  Deep learning has gained significant attention in remote sensing, especially
in pixel- or patch-level applications. Despite initial attempts to integrate
deep learning into object-based image analysis (OBIA), its full potential
remains largely unexplored. In this article, as OBIA usage becomes more
widespread, we conducted a comprehensive review and expansion of its task
subdomains, with or without the integration of deep learning. Furthermore, we
have identified and summarized five prevailing strategies to address the
challenge of deep learning's limitations in directly processing unstructured
object data within OBIA, and this review also recommends some important future
research directions. Our goal with these endeavors is to inspire more
exploration in this fascinating yet overlooked area and facilitate the
integration of deep learning into OBIA processing workflows.
