TERNformer: Topology-Enhanced Road Network Extraction by Exploring Local Connectivity

Wang, Bin; Liu, Qingjie; Hu, Zhenghui; Wang, Wei; Wang, Yunhong

doi:10.1109/tgrs.2023.3296717

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…This approach yielded more detailed segmentation results by enhancing the semantic information in feature maps. To address challenges posed by occlusion from trees and shadows, as well as complex topological structures, Wang et al proposed a topology-enhanced road network extraction method called TERNformer [38], which strengthens road network topologies by exploring local connectivity. Chen et al, capitalizing on the global context modeling capability of Swin Transformer and the local feature extraction prowess of ResNet, devised a novel dual-branch encoding block named CoSwin [39].…”

Section: Transformer-based Methods For Road Extractionmentioning

confidence: 99%

Global–Local Information Fusion Network for Road Extraction: Bridging the Gap in Accurate Road Segmentation in China

Wang,

Cai,

et al. 2023

Remote Sensing

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Road extraction is crucial in urban planning, rescue operations, and military applications. Compared to traditional methods, using deep learning for road extraction from remote sensing images has demonstrated unique advantages. However, previous convolutional neural networks (CNN)-based road extraction methods have had limited receptivity and failed to effectively capture long-distance road features. On the other hand, transformer-based methods have good global information-capturing capabilities, but face challenges in extracting road edge information. Additionally, existing excellent road extraction methods lack validation for the Chinese region. To address these issues, this paper proposes a novel road extraction model called the global–local information fusion network (GLNet). In this model, the global information extraction (GIE) module effectively integrates global contextual relationships, the local information extraction (LIE) module accurately captures road edge information, and the information fusion (IF) module combines the output features from both global and local branches to generate the final extraction results. Further, a series of experiments on two different Chinese road datasets with geographic robustness demonstrate that our model outperforms the state-of-the-art deep learning models for road extraction tasks in China. On the CHN6-CUG dataset, the overall accuracy (OA) and intersection over union (IoU) reach 97.49% and 63.27%, respectively, while on the RDCME dataset, OA and IoU reach 98.73% and 84.97%, respectively. These research results hold significant implications for road traffic, humanitarian rescue, and environmental monitoring, particularly in the context of the Chinese region.

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Section: Transformer-based Methods For Road Extractionmentioning

confidence: 99%

Global–Local Information Fusion Network for Road Extraction: Bridging the Gap in Accurate Road Segmentation in China

Wang,

Cai,

et al. 2023

Remote Sensing

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“…Road Graph Representations. There have been plenty of studies for vector road mapping, mainly relying on either the rasterized road map or the keypoint/vertex-based graph representations, and derived two categories, the segmentationbased (Máttyus, Luo, and Urtasun 2017;Zhou, Zhang, and Wu 2018;Mei et al 2021;Wang et al 2023;Batra et al 2019;Cheng et al 2021) and the keypoint-based approaches (He et al 2020;He, Garg, and Chowdhury 2022;Shit et al 2022;Yang et al 2023;Xie et al 2023). Regarding the popularity of end-to-end learning for better performance, the state-of-the-art approaches (He, Garg, and Chowdhury 2022;Xu et al 2023b) mainly learn keypoints (i.e., graph vertices) and the connectivity between vertices while using the rasterized road masks/maps as the additional supervision signals to enhance the feature representation ability of ConvNets.…”

Section: Related Workmentioning

confidence: 99%

Patched Line Segment Learning for Vector Road Mapping

Xu,

Xia

et al. 2024

AAAI

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This paper presents a novel approach to computing vector road maps from satellite remotely sensed images, building upon a well-defined Patched Line Segment (PaLiS) representation for road graphs that holds geometric significance. Unlike prevailing methods that derive road vector representations from satellite images using binary masks or keypoints, our method employs line segments. These segments not only convey road locations but also capture their orientations, making them a robust choice for representation. More precisely, given an input image, we divide it into non-overlapping patches and predict a suitable line segment within each patch. This strategy enables us to capture spatial and structural cues from these patch-based line segments, simplifying the process of constructing the road network graph without the necessity of additional neural networks for connectivity. In our experiments, we demonstrate how an effective representation of a road graph significantly enhances the performance of vector road mapping on established benchmarks, without requiring extensive modifications to the neural network architecture. Furthermore, our method achieves state-of-the-art performance with just 6 GPU hours of training, leading to a substantial 32-fold reduction in training costs in terms of GPU hours.

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CTMNet: Enhanced Open-Pit Mine Extraction and Change Detection With a Hybrid CNN-Transformer Multitask Network

Xing,

Zhang,

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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TERNformer: Topology-Enhanced Road Network Extraction by Exploring Local Connectivity

Cited by 3 publications

References 26 publications

Global–Local Information Fusion Network for Road Extraction: Bridging the Gap in Accurate Road Segmentation in China

Global–Local Information Fusion Network for Road Extraction: Bridging the Gap in Accurate Road Segmentation in China

Patched Line Segment Learning for Vector Road Mapping

CTMNet: Enhanced Open-Pit Mine Extraction and Change Detection With a Hybrid CNN-Transformer Multitask Network

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