Road Topology Refinement via a Multi-Conditional Generative Adversarial Network

Zhang, Yang; Li, Xiang; Zhang, Qianyu

doi:10.3390/s19051162

Cited by 14 publications

(6 citation statements)

References 45 publications

(101 reference statements)

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“…These images further highlight the effectiveness of the proposed GAN approach, which is particularly effective in preserving the edges of the roads while maintaining high fidelity with the ground truth labels. Also, we compared the performance of the proposed GAN+MUNet approach with other GAN-based road extraction approaches reported in the literature such as GAN+FCN [32], GAN+SegNet [21], E-WGAN [33], MsGAN [34], and McGAN [35] to test the efficacy of the presented model in road extraction. For comparison purpose, that the statistical measure such as the accuracy, recall, and F1 scores reported in the referenced papers vs. our proposed Prop-GAN approach are shown in Table 4.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…The main contribution of this research lies in proposing a GAN with a modified U-Net generative model to extract roads from high-resolution aerial imagery. Compared to prior GANbased road extraction approaches such as GAN+FCN proposed by [32], GAN+SegNet presented by [21], Ensemble Wasserstein Generative Adversarial Network (E-WGAN) proposed by [33], Multi-supervised Generative Adversarial Network (MsGAN) performed by [34], and Multi-conditional Generative Adversarial Network (McGAN) implemented by [35], we introduce the modified U-Net model (MUNet) for the generative term to create a high-resolution smooth segmentation map, with high spatial consistency and clear segmentation boundaries. The proposed model does not require high computational time and a large training dataset and still improves performance and addresses the aforementioned challenges for road extraction from remote sensing imagery.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving Road Semantic Segmentation Using Generative Adversarial Network

et al. 2021

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Road network extraction from remotely sensed imagery has become a powerful tool for updating geospatial databases, owing to the success of convolutional neural network (CNN) based deep learning semantic segmentation techniques combined with the high-resolution imagery that modern remote sensing provides. However, most CNN approaches cannot obtain high precision segmentation maps with rich details when processing highresolution remote sensing imagery. In this study, we propose a generative adversarial network (GAN)-based deep learning approach for road segmentation from high-resolution aerial imagery. In the generative part of the presented GAN approach, we use a modified UNet model (MUNet) to obtain a high-resolution segmentation map of the road network. In combination with simple pre-processing comprising edge-preserving filtering, the proposed approach offers a significant improvement in road network segmentation compared with prior approaches. In experiments conducted on the Massachusetts road image dataset, the proposed approach achieves 91.54% precision and 92.92% recall, which correspond to a Mathews correlation coefficient (MCC) of 91.13%, a Mean intersection over union (MIOU) of 87.43% and a F1-score of 92.20%. Comparisons demonstrate that the proposed GAN framework outperforms prior CNN-based approaches and is particularly effective in preserving edge information.

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Section: Comparison and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Road Semantic Segmentation Using Generative Adversarial Network

et al. 2021

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“…Costea et al [39] proposed a road extraction method composed of an edge detection phase with a GAN, and a later stage of smoothing to post-process the results and improve the initial segmentation predictions. Lastly, Zhang et al implemented a Multi-conditional GAN (McGAN) [40] to refine the road topology and obtain more complete road network graphs. Different from these works, we wanted to avoid focusing on small, ideal study areas and decided to build a new dataset containing 8480 tiles of 256 × 256 pixels containing roads from official cartography and their correspondent segmentation masks to add real world complexity to the generative task and carry out the experiments on a large scale.…”

Section: Related Workmentioning

confidence: 99%

Improving Road Surface Area Extraction via Semantic Segmentation with Conditional Generative Learning for Deep Inpainting Operations

Cira

Kada

Callejo

et al. 2022

IJGI

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The road surface area extraction task is generally carried out via semantic segmentation over remotely-sensed imagery. However, this supervised learning task is often costly as it requires remote sensing images labelled at the pixel level, and the results are not always satisfactory (presence of discontinuities, overlooked connection points, or isolated road segments). On the other hand, unsupervised learning does not require labelled data and can be employed for post-processing the geometries of geospatial objects extracted via semantic segmentation. In this work, we implement a conditional Generative Adversarial Network to reconstruct road geometries via deep inpainting procedures on a new dataset containing unlabelled road samples from challenging areas present in official cartographic support from Spain. The goal is to improve the initial road representations obtained with semantic segmentation models via generative learning. The performance of the model was evaluated on unseen data by conducting a metrical comparison where a maximum Intersection over Union (IoU) score improvement of 1.3% was observed when compared to the initial semantic segmentation result. Next, we evaluated the appropriateness of applying unsupervised generative learning using a qualitative perceptual validation to identify the strengths and weaknesses of the proposed method in very complex scenarios and gain a better intuition of the model’s behaviour when performing large-scale post-processing with generative learning and deep inpainting procedures and observed important improvements in the generated data.

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“…According to a large number of existing labeled datasets, object extraction determines the geographical features of roads, buildings, and water systems. The representative research works are as follows: Zhang et al [43] proposed a multi-conditional generation of adversative network reconstruction, aiming at the problems of fuzzy boundary and incomplete extraction results of existing image segmentation results. Marmanis et al [44] improve semantic segmentation quality by combining semantically informed edge detection, thus making class boundaries explicit in the model.…”

Section: Geographic Information Extraction Of Remote Sensing Images Based On Deep Learningmentioning

confidence: 99%

MapGen-GAN: A Fast Translator for Remote Sensing Image to Map Via Unsupervised Adversarial Learning

Song

Chen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Map is an essential medium for people to understand our changing planet. Recently, research on generating and updating maps through remote sensing images has been an important and challenging task in geographic information. Traditional methods for map generation are time-consuming and labor-intensive. Besides, most supervised learning methods for map generation lack labeled training samples. It is challenging to generate maps quickly and efficiently for emergency rescue operations such as earthquakes, fire disasters, or tsunami. In this paper, we propose an unsupervised domain mapping model based on adversarial learning called MapGen-GAN. MapGen-GAN is a generative adversarial network that can do end-to-end translation from remote sensing images to general map quickly, and trained with no human annotation data. In order to improve the fidelity and the geometry precision of generated maps, we employ circularity-consistency and geometrical-consistency constraints as a part of the loss function of the proposed model. And then, an improved residual block Unet is designed and adopted as the generator of MapGen-GAN to capture the geographic structure information of buildings, roads, and topography outlines under different resolutions in the map generation. By applying the proposed model to two distinct datasets, experiments demonstrate that our model can generate maps efficiently and quickly and outperform the state-of-the-art approaches.

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Road Topology Refinement via a Multi-Conditional Generative Adversarial Network

Cited by 14 publications

References 45 publications

Improving Road Semantic Segmentation Using Generative Adversarial Network

Improving Road Semantic Segmentation Using Generative Adversarial Network

Improving Road Surface Area Extraction via Semantic Segmentation with Conditional Generative Learning for Deep Inpainting Operations

MapGen-GAN: A Fast Translator for Remote Sensing Image to Map Via Unsupervised Adversarial Learning

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