AI-based solutions for autonomous underwater observing systems and science discovery
2024
Marini, Simone | Lagomarsino Oneto, Daniele | Cavaiola, Mattia | Aguzzi, Jacopo | D'Agostino, Daniele
Biodiversity Change in the Anthropocene: Priorities for research - Cambiamento della biodiversità nell’Antropocene: priorità per la Ricerca, A dynamic and open discussion on one of the major issues of our time,10-11 April 2024, Fano, Italy.-- 1 page
Mostrar más [+] Menos [-]The recent advances in Artificial Intelligence (AI) support the scientific discoveries not only by providing new advanced data analysis tools, rather supporting scientists generating new hypotheses, designing new experiments, allowing the acquisition of large datasets, providing new insights on their interpretation [1]. As AI needs a vast amount of data to be developed, data-rich environments provide for more opportunities to advance their domain of knowledge, allowing AI-based approaches to perform autonomous intelligent actions. The underwater domain is a remarkable case of data-rich environment, where AI approaches have a relevant impact on the scope and scale of ocean observations and smart sensors lead to a continuous flood of data. This work presents a research perspective on the continuum between two novel topics relevant for the biodiversity research: the study of underwater intelligent multi-parameter observing systems and the study of novel methodologies for multi-varied data analysis based on explainable AI approaches
Mostrar más [+] Menos [-]Peer reviewed
Mostrar más [+] Menos [-]Palabras clave de AGROVOC
Información bibliográfica
Este registro bibliográfico ha sido proporcionado por Institut de Ciències del Mar