Automatic Building Extraction via Adaptive Iterative Segmentation With LiDAR Data and High Spatial Resolution Imagery Fusion

Abstract

Extracting buildings from remotely sensed data is a fundamental task in many geospatial applications. However, this task is resistant to automation due to variability in building shapes and the environmental complexity surrounding buildings. To solve this problem, this article introduces a novel automatic building extraction method that integrates LiDAR data and high spatial resolution imagery using adaptive iterative segmentation and hierarchical overlay analysis based on data fusion. An adaptive iterative segmentation method overcomes over-and undersegmentation based on the globalized probability of boundary contour detection algorithm. A data-fusion-based hierarchical overlay analysis extracts building candidate regions based on segmentation results. A morphological operation optimizes a building candidate region to obtain final building results. Experiments were conducted on the international society for photogrammetry and remote sensing (ISPRS) Vaihingen benchmark dataset. The extracted building footprints were compared with those extracted using the state-ofthe-art methods. Evaluation results show that the proposed method achieved the highest area-based quality compared to results from the other tested methods on the ISPRS website. A detailed comparison with four state-of-the-art methods shows that the proposed method requiring no samples achieves competitive extraction results. Furthermore, the proposed method achieved a completeness of 94.1%, a correctness of 90.3%, and a quality of 85.5% over the whole Vaihingen dataset, indicating that the method is robust, with great potential in practical applications.

Building Extraction from Airborne LiDAR Data Based on Min-Cut and Improved Post-Processing

“…It is still a challenge for the proposed method to successfully extract some buildings attached by complex skylight or roof terrace due to rough points. A feasible solution is to combine images or intensity data to obtain extra features [6,10], which deserves further studies. In addition, there are several parameters that are adopted in the proposed method, which reduces the full automation of building extraction.…”

Automatic LIDAR building segmentation based on DGCNN and euclidean clustering

“…It happened when converting LIDAR data into DSM format. Based on several references, which uses the DSM format + deep learning such as [37][38][39]. These researchers used a combination of LIDAR data and High-Resolution aerial images.…”

“…Chen Shanxiong et al employed Adaptive Iterative Segmentation to extract the building [39]. They utilized LIDAR Data, which is in 3D-form to Digital Surface Model (2Dform).…”

Deep Learning-Based Building Extraction from Remote Sensing Images: A Comprehensive Review

“…With the rapid development of imaging technology, high-resolution remote sensing (RS) imagery is becoming more and more readily available. Therefore, research within the field of RS has flourished, and automatic building segmentation from high-resolution images has received widespread attention [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. The process of extracting buildings from RS images is shown in Figure 1, which is essentially a pixel-level classification of RS images to obtain binary images with contents of building or non-building, and this process can be modeled as a semantic segmentation problem [16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”