor in the PG and Research Department of Geography at MGSM'S Dadasaheb Dr. Suresh G. Patil College, Maharashtra, India. His research interests include environmental management, GIS, tourism, and sustainable development.
The tourist, hospitality, and cultural heritage sectors are seeing swift digital transformation propelled by data analytics, Artificial Intelligence (AI), the Internet of Things (IoT), cloud computing, big data, and immersive technologies. The increasing accessibility of real-time data from travellers, intelligent destinations, social media, booking platforms, sensors, and heritage sites has generated new prospects for personalised services, operational efficiency, sustainable destination management, and cultural preservation. Data-driven innovation empowers tourism stakeholders to elevate tourist experiences, optimise resource allocation, anticipate demand, refine decision-making, and safeguard cultural assets via advanced technologies. This chapter examines the principles, methodologies, technologies, applications, challenges, and future trajectories of data-driven innovation in tourism, hospitality, and cultural heritage management, emphasising its significance in promoting sustainable tourism development, digital transformation, and heritage preservation.
The escalation of environmental deterioration, climate change, biodiversity loss, deforestation, pollution, and the unsustainable exploitation of natural resources has heightened the necessity for sophisticated monitoring and conservation techniques. Artificial Intelligence (AI) has emerged as a transformative technology for tackling these difficulties by facilitating real-time environmental monitoring, predictive analytics, automated resource management, and evidence-based conservation planning. Through the integration of machine learning, deep learning, computer vision, remote sensing, Geographic Information Systems (GIS), Internet of Things (IoT), drones, and satellite imagery, AI-driven systems can analyse extensive environmental data, identify ecological changes, forecast environmental risks, and facilitate sustainable resource management. This chapter examines the principles, technology, applications, problems, and future trajectories of AI in environmental monitoring and natural resource conservation, emphasising its contribution to ecological sustainability, biodiversity preservation, and climate resilience.