Improving Road Surface Area Extraction via Semantic Segmentation with Conditional Generative Learning for Deep Inpainting Operations
Abstract
:1. Introduction
- We implemented a cGAN model for the deep inpainting task to improve the initial semantic segmentation predictions of roads. We proposed generator, , and discriminator, , architectures in order to make the training better suited for our learning objective. is a U-Net [13]-like network, heavily modified for computational efficiency, while is a modified PatchGAN [14], adapted to process images of 256 × 256 pixels.
- We trained the model on a new dataset composed of real segmentation maps of roads present in official cartography. Here, we applied randomness in the form of synthetic gaps to the input for training (which will result in many possible corrupted images [15]). This source of randomness applied to the conditional information allows to generate realistic images. We validated the model on a new test set composed of real semantic segmentation predictions obtained by a state-of-the-art semantic segmentation network (with U-Net as base architecture and SEResNeXt50 [16] as segmentation backbone). We performed this operation at large-scale, with an intent to obtain a production model capable of successfully reducing human participation in the road extraction task.
- We studied the appropriateness of applying generative learning with inpainting operations for the task of road post-processing by evaluating the model’s ability in generating new samples from the learned domain and conducting metrical comparison and perceptual validation operations. The cGAN proposed achieved a maximum increase of 1.28% over the IoU score obtained by the semantic segmentation model.
2. Related Work
3. Problem Description
4. Data
5. cGAN for Post-Processing Road Predictions via Deep Inpainting Operations
5.1. Generator
5.2. Discriminator
5.3. Learning Process
6. Experiments and Analysis of the Results
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Performance Metric | (1) Best Performing Semantic Segmentation Model | (2) Thin-Structure-Inpainting [15] | (3) Our cGAN Implementation | ||||
---|---|---|---|---|---|---|---|
Average Result and Standard Deviation | Mean Percentage Difference (Initial Segmentation Results) | Maximum Result | Average Result and Standard Deviation | Mean Percentage Difference (Initial Segmentation Results) | Maximum Result | ||
IoU score (positive class) | 0.4100 | 0.4068 ± 0.0012 | −0.32% | 0.4088 | 0.4149 ± 0.0073 | +0.49% | 0.4252 |
IoU score (negative class) | 0.9352 | 0.9414 ± 0.0009 | +0.61% | 0.9412 | 0.9454 ± 0.0028 | +1.02% | 0.9484 |
IoU score | 0.6726 | 0.6741 ± 0.0008 | +0.15% | 0.6750 (+0.24%) | 0.6801 ± 0.0040 | +0.75% | 0.6854 (+1.28%) |
F1 score (positive class) | 0.5686 | 0.5638 ± 0.0012 | −0.48% | 0.5658 | 0.5714 ± 0.0082 | +0.28% | 0.5819 |
F1 score (negative class) | 0.9648 | 0.9692 ± 0.0005 | +0.44% | 0.9690 | 0.9711 ± 0.0016 | +0.63% | 0.9729 |
F1 score | 0.7667 | 0.7665 ± 0.0006 | −0.02% | 0.7674 | 0.7713 ± 0.0040 | +0.46% | 0.7765 |
Accuracy | 0.9379 | 0.9437 ± 0.0009 | +0.58% | 0.9448 | 0.9475 ± 0.0026 | +0.96% | 0.9503 |
Precision (positive class) | 0.4183 | 0.4247 ± 0.0019 | +0.64% | 0.4271 | 0.4546 ± 0.0187 | +3.63% | 0.4673 |
Precision (negative class) | 0.9976 | 0.9953 ± 0.0002 | −0.23% | 0.9953 | 0.9937 ± 0.0014 | −0.39% | 0.9953 |
Precision | 0.7080 | 0.7100 ± 0.0009 | +0.20% | 0.7112 | 0.7242 ± 0.0089 | +1.62% | 0.7302 |
Recall (positive class) | 0.9504 | 0.8908 ± 0.0062 | −5.96% | 0.8904 | 0.8376 ± 0.0459 | −11.28% | 0.8947 |
Recall (negative class) | 0.9372 | 0.9452 ± 0.0012 | +0.80% | 0.9452 | 0.9509 ± 0.0040 | +1.37% | 0.9558 |
Recall | 0.9438 | 0.9181 ± 0.0025 | −2.57% | 0.9178 | 0.8943 ± 0.0210 | −4.95% | 0.9205 |
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Cira, C.-I.; Kada, M.; Manso-Callejo, M.-Á.; Alcarria, R.; Bordel Sanchez, B. Improving Road Surface Area Extraction via Semantic Segmentation with Conditional Generative Learning for Deep Inpainting Operations. ISPRS Int. J. Geo-Inf. 2022, 11, 43. https://doi.org/10.3390/ijgi11010043
Cira C-I, Kada M, Manso-Callejo M-Á, Alcarria R, Bordel Sanchez B. Improving Road Surface Area Extraction via Semantic Segmentation with Conditional Generative Learning for Deep Inpainting Operations. ISPRS International Journal of Geo-Information. 2022; 11(1):43. https://doi.org/10.3390/ijgi11010043
Chicago/Turabian StyleCira, Calimanut-Ionut, Martin Kada, Miguel-Ángel Manso-Callejo, Ramón Alcarria, and Borja Bordel Sanchez. 2022. "Improving Road Surface Area Extraction via Semantic Segmentation with Conditional Generative Learning for Deep Inpainting Operations" ISPRS International Journal of Geo-Information 11, no. 1: 43. https://doi.org/10.3390/ijgi11010043
APA StyleCira, C. -I., Kada, M., Manso-Callejo, M. -Á., Alcarria, R., & Bordel Sanchez, B. (2022). Improving Road Surface Area Extraction via Semantic Segmentation with Conditional Generative Learning for Deep Inpainting Operations. ISPRS International Journal of Geo-Information, 11(1), 43. https://doi.org/10.3390/ijgi11010043