Local Climate Zone Classification Using YOLOV8 Modeling in Instance Segmentation Method

Abstract

Local climate zones play a crucial role in understanding the microclimates within urban areas, contributing to urban planning, environmental sustainability, and human comfort. Istanbul, as a transcontinental city straddling Europe and Asia, exhibits a rich blend of historical and modern architecture, varying land use patterns, and diverse microclimates. In this study, using high-resolution Google Earth imagery for explores the classification, utilizing a cutting-edge deep learning architecture YOLOv8 model, of Local Climate Zones (LCZ) in Istanbul, a city known for its diverse and dynamic urban landscape. The latest cutting-edge YOLO model, YOLOv8, is designed for tasks such as object detection, image classification, and instance segmentation, showcasing its versatility in computer vision applications. Labeled data was created according to WUDAPT's sharing the things to consider when "create LCZ training areas" from google earth images. The model is trained on high-resolution, bird's-eye-view images of Istanbul obtained from Google Earth, meticulously labeled with LCZ categories. The results obtained from the test images demonstrate the model's efficacy in accurately classifying and segmenting LCZ categories, providing valuable insights into the local climate variations within Istanbul. This research contributes to the field of urban climate studies by offering a robust and scalable approach to LCZ classification using advanced deep learning techniques. The outcomes hold implications for urban planning, environmental sustainability, and informed decision-making in the context of Istanbul's unique and diverse urban environment.

Keywords

• local climate zones
• deep learning
• LCZ classification
• instance segmentation

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