Artificial Intelligence for Sustainable Urban Biodiversity: A Framework for Monitoring and Conservation

TL;DR

This paper presents an AI-driven framework for monitoring and conserving urban biodiversity, demonstrating significant improvements in species detection, ecosystem analysis, and conservation planning accuracy.

Contribution

It introduces a comprehensive AI framework for urban biodiversity management, integrating multiple data sources and providing strategies for ethical and equitable implementation.

Findings

AI achieves over 90% accuracy in wildlife tracking

Data integration enables large-scale ecosystem analysis

AI tools increase conservation prediction accuracy by 18.5%

Abstract

The rapid expansion of urban areas challenges biodiversity conservation, requiring innovative ecosystem management. This study explores the role of Artificial Intelligence (AI) in urban biodiversity conservation, its applications, and a framework for implementation. Key findings show that: (a) AI enhances species detection and monitoring, achieving over 90% accuracy in urban wildlife tracking and invasive species management; (b) integrating data from remote sensing, acoustic monitoring, and citizen science enables large-scale ecosystem analysis; and (c) AI decision tools improve conservation planning and resource allocation, increasing prediction accuracy by up to 18.5% compared to traditional methods. The research presents an AI-Driven Framework for Urban Biodiversity Management, highlighting AI's impact on monitoring, conservation strategies, and ecological outcomes. Implementation strategies include: (a) standardizing data collection and model validation, (b) ensuring equitable AI access across urban contexts, and (c) developing ethical guidelines for biodiversity monitoring. The study concludes that integrating AI in urban biodiversity conservation requires balancing innovation with ecological wisdom and addressing data quality, socioeconomic disparities, and ethical concerns.