Computer Vision in Environmental Monitoring Automated Detection for Biodiversity and Climate Action
DOI:
https://doi.org/10.64252/a24f9536Keywords:
Computer Vision, Environmental Monitoring, Biodiversity, Climate Action, Object Detection, Species Identification, Deep Learning, SDGs, Remote Sensing, AI in Ecology.Abstract
The climate crisis and the worldwide shrinking of biodiversity are speeding up, and the world needs new approaches to environmental monitoring with the help of technologies. Artificial intelligence The subfield of computer vision has become a revolutionary aid in automatizing the task of detecting, classifying, and tracking features and species in the environment. This paper both examines the role of computer vision in biodiversity surveillance and climate action plans. We examine recent methodology, datasets and frameworks in object detection, species identification as well as habitat maps. A comparative study and practical case scenarios provide us with the ability to show how automated vision systems can resolve real-time environmental sensing, deforestation tracking, and marine species surveillance. These results highlight the value of AI-powered visual analytics in in the context of sustainable development goals (SDGs) of particular importance are SDG 13 (Climate Action) and SDG 15 (Life on Land). To summarize the paper, challenges and future directions of scalable, ethical, and deployable monitoring systems all over the world are discussed.