Snake-Based Model for Automatic Roof Boundary Extraction in the Object Space Integrating a High-Resolution Aerial Images Stereo Pair and 3D Roof Models

Abstract

The accelerated urban development over the last decades has made it necessary to update spatial information rapidly and constantly. Therefore, cities' three-dimensional models have been widely used as a study base for various urban problems. However, although many efforts have been made to develop new building extraction methods, reliable and automatic extraction is still a major challenge for the remote sensing and computer vision communities, mainly due to the complexity and variability of urban scenes. This paper presents a method to extract building roof boundaries in the object space by integrating a high-resolution aerial images stereo pair, three-dimensional roof models reconstructed from light detection and ranging (LiDAR) data, and contextual information of the scenes involved. The proposed method focuses on overcoming three types of common problems that can disturb the automatic roof extraction in the urban environment: perspective occlusions caused by high buildings, occlusions caused by vegetation covering the roof, and shadows that are adjacent to the roofs, which can be misinterpreted as roof edges. For this, an improved Snake-based mathematical model is developed considering the radiometric and geometric properties of roofs to represent the roof boundary in the image space. A new approach for calculating the corner response and a shadow compensation factor was added to the model. The created model is then adapted to represent the boundaries in the object space considering a stereo pair of aerial images. Finally, the optimal polyline, representing a selected roof boundary, is obtained by optimizing the proposed Snake-based model using a dynamic programming (DP) approach considering the contextual information of the scene. The results showed that the proposed method works properly in boundary extraction of roofs with occlusion and shadows areas, presenting completeness and correctness average values above 90%, RMSE average values below 0.5 m for E and N components, and below 1 m for H component.

Procedural Roof Generation From a Single Satellite Image

“…Li et al [LYT ∗ 20] present a novel approach to segment the roof planes from airborne LiDAR point clouds using hierarchical clustering and boundary relabeling. Ywata et al [YDSdO21] introduce a method to extract building roof boundaries in object space by integrating a high‐resolution aerial images stereo pair and three‐dimensional roof models reconstructed from LiDAR data. In [AAT19; AAH20], they work on a deep learning‐based approach to detect and reconstruct roof parts of buildings from a single image.…”

Robust Building Detection in Urban Environments from Airborne LiDAR Data: A Geometry-Based Approach

“…The use of training data can be considered as a limitation in certain cases where manual labelling of the point cloud is required, representing a time-consuming task. Additionally, the differences in the data distribution between the training data and the experiment scenes impact the accuracy [15], [27].…”