In this paper, we describe efforts towards a continual learning workflow for automated building segmentation from remote sensing imagery that is intended to support decision making by governmental agencies. We describe a workflow under development that leverages human-machine collaboration as a core strategy to decrease annotation costs/needs, as well as aid quality assurance and increase understandability from end-users without necessarily machine learning expertise. These aspects will potentially increase trust by intended users and overall impact of model outcomes on real-world decision making.