Precision in Building Extraction: Comparing Shallow and Deep Models using LiDAR Data
This work addresses building extraction for infrastructure and population management, but it is incremental as it primarily compares existing shallow and deep methods on a known benchmark.
This paper tackles building segmentation from aerial and LiDAR data by comparing shallow models like LightGBM with deep learning models, finding that shallow models outperform in IoU by 8% with aerial images only and 2% with combined data, while deep models are better at BIoU scores, and boundary masks improve BIoU by 4%.
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).