# Lessons from a human-in-the-loop machine learning approach for identifying vacant, abandoned, and deteriorated properties in Savannah, Georgia

arXiv: https://arxiv.org/abs/2407.11138 · HTML: https://mlsanity.com/paper/2407.11138/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 51 references that are in the map.
- First posted on arXiv 15 Jul 2024.

## Builds on (most cited first, 1 of 1)

- [Iterative Random Forests to detect predictive and stable high-order interactions](https://mlsanity.com/paper/1706.08457/) (2017)

## Built on by (most cited first, 1 of 1)

- [Can Urban Blight Be Accessed with Vision-language Models: A Case Study in Detroit](https://mlsanity.com/paper/2608.01753/) (2026)

## Abstract (arXiv)

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]