Lessons from a human-in-the-loop machine learning approach for identifying vacant, abandoned, and deteriorated properties in Savannah, Georgia
<|reference_start|>Lessons from a human-in-the-loop machine learning approach for identifying vacant, abandoned, and deteriorated properties in Savannah, Georgia: 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]<|reference_end|>
@article{liang2024lessons,
    title={Lessons from a human-in-the-loop machine learning approach for
  identifying vacant, abandoned, and deteriorated properties in Savannah,
  Georgia},
    author={Xiaofan Liang, Brian Brainerd, Tara Hicks, Clio Andris},
    journal={Journal of Planning Education and Research (2024)},
    year={2024},
    doi={10.1177/0739456X241273945},
    archivePrefix={arXiv},
    eprint={2407.11138},
    primaryClass={cs.LG physics.soc-ph}
}