Feb. 22, 2024, 5:41 a.m. | Adrian H\"ohl, Ivica Obadic, Miguel \'Angel Fern\'andez Torres, Hiba Najjar, Dario Oliveira, Zeynep Akata, Andreas Dengel, Xiao Xiang Zhu

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.13791v1 Announce Type: new
Abstract: In recent years, black-box machine learning approaches have become a dominant modeling paradigm for knowledge extraction in Remote Sensing. Despite the potential benefits of uncovering the inner workings of these models with explainable AI, a comprehensive overview summarizing the used explainable AI methods and their objectives, findings, and challenges in Remote Sensing applications is still missing. In this paper, we address this issue by performing a systematic review to identify the key trends of how …

abstract arxiv become benefits box cs.lg explainable ai extraction knowledge machine machine learning modeling overview paradigm review sensing summarizing type

Founding AI Engineer, Agents

@ Occam AI | New York

AI Engineer Intern, Agents

@ Occam AI | US

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne