  Addressing strategies for managing vacant, abandoned, and deteriorated (VAD)
properties is important for maintaining healthy communities. Yet, the process
of identifying these properties can be difficult. Here, we create a
human-in-the-loop machine learning (HITLML) model called VADecide and apply it
to a parcel-level case study in Savannah, Georgia. The results show a higher
prediction accuracy than was achieved when using a machine learning model
without human input in the training. The HITLML approach also reveals
differences between machine vs. human-generated results. Our findings
contribute to knowledge about the advantages and challenges of HITLML in urban
planning.
  [Accepted for Publication at a Peer Review Journal]
