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).

本篇论文研究了建筑分割问题，利用浅层模型评估 LiDAR 数据在监督分割中的应用，并通过采用边界掩模提升边界一致性得分，结合航空图像和 LiDAR 数据进行模型的训练与比较，结果表明浅层模型在航空图像任务和航空图像与 LiDAR 数据任务下的 Intersection over Union 得分分别优于深度学习模型 8% 和 2%，与此同时深度学习模型在 Boundary Intersection over Union 得分方面表现更好，采用 Light Gradient-Boosting Machine (LightGBM) 的效果优于 RF 和 Extreme Gradient Boosting (XGBoost)。