# CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities

arXiv: https://arxiv.org/abs/2607.08554 · HTML: https://mlsanity.com/paper/2607.08554/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 9 Jul 2026.

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

- [A Cost Efficient Approach to Correct OCR Errors in Large Document Collections](https://mlsanity.com/paper/1905.11739/) (2019)

## Built on by (most cited first, 2 of 2)

- [EEG Emotion Recognition From AI-Generated Biodigital Architecture Images](https://mlsanity.com/paper/2607.24808/) (2026)
- [SetEasy: A Multi-Modal Classroom Engagement Assessment and Seating Optimization Framework](https://mlsanity.com/paper/2608.07188/) (2026)

## Abstract (arXiv)

For urban managers and designers, improving the functional attributes of urban communities to enhance territorial resilience in the face of complexity and uncertainty is crucial. Currently, community planning often follows a top-down approach and lacks effective metrics to quantify informal behaviors of residents, leading to frequent conflicts with original plans. This study introduces CommuniWave, a machine learning model designed to efficiently detect and quantify the Degree of Informal Behavior (DIB) in urban communities. The model integrates a Behavior Capture Net (BCN) based on mmaction2, a self-developed YOLOv10 model (YLX), and a Behavior Eval Model (BEM) using random forest. Ultimately, by generating DIB fluctuation charts from street videos, the model facilitates dynamic monitoring, supporting urban managers in making refined decisions to enhance the overall resilience of communities.