Automatic Roof Type Classification Through Machine Learning for Regional Wind Risk Assessment
This work addresses a critical gap in building vulnerability modeling for regional wind risk assessment, though it is incremental as it applies existing machine learning methods to a specific domain problem.
The authors tackled the problem of missing roof-type data for wind risk assessment by developing an automatic classification framework using a Convolutional Neural Network on satellite images, achieving an F1 score of 0.96 on test data and applying it to over 161,000 houses.
Roof type is one of the most critical building characteristics for wind vulnerability modeling. It is also the most frequently missing building feature from publicly available databases. An automatic roof classification framework is developed herein to generate high-resolution roof-type data using machine learning. A Convolutional Neural Network (CNN) was trained to classify roof types using building-level satellite images. The model achieved an F1 score of 0.96 on predicting roof types for 1,000 test buildings. The CNN model was then used to predict roof types for 161,772 single-family houses in New Hanover County, NC, and Miami-Dade County, FL. The distribution of roof type in city and census tract scales was presented. A high variance was observed in the dominant roof type among census tracts. To improve the completeness of the roof-type data, imputation algorithms were developed to populate missing roof data due to low-quality images, using critical building attributes and neighborhood-level roof characteristics.