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The urban heat island (UHI) phenomenon is concurrently influenced by urban expansion and climate change. However, the individual impacts of land use/land cover (LULC) changes and climate change remain unclear. In the present study, a high-resolution numerical Weather Research and Forecasting (WRF) model coupled with a single-layer urban canopy model (UCM) is implemented for Hefei to assess the influences of LULC and climate change simultaneously. The comprehensive increase in UHI intensity (UHII) was 0.76 K from 2003 to 2019 in the study area. The overall influence of LULC changes on the UHI effect was a 0.33 K increase in intensity and a 190 km² expansion in coverage. The results also show that the emergence of new high-intensity urban areas developed from farmlands had the strongest impact on UHI development compared to other types of LULC changes. The overall contribution of climate change to the UHII increased by 0.27 K from 2003 to 2019. The change in the storage heat flux was found to be responsible for the nocturnal UHII variation and long-term increase in the UHII, while the sensible heat flux was responsible for the diurnal UHII. [ABSTRACT FROM AUTHOR]

This study investigates the responses of the urban atmospheric thermal environment to two distinct heat waves in Hefei, China, and explores potential changes associated with future urban expansion. During the Event 1, characterized by clear and dry conditions, the western Pacific subtropical high limits water vapor influx, resulting in a significant cooling effect in rural area due to higher surface latent heat flux. The urban heat island (UHI) intensity, calculated using surface temperature and 2‐m temperature, reaches 5.2°C and 1.7°C during the Event 1, respectively. Although Event 2, characterized by cloudy and humid conditions, exhibits weaker UHI and urban dry island effects, it remains highly unfavorable for human comfort. During distinct heat waves, the vertical extent of the warming effect induced by future urban expansion varies, which can be attributed to environmental factors, such as atmospheric stability and near‐surface wind speed. Plain Language Summary: The study investigates the interactions between the urban atmospheric thermal environment and two distinct heat waves in Hefei, China, as well as the changes induced by future urban expansion. During the first event, which has clear and dry weather conditions, the influx of water vapor is limited by a high‐pressure system. Consequently, rural area experiences a significant cooling effect due to higher surface latent heat flux. The urban heat island (UHI) intensity, measured by surface temperature and 2‐m temperature, reaches 5.2°C and 1.7°C, respectively, during this event. In contrast, the second event, characterized by cloudy and humid weather conditions, exhibits weaker UHI and urban dry island effects but remains highly uncomfortable for humans. The vertical extent of the warming effect caused by future urban expansion varies during distinct heat waves. This variation can be attributed to environmental factors, such as atmospheric stability and near‐surface wind speed. Key Points: The underlying mechanisms of the different responses of the urban atmospheric thermal environment to distinct heat wave events are analyzedThe limitation of water vapor influx by a high‐pressure system and intense solar radiation jointly enhance the urban heat island effectAtmospheric stability and wind speed could influence the vertical extent of the warming effect induced by future urban expansion [ABSTRACT FROM AUTHOR]

The urban functional zone, as a special fundamental unit of the city, helps to understand the complex interaction between human space activities and environmental changes. Based on the recognition of physical and social semantics of buildings, combining remote sensing data and social sensing data is an effective way to quickly and accurately comprehend urban functional zone patterns. From the object level, this paper proposes a novel object-wise recognition strategy based on very high spatial resolution images (VHSRI) and social sensing data. First, buildings are extracted according to the physical semantics of objects; second, remote sensing and point of interest (POI) data are combined to comprehend the spatial distribution and functional semantics in the social function context; finally, urban functional zones are recognized and determined by building with physical and social functional semantics. When it comes to building geometrical information extraction, this paper, given the importance of building boundary information, introduces the deeper edge feature map (DEFM) into the segmentation and classification, and improves the result of building boundary recognition. Given the difficulty in understanding deeper semantics and spatial information and the limitation of traditional convolutional neural network (CNN) models in feature extraction, we propose the Deeper-Feature Convolutional Neural Network (DFCNN), which is able to extract more and deeper features for building semantic recognition. Experimental results conducted on a Google Earth image of Shenzhen City show that the proposed method and model are able to effectively, quickly, and accurately recognize urban functional zones by combining building physical semantics and social functional semantics, and are able to ensure the accuracy of urban functional zone recognition. [ABSTRACT FROM AUTHOR]