Smart Urban Ecosystems for Biodiversity Conservation Using Artificial Intelligence and Digital Technologies

Abstract

Biodiversity loss caused by rapid urbanization, fragmentation of habitats, ecological degradation and the lack of efficient monitoring of the environment, as well as the lack of integration of ecological intelligence in real-time decision making for conservation, are still significant challenges.Despite these trends, the implementation of existing smart-city solutions fails to combine ecological intelligence with real-time conservation decision making. The proposed Smart Urban Ecosystem Framework for this study is based on the Internet of Things (IoT) sensors, satellite images, Unmanned Aerial Vehicle (UAV), computer vision, graph neural networks (GNN), explainable artificial intelligence (XAI), and geospatial analytics, which can be used to continuously assess biodiversity and enable adaptive ecosystem management. Within a dynamic digital twin, multi-source environmental data are fused, and habitat quality is monitored, species distribution is predicted, biodiversity hotspots are identified, ecological connectivity is evaluated and conservation interventions are recommended based on interpretable predictive models. The experimental evaluation shows that the species classification accuracy of 97.1% and habitat suitability prediction accuracy of 95.4%, and the precision of detecting biodiversity hotspots and assessing ecological connectivity accuracy 96.2% and 94.3%, respectively, and the accuracy of predicting biodiversity risk of 95.1% are high. Monitoring time was reduced by 43.6% and conservation planning efficiency was increased by 31.8% compared with conventional methods. The proposed framework brings together, in a unified, explainable and real-time digital ecosystem, the world of artificial intelligence and urban ecological management. The research provides a platform towards resolving the challenges of decision support for biodiversity-positive urban planning that is scalable, data-driven, and data-informed, driving towards resilient smart cities by intelligent ecosystem conservation and proactively managing the environment.