Local Climate Zone Classification Using YOLOV8 Modeling in Instance Segmentation Method

Melike Nicancı Sinanoğlu, Şinasi Kaya
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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.
在实例分割法中使用 YOLOV8 建模进行地方气候带分类
当地气候带对了解城市地区的微气候起着至关重要的作用,有助于城市规划、环境可持续性和人类舒适度。伊斯坦布尔作为一座横跨欧亚大陆的城市,展现了历史与现代建筑的丰富融合、不同的土地利用模式和多样的微气候。在这项研究中,利用高分辨率的谷歌地球图像,利用最先进的深度学习架构 YOLOv8 模型,探索了伊斯坦布尔地方气候区(LCZ)的分类,这座城市以其多样化和动态的城市景观而闻名。最新的前沿 YOLO 模型 YOLOv8 专为物体检测、图像分类和实例分割等任务而设计,展示了其在计算机视觉应用中的多功能性。标签数据是根据 WUDAPT 分享的从谷歌地球图像 "创建 LCZ 训练区 "时应考虑的事项创建的。该模型是在从谷歌地球获取的伊斯坦布尔高分辨率鸟瞰图像上进行训练的,这些图像都精心标注了 LCZ 类别。从测试图像中获得的结果证明了该模型在准确分类和划分低纬度地区类别方面的功效,为了解伊斯坦布尔当地的气候差异提供了宝贵的信息。这项研究利用先进的深度学习技术提供了一种稳健且可扩展的 LCZ 分类方法,为城市气候研究领域做出了贡献。在伊斯坦布尔独特而多样的城市环境背景下,研究成果对城市规划、环境可持续性和知情决策具有重要意义。
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