Graph Convolutional Networks for Road Networks

Jepsen, Tobias Skovgaard; Jensen, Christian S.; Nielsen, Thomas D.

doi:10.1145/3347146.3359094

Cited by 32 publications

(16 citation statements)

References 3 publications

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“…Although these models showed excellent performance in various domains such as information science, bibliometrics, water distribution systems, or biology [43,49,50,58], they can be further adapted to the specific context of each domain to produce better results. For instance, in [59], a novel type of GCN for road networks called Relational Fusion Network (RFN) is put forward for driving speed estimation and speed limit classification. The results indicate that RFN outperforms state-of-the-art GCN algorithms such as GraphSAGE in this application.…”

Section: Discussionmentioning

confidence: 99%

Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

Bel-Ghaddar

Chahinian

Seriai³

et al. 2021

Water

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Wastewater networks are mandatory for urbanisation. Their management, including the prediction and planning of repairs and expansion operations, requires precise information on their underground components (manhole covers, equipment, nodes, and pipes). However, due to their years of service and to the increasing number of maintenance operations they may have undergone over time, the attributes and characteristics associated with the various objects constituting a network are not all available at a given time. This is partly because (i) the multiple actors that carry out repairs and extensions are not necessarily the operators who ensure the continuous functioning of the network, and (ii) the undertaken changes are not properly tracked and reported. Therefore, databases related to wastewater networks may suffer from missing data. To overcome this problem, we aim to exploit the structure of wastewater networks in the learning process of machine learning approaches, using topology and the relationship between components, to complete the missing values of pipes. Our results show that Graph Convolutional Network (GCN) models yield better results than classical methods and represent a useful tool for missing data completion.

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Section: Discussionmentioning

confidence: 99%

Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

Bel-Ghaddar

Chahinian

Seriai³

et al. 2021

Water

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“…A preliminary four-page conference version of this paper presents the RFN-N+A variant [23]. The present version proposes an additional fusion function, INTERACTIONALFUSE, and an attentional aggregation function for RFNs.…”

Section: E Case Study: Danalienmentioning

confidence: 99%

Relational Fusion Networks: Graph Convolutional Networks for Road Networks

Jepsen

Jensen

Nielsen

2022

IEEE Trans. Intell. Transport. Syst.

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The application of machine learning techniques in the setting of road networks holds the potential to facilitate many important intelligent transportation applications. Graph Convolutional Networks (GCNs) are neural networks that are capable of leveraging the structure of a network. However, many implicit assumptions of GCNs do not apply to road networks.We introduce the Relational Fusion Network (RFN), a novel type of GCN designed specifically for road networks. In particular, we propose methods that substantially outperform state-ofthe-art GCNs on two machine learning tasks in road networks. Furthermore, we show that state-of-the-art GCNs may fail to effectively leverage road network structure and may not generalize well to other road networks. Manuscript received by ...

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“…More recently, the authors in [43] propose a multi-layer CRF (Conditional Random Field) model to perform hierarchical classification of street types into coarse and fine-grained classes. In [44] the authors propose a graph convolutional network based method for driving speed estimation of road segments. The output of this method could be used as an additional feature for models that classify road types.…”

Section: Extracting Roads From Aerial Imagesmentioning

confidence: 99%

OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

Muñoz

Srivastava

Tuia

et al. 2021

IEEE Geosci. Remote Sens. Mag.

105

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OpenStreetMap (OSM) is a community-based, freely available, editable map service that was created as an alternative to authoritative ones. Given that it is edited mainly by volunteers with different mapping skills, the completeness and quality of its annotations are heterogeneous across different geographical locations. Despite that, OSM has been widely used in several applications in Geosciences, Earth Observation and environmental sciences. In this work, we present a review of recent methods based on machine learning to improve and use OSM data. Such methods aim either 1) at improving the coverage and quality of OSM layers, typically using GIS and remote sensing technologies, or 2) at using the existing OSM layers to train models based on image data to serve applications like navigation or land use classification. We believe that OSM (as well as other sources of open land maps) can change the way we interpret remote sensing data and that the synergy with machine learning can scale participatory map making and its quality to the level needed to serve global and up-to-date land mapping. A preliminary version of this manuscript has been presented in the first authors dissertation [1].

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Graph Convolutional Networks for Road Networks

Cited by 32 publications

References 3 publications

Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

Graph Convolutional Networks: Application to Database Completion of Wastewater Networks

Relational Fusion Networks: Graph Convolutional Networks for Road Networks

OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

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