Precision in Building Extraction: Comparing Shallow and Deep Models using LiDAR Data

TL;DR

This study compares shallow and deep models for building segmentation using LiDAR data, finding shallow models outperform deep ones in IoU scores with aerial images, while boundary masks enhance shape accuracy.

Contribution

It demonstrates the effectiveness of shallow models over deep models in certain segmentation tasks and introduces boundary masks to improve shape boundary accuracy.

Findings

Shallow models outperform deep models in IoU by 8% with aerial images.

Boundary masks improve BIoU scores by 4%.

LightGBM outperforms RF and XGBoost.

Abstract

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).