  With increasing urbanization, flooding is a major challenge for many cities
today. Based on forecast precipitation, topography, and pipe networks, flood
simulations can provide early warnings for areas and buildings at risk of
flooding. Basement windows, doors, and underground garage entrances are common
places where floodwater can flow into a building. Some buildings have been
prepared or designed considering the threat of flooding, but others have not.
Therefore, knowing the heights of these facade openings helps to identify
places that are more susceptible to water ingress. However, such data is not
yet readily available in most cities. Traditional surveying of the desired
targets may be used, but this is a very time-consuming and laborious process.
This research presents a new process for the extraction of windows and doors
from LiDAR mobile mapping data. Deep learning object detection models are
trained to identify these objects. Usually, this requires to provide large
amounts of manual annotations. In this paper, we mitigate this problem by
leveraging a rule-based method. In a first step, the rule-based method is used
to generate pseudo-labels. A semi-supervised learning strategy is then applied
with three different levels of supervision. The results show that using only
automatically generated pseudo-labels, the learning-based model outperforms the
rule-based approach by 14.6% in terms of F1-score. After five hours of human
supervision, it is possible to improve the model by another 6.2%. By comparing
the detected facade openings' heights with the predicted water levels from a
flood simulation model, a map can be produced which assigns per-building flood
risk levels. This information can be combined with flood forecasting to provide
a more targeted disaster prevention guide for the city's infrastructure and
residential buildings.
