Abstract
Local climate zones (LCZ) describe urban surface structures, supporting studies of urban heat islands, sustainable urbanization, and energy balance. The existing studies mapped LCZs from satellite images using scene-based classification, which trained deep-learning classifiers by labeled image patches, segmented satellite images into patches by sliding windows to match the size of training data, and finally classified the segmented patches to obtain LCZ maps. However, sliding windows are different from the real footprints of LCZs, which leads to large errors in classification. To address this problem, this article proposes a parcel-based method for LCZ classification using Sentinel-2 images, road networks, and elevation data. First, the Sentinel-2 images are segmented by the road network to obtain the land parcels as classification units. Second, each image parcel is standardized to match the training dataset, So2Sat LCZ42. Third, the trained convolutional neural network (CNN) is used to classify the standardized parcels into LCZs. Finally, the building height information derived from elevation data is used to refine the LCZs by a rule-based classifier. The results of the four test sites show that the overall accuracy of our method is 0.75, higher than the sliding-window-based method's accuracy of 0.47. Additional simulation experiments demonstrated that parcels derived from road networks can reduce the mixture effect in image patches, and parcel standardization can ensure the transferability of the CNN model trained by regular image patches. Considering that the road network and elevation data are widely available, the proposed method has the potential of mapping LCZs in large areas.
Highlights
O VER THE past few decades, the process of urbanization in the world has accelerated [1]
The trained convolutional neural network (CNN) model with the smallest cross-entropy loss in the validation set was selected as the best model for the first-hierarchy classification in the proposed Local climate zones (LCZ) mapping method
It employed road networks to segment Sentinel-2 images to image parcels with irregular shapes and various sizes to better match the real footprints of LCZs
Summary
O VER THE past few decades, the process of urbanization in the world has accelerated [1]. Many urban climate models have been developed to study the effect of urban climate change on the human system and predict future scenarios. These urban climate models require the maps of the urban landscape as input. Local climate zones (LCZs) were proposed in 2012 to provide a comprehensive, generic, and flexible scheme to describe the urban landscapes with respect to the climate-related properties on a global scale [4]. LCZ has become a standard quantitative description of the urban landscape that was used as the input of urban climate models [5] and provides useful information for various applications, such as infrastructure planning, disaster mitigation, and population assessment [6]
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More From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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