Comparing Object-Based and Pixel-Based Methods for Local Climate Zones Mapping with Multi-Source Data
TL;DR: Zhang et al. as mentioned in this paper compared object-based and pixel-based local climate zones (LCZs) mapping with multi-source data in detail, and concluded that the object based method is capable of LCZ mapping and performs better than the pixel based method under the same training condition unless in undersegmentation cases.
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Abstract: The local climate zones (LCZs) system, a standard framework characterizing urban form and environment, effectively promotes urban remote sensing research, especially urban heat island (UHI) research. However, whether mapping with objects is more advantageous than with pixels in LCZ mapping remains uncertain. This study aims to compare object-based and pixel-based LCZ mapping with multi-source data in detail. By comparing the object-based method with the pixel-based method in 50 and 100 m, respectively, we found that the object-based method performed better with overall accuracy (OA) higher at approximately 2% and 5%, respectively. In per-class analysis, the object-based method showed a clear advantage in the land cover types and competitive performance in built types while LCZ2, LCZ5, and LCZ6 performed better with the pixel-based method in 50 m. We further employed correlation-based feature selection (CFS) to evaluate feature importance in the object-based paradigm, finding that building height (BH), sky view factor (SVF), building surface fraction (BSF), permeable surface fraction (PSF), and land use exhibited high selection frequency while image bands were scarcely selected. In summary, we concluded that the object-based method is capable of LCZ mapping and performs better than the pixel-based method under the same training condition unless in under-segmentation cases.
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Citations
Investigating urban heat-related health risks based on local climate zones: A case study of Changzhou in China
Lei Ma,Guoan Huang,Brian Alan Johnson,Zhenjie Chen,Manchun Li,Ziyu Yan,Wenfeng Zhan,Heng Lu,Weiqiang He,Dongjie Lian +9 more
TL;DR: Li et al. as discussed by the authors proposed an LCZ-based risk assessment approach for assessing heat-related health risks, which can be used for informing and implementing area-level urban planning strategies.
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Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis
TL;DR: This work underscores the potential of coupling image compression and texture feature quantification for monitoring changes related to human chest conditions, offering a promising avenue for efficient storage and diagnostic assessment of critical medical imaging.
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Towards better exploiting object-based image analysis paradigm for local climate zones mapping
TL;DR: Zhang et al. as mentioned in this paper proposed a new object-based LCZ mapping framework based on multi-level features, which consist of information from pixel and region, bridging adjacent objects through regionlevel features while preserving original image features.
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SAR and Multi-Spectral Data Fusion for Local Climate Zone Classification with Multi-Branch Convolutional Neural Network
TL;DR: Li et al. as discussed by the authors proposed a multi-source data fusion network for local climate zone (MsF-LCZ-Net), which contains a multibranch CNN for multi-modal feature extraction and fusion, and a classifier for LCZ prediction.
Application and future of local climate zone system in urban climate assessment and planning—Bibliometrics and meta-analysis
Hongchi Zhang,Jin Bai,Jun Zhao,Fei Guo,Peisheng Zhu,Jing Dong,Jun Cai +6 more
TL;DR: This study conducts a bibliometric and meta-analysis review of Local Climate Zone (LCZ) empirical studies over the past decade, analyzing geographical distribution, keywords, and research hotspots to improve understanding and address challenges in urban climate planning.
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