Precision in Building Extraction: Comparing Shallow and Deep Models
  using LiDAR Data
Muhammad Sulaiman Mina Farmanbar Ahmed Nabil Belbachir Chunming Rong
Building segmentation is essential in infrastructure development, population
management, and geological observations. This article targets shallow models
due to their interpretable nature to assess the presence of LiDAR data for
supervised segmentation. The benchmark data used in this article are published
in NORA MapAI competition for deep learning model. Shallow models are compared
with deep learning models based on Intersection over Union (IoU) and Boundary
Intersection over Union (BIoU). In the proposed work, boundary masks from the
original mask are generated to improve the BIoU score, which relates to
building shapes' borderline. The influence of LiDAR data is tested by training
the model with only aerial images in task 1 and a combination of aerial and
LiDAR data in task 2 and then compared. shallow models outperform deep learning
models in IoU by 8% using aerial images (task 1) only and 2% in combined aerial
images and LiDAR data (task 2). In contrast, deep learning models show better
performance on BIoU scores. Boundary masks improve BIoU scores by 4% in both
tasks. Light Gradient-Boosting Machine (LightGBM) performs better than RF and
Extreme Gradient Boosting (XGBoost).