Enhancing Urban Green Spaces: AI-Driven Insights for Biodiversity Conservation and Ecosystem Services

Urban green spaces (UGS) enhance biodiversity and provide essential ecosystem services like air purification, climate regulation, water management, and recreation. Despite their importance, UGS are often overlooked in urban planning, limiting their potential for resilience and sustainability. This study examines biodiversity in UGS and their capacity to deliver ecosystem services using field surveys, GIS mapping, stakeholder interviews, and AI-driven analytics. AI-based image recognition and remote sensing automate species identification and assess vegetation health, improving biodiversity assessments. Machine learning models analyze spatial and environmental data to predict UGS contributions to mitigating heat islands, air pollution, and stormwater runoff. Findings show that UGS serve as biodiversity hotspots, hosting diverse flora and fauna. Ecosystem service provision varies based on green space type, size, and management. AI-driven insights reveal key biodiversity factors like vegetation composition, spatial configurations, and human activities, offering data-driven recommendations for urban planning. Integrating AI into urban ecology supports evidence-based decision-making, urging policymakers and communities to optimize UGS management for biodiversity and human well-being.