CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities
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,
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- PossiblePossibly related (embedding) · 49%Cross-scenario evaluation of explainable machine learning for non-invasive summer occupancy detection across five building scenarios - Nature →
- LinkedLinked via arxiv author · 85%Hongye Yang →
“CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities”
- LinkedLinked via arxiv author · 85%Shien Liu →
“CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities”
- LinkedLinked via arxiv author · 85%Zhihao Xie →
“CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities”