Local Climate Zone Classification Using YOLOV8 Modeling in Instance Segmentation Method

Melike Nicancı Sinanoğlu; Şinasi Kaya

Makale detayları

Local Climate Zone Classification Using YOLOV8 Modeling in Instance Segmentation Method

Yazarlar:

Melike Nicancı Sinanoğlu | Şinasi Kaya

Yayıncı:

Istanbul University

Ana Başlıklar:

Photogrammetry and Remote Sensing

Dergi:

International Journal of Environment and Geoinformatics Vol.11 Issue 2, pp.1-9

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001317727

pISSN:

2148-9173

Anahtar kelimeler:

Özet:

 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 WUDAPTs sharing the things to consider when create LCZ training areas from google earth images. The model is trained on high-resolution, birds-eye-view images of Istanbul obtained from Google Earth, meticulously labeled with LCZ categories. The results obtained from the test images demonstrate the models 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 Istanbuls unique and diverse urban environment.

 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 WUDAPTs sharing the things to consider when create LCZ training areas from google earth images. The model is trained on high-resolution, birds-eye-view images of Istanbul obtained from Google Earth, meticulously labeled with LCZ categories. The results obtained from the test images demonstrate the models 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 Istanbuls unique and diverse urban environment.

 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 WUDAPTs sharing the things to consider when create LCZ training areas from google earth images. The model is trained on high-resolution, birds-eye-view images of Istanbul obtained from Google Earth, meticulously labeled with LCZ categories. The results obtained from the test images demonstrate the models 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 Istanbuls unique and diverse urban environment.

 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 WUDAPTs sharing the things to consider when create LCZ training areas from google earth images. The model is trained on high-resolution, birds-eye-view images of Istanbul obtained from Google Earth, meticulously labeled with LCZ categories. The results obtained from the test images demonstrate the models 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 Istanbuls unique and diverse urban environment.

Makale detayları

Local Climate Zone Classification Using YOLOV8 Modeling in Instance Segmentation Method

Yazarlar:

Melike Nicancı Sinanoğlu | Şinasi Kaya

Yayıncı:

Istanbul University

Ana Başlıklar:

Photogrammetry and Remote Sensing

Dergi:

International Journal of Environment and Geoinformatics Vol.11 Issue 2, pp.1-9

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001317727

pISSN:

2148-9173

local climate zones

deep learning

LCZ classification

instance segmentation

Sayı

Makale detayları

Local Climate Zone Classification Using YOLOV8 Modeling in Instance Segmentation Method

Yazarlar: Melike Nicancı Sinanoğlu | Şinasi Kaya

Yayıncı: Istanbul University

Ana Başlıklar: Photogrammetry and Remote Sensing

Dergi: International Journal of Environment and Geoinformatics Vol.11 Issue 2, pp.1-9

Belge Tipi: Makale

Yayın Tarihi: 6/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001317727

pISSN: 2148-9173

local climate zones

deep learning

LCZ classification

instance segmentation

Sayı

Yazarlar:

Melike Nicancı Sinanoğlu | Şinasi Kaya

Yayıncı:

Istanbul University

Ana Başlıklar:

Photogrammetry and Remote Sensing

Dergi:

International Journal of Environment and Geoinformatics Vol.11 Issue 2, pp.1-9

Belge Tipi:

Makale

Yayın Tarihi:

6/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001317727

pISSN:

2148-9173