MaRTiny—A Low-Cost Biometeorological Sensing Device With Embedded Computer Vision for Urban Climate Research
MaRTiny—A Low-Cost Biometeorological Sensing Device With Embedded Computer Vision for Urban Climate Research
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DOI：
10.3389/fenvs.2022.866240
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Karthik K. Kulkarni;Florian A. Schneider;Tejaswini M. Gowda;Suren Jayasuriya;Ariane Middel
中科院分区：
文献类型：
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作者：
Karthik K. Kulkarni;Florian A. Schneider;Tejaswini M. Gowda;Suren Jayasuriya;Ariane Middel
极端的热量对人类健康施加了巨大的压力，并限制了人们在户外工作的能力，在公共空间中减轻热量。是昂贵的（6个方向设置）或缓慢且不准确的（地球温度计），并且该工程纸不会介绍一个新颖的，低成本的热敏感设备的硬件和软件设置。 ontrollers收集与人类热曝光相关的天气数据：空气温度，风速，地球温度和紫外线辐射。由于暴露水平的不同，阳光普照的是潜在的传感器，我们估计了使用机器学习（SVM）的Martiny天气观察，这是RMSE的重点。
Extreme heat puts tremendous stress on human health and limits people’s ability to work, travel, and socialize outdoors. To mitigate heat in public spaces, thermal conditions must be assessed in the context of human exposure and space use. Mean Radiant Temperature (MRT) is an integrated radiation metric that quantifies the total heat load on the human body and is a driving parameter in many thermal comfort indices. Current sensor systems to measure MRT are expensive and bulky (6-directional setup) or slow and inaccurate (globe thermometers) and do not sense space use. This engineering systems paper introduces the hardware and software setup of a novel, low-cost thermal and visual sensing device (MaRTiny). The system collects meteorological data, concurrently counts the number of people in the shade and sun, and streams the results to an Amazon Web Services (AWS) server. MaRTiny integrates various micro-controllers to collect weather data relevant to human thermal exposure: air temperature, humidity, wind speed, globe temperature, and UV radiation. To detect people in the shade and Sun, we implemented state of the art object detection and shade detection models on an NVIDIA Jetson Nano. The system was tested in the field, showing that meteorological observations compared reasonably well to MaRTy observations (high-end human-biometeorological station) when both sensor systems were fully sun-exposed. To overcome potential sensing errors due to different exposure levels, we estimated MRT from MaRTiny weather observations using machine learning (SVM), which improved RMSE. This paper focuses on the development of the MaRTiny system and lays the foundation for fundamental research in urban climate science to investigate how people use public spaces under extreme heat to inform active shade management and urban design in cities.