東京大学

関本研究室 / Sekimoto Lab. IIS, the University of Tokyo.

Revamping Building Extraction with YOLOv8: A Multi-Domain Assessment inBangkok's Diverse Land-Use Types
Bhanu Prasad CHINTAKINDI, Shenglong CHEN, Yoshiki OGAWA, Yoshihide SEKIMOTO
Background● Building footprint data is crucial for urban planning,crisis management, navigation, database creation,change detection, and 3D modeling.
● YOLO models (You Only Look Once), especially
YOLOv7 and YOLOv8, are prominent in object
detection due to their speed and accuracy.
● Existing models face limitations in rural regions ordeveloping countries due to intricate building
patterns across diverse land-use types.

Methodology
1. Model Development:
a. Seven specialized building extraction models
were developed using instance segmentation
techniques.
b. YOLOv8x-seg model was chosen as thebaseline and trained from scratch to createmodels for Rural, BuiltUp, SubUrban, Urban,Commercial, and ALUT (All Land-Use-Types).
c. YOLOv7-seg model was used as an additional
baseline, trained with the ALUT dataset.
2. Multi-Domain Assessment:
a. Evaluated and compared model performancewithin and across land-use types using metrics
such as F1 scores, visual predictions, andradar plots.

Objective● Develop seven specialized YOLOv8 and YOLOv7 models trained fromscratch to accommodate distinct land-use types and their unique building
patterns.
● Conduct a multi-domain assessment across various land-use types toevaluate the accuracy, applicability, and adaptability of these models.

Dataset● Source: Open-source 0.3m resolution satellite imagery from Mapbox and2 million building footprints from the Bangkok Metropolitan Administration
GIS Portal.
● Land-Use Types: Categorized into Commercial, Urban, SubUrban,BuiltUp, and Rural based on Bangkok's 2013 land-use zoning plan.
● Data Preparation: Cropped 0.3m resolution satellite image patches(640x640 pixels) with a 30% overlap were used to create training,validation, and testing datasets for each land-use category.

Results
Accuracy Analysis:● Models showed remarkable adaptability, aligningclosely with ground truth buildings.
● Performance varied depending on land use; forinstance, the rural model performed best in ruralzones, and the commercial model excelled incommercial zones.
● The ALUT model consistently ranked second,demonstrating strong generalization capabilities.

Predicted results by Individual Land-Use-Type Models and corresponding source image and ground truth label in (a) RuralArea, (b) BuiltUp Area, (c) SubUrban Area, (d) Urban Area, and (e) Commercial Area.

Conclusion● The study highlights the effectiveness ofspecialized YOLOv8 models in accurately
extracting building footprints across diverse landuse types.
● Future research involves creating a global
building extraction model by ensembling
individual models for specific land-use cases,
providing a template for accurate building
extraction in various international contexts.

Radar Plots illustrating the performance of the six YOLOv8: Individual Land-Use-Type Models and ALUT Model across different
building-size categories in various test areas, including (a) Rural, (b) BuiltUp, (c) SubUrban, (d) Urban, (e) Commercial, and (f)ALUT. The quantile limit values are also included for reference.

Sekimoto Lab. @ IIS Human Centered Urban Informatics, the University of Tokyo