Cost-Sensitive Multitask Active Learning for Characterization of Urban Environments With Remote Sensing
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DOI: 10.1109/lgrs.2018.2813436
Abstract: We propose a novel cost-sensitive multitask active learning (CSMTAL) approach. Cost-sensitive active learning (CSAL) methods were recently introduced to specifically minimize labeling efforts emerging from ground surveys. Here, we build upon a CSAL method but… read more here.
Keywords: active learning; multitask active; remote sensing; sensitive multitask ... See more keywords