This paper explores the potential applications of cutting-edge Artificial Intelligence (AI) technologies, specifically Vision Transformer and Diffusion models in the field of environmental monitoring and preservation. By analyzing the current state of the art, we highlight the potential of these advanced techniques in capturing, analyzing, and communicating environmental data. Vision Transformer models demonstrate their effectiveness in identifying and classifying environmental features, while Diffusion models offer realistic image generation capabilities. We discuss the diverse applications of these models, such as visualizing environmental impacts, enhancing data quality, and generating synthetic images for training datasets. By bridging the gap between AI advancements and environmental research, this paper paves the way for improved accuracy, efficiency, and communication in the realm of sustainable environmental practices.

Recent advances in AI for enhanced environmental monitoring and preservation / Paolo Russo; Fabiana Di Ciaccio. - ELETTRONICO. - (2023), pp. 0-0. (Intervento presentato al convegno 2023 IEEE International Workshop on Metrology for the Sea).

Recent advances in AI for enhanced environmental monitoring and preservation

Fabiana Di Ciaccio
2023

Abstract

This paper explores the potential applications of cutting-edge Artificial Intelligence (AI) technologies, specifically Vision Transformer and Diffusion models in the field of environmental monitoring and preservation. By analyzing the current state of the art, we highlight the potential of these advanced techniques in capturing, analyzing, and communicating environmental data. Vision Transformer models demonstrate their effectiveness in identifying and classifying environmental features, while Diffusion models offer realistic image generation capabilities. We discuss the diverse applications of these models, such as visualizing environmental impacts, enhancing data quality, and generating synthetic images for training datasets. By bridging the gap between AI advancements and environmental research, this paper paves the way for improved accuracy, efficiency, and communication in the realm of sustainable environmental practices.
2023
2023 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters, MetroSea 2023
2023 IEEE International Workshop on Metrology for the Sea
Goal 14: Life below water
Goal 15: Life on land
Goal 9: Industry, Innovation, and Infrastructure
Paolo Russo; Fabiana Di Ciaccio
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1348851
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