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.