对于城市管理者和设计师,来说，改善城市社区的功能属性，增强面对复杂性和不确定性的地域弹性至关重要。目前,社区规划往往遵循自上而下的方法，缺乏有效的指标来量化居民,的非正式行为，导致与原规划经常发生冲突。本研究引入了 CommuniWave, 一种机器学习模型，旨在有效检测和量化城市社区的非正式行为程度 (DIB)。该模型集成了基于mmaction2,的行为捕获网络(BCN)、自主开发的YOLOv10模型(YLX),和使用随机森林的行为评估模型(BEM)。最终, 通过从街头视频生成DIB 波动图, 该模型有助于动态监控, 支持城市管理者做出精细决策，以增强社区的整体弹性。
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.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)