A Deep Learning-Based Framework for Automated Extraction of Building Footprint Polygons from Very High-Resolution Aerial Imagery
Abstract
:1. Introduction
2. Methodology
2.1. Overview
2.2. Building Segmentation
2.3. Building Object Localization
2.4. Corner Detection
2.5. Polygon Construction
3. Experiment Setup
3.1. Dataset
3.2. Implementation Details
3.3. Comparative Methods
3.4. Ablation Studies
3.5. Evaluation Metrics
4. Results
4.1. Results on WHU Dataset
4.2. Results on Vaihingen Dataset
4.3. Ablation Studies
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | Scene 1 | Scene 2 | Scene 3 | Scene 4 | Scene 5 | Test Dataset | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Precision | Recall | IoU | Precision | Recall | IoU | Precision | Recall | IoU | Precision | Recall | IoU | Precision | Recall | IoU | Precision | Recall | IoU | |
FCN | 0.941 | 0.941 | 0.889 | 0.925 | 0.921 | 0.857 | 0.918 | 0.942 | 0.868 | 0.942 | 0.917 | 0.868 | 0.938 | 0.909 | 0.858 | 0.932 | 0.897 | 0.841 |
SegNet | 0.941 | 0.959 | 0.905 | 0.887 | 0.957 | 0.853 | 0.898 | 0.957 | 0.864 | 0.951 | 0.941 | 0.898 | 0.922 | 0.940 | 0.870 | 0.911 | 0.925 | 0.848 |
U-Net | 0.945 | 0.970 | 0.917 | 0.908 | 0.956 | 0.872 | 0.907 | 0.964 | 0.879 | 0.947 | 0.949 | 0.902 | 0.933 | 0.954 | 0.893 | 0.917 | 0.933 | 0.861 |
DLEBFP | 0.959 | 0.970 | 0.932 | 0.943 | 0.939 | 0.886 | 0.935 | 0.954 | 0.895 | 0.950 | 0.941 | 0.896 | 0.932 | 0.949 | 0.887 | 0.926 | 0.914 | 0.851 |
Method | IoU Based on the Raster Data | IoU Based on the Vector Data | Changing Rate | Number of Reference Buildings | Number of Extracted Buildings | Number of Reference Vertices | Number of Extracted Vertices | VertexF0.5 | VertexF1.0 |
---|---|---|---|---|---|---|---|---|---|
DLEBFP | 0.851 | 0.850 | 0.1% | 15,675 | 14,687 | 134,246 | 135,623 | 0.668 | 0.744 |
U-Net | 0.861 | 0.858 | 0.3% | 15,675 | 18,302 | 134,246 | 7,990,152 | 0.022 | 0.025 |
U-Net + Douglas–Peucker (d = 0.1 m) | 0.861 | 0.858 | 0.3% | 15,675 | 18,302 | 134,246 | 1,138,969 | 0.114 | 0.134 |
U-Net + Douglas–Peucker (d = 0.5 m) | 0.861 | 0.851 | 1.0% | 15,675 | 18,302 | 134,246 | 278,541 | 0.229 | 0.309 |
U-Net + Douglas–Peucker (d = 1.0 m) | 0.861 | 0.840 | 2.1% | 15,675 | 18,302 | 134,246 | 250,132 | 0.216 | 0.295 |
Model | Precision | Recall | IoU | VertexF0.5 | VertexF1.0 |
---|---|---|---|---|---|
FCN | 0.883 | 0.950 | 0.844 | 0.024 | 0.040 |
SegNet | 0.910 | 0.932 | 0.854 | 0.023 | 0.030 |
U-Net | 0.932 | 0.942 | 0.881 | 0.037 | 0.043 |
U-Net + DP0.1 | 0.932 | 0.941 | 0.879 | 0.198 | 0.245 |
U-Net + DP0.5 | 0.936 | 0.929 | 0.874 | 0.404 | 0.555 |
U-Net + DP1.0 | 0.938 | 0.917 | 0.865 | 0.417 | 0.562 |
DLEBFP | 0.947 | 0.922 | 0.876 | 0.731 | 0.782 |
Model | Scene 1 | Scene 2 | Scene 3 | Scene 4 | Scene 5 | Test Dataset | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Precision | Recall | IoU | Precision | Recall | IoU | Precision | Recall | IoU | Precision | Recall | IoU | Precision | Recall | IoU | Precision | Recall | IoU | |
Baseline | 0.885 | 0.776 | 0.705 | 0.891 | 0.771 | 0.704 | 0.875 | 0.792 | 0.712 | 0.869 | 0.702 | 0.635 | 0.906 | 0.817 | 0.753 | 0.882 | 0.736 | 0.670 |
Baseline + BB | 0.958 | 0.737 | 0.714 | 0.943 | 0.743 | 0.711 | 0.926 | 0.736 | 0.694 | 0.941 | 0.653 | 0.627 | 0.927 | 0.790 | 0.743 | 0.918 | 0.706 | 0.664 |
Baseline + VC | 0.914 | 0.954 | 0.877 | 0.911 | 0.890 | 0.820 | 0.909 | 0.954 | 0.871 | 0.896 | 0.931 | 0.840 | 0.925 | 0.946 | 0.879 | 0.905 | 0.911 | 0.832 |
Baseline + BB + VC | 0.959 | 0.970 | 0.932 | 0.943 | 0.939 | 0.886 | 0.935 | 0.954 | 0.895 | 0.950 | 0.941 | 0.896 | 0.931 | 0.949 | 0.887 | 0.926 | 0.914 | 0.851 |
Model | Inference Time (ms) | Post-Processing Time (ms) | File Size (MB) |
---|---|---|---|
FCN | 153.11 | 11.29 | 95.6 |
SegNet | 131.43 | 21.22 | 129.7 |
U-Net | 185.64 | 37.94 | 141.8 |
U-Net + DP0.1 | 185.64 | 141.37 | 26.4 |
U-Net + DP0.5 | 185.64 | 135.95 | 10.9 |
U-Net + DP1.0 | 185.64 | 135.05 | 9.9 |
DLEBFP | 523.49 | 351.52 | 3.3 |
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Li, Z.; Xin, Q.; Sun, Y.; Cao, M. A Deep Learning-Based Framework for Automated Extraction of Building Footprint Polygons from Very High-Resolution Aerial Imagery. Remote Sens. 2021, 13, 3630. https://doi.org/10.3390/rs13183630
Li Z, Xin Q, Sun Y, Cao M. A Deep Learning-Based Framework for Automated Extraction of Building Footprint Polygons from Very High-Resolution Aerial Imagery. Remote Sensing. 2021; 13(18):3630. https://doi.org/10.3390/rs13183630
Chicago/Turabian StyleLi, Ziming, Qinchuan Xin, Ying Sun, and Mengying Cao. 2021. "A Deep Learning-Based Framework for Automated Extraction of Building Footprint Polygons from Very High-Resolution Aerial Imagery" Remote Sensing 13, no. 18: 3630. https://doi.org/10.3390/rs13183630