Automatic Roof Type Classification Through Machine Learning for Regional
  Wind Risk Assessment
Shuochuan Meng Mohammad Hesam Soleimani-Babakamali Ertugrul Taciroglu
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.