Dataset containing orthoimages tagged with road information covering approximately 8650 km2 of the Spanish territory (SROADEX)

Callejo, Miguel Ángel Manso; Cira, Calimanut-Ionut; González-Jiménez, Alicia; Querol-Pascual, José-Javier

doi:10.1016/j.dib.2022.108316

Data in Brief

2022

DOI: 10.1016/j.dib.2022.108316

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Dataset containing orthoimages tagged with road information covering approximately 8650 km2 of the Spanish territory (SROADEX)

Miguel Ángel Manso Callejo

Calimanut-Ionut Cira

Alicia González-Jiménez

et al.

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Cited by 5 publications

(1 citation statement)

References 7 publications

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“…A benchmark dataset (RoadArrowORIEN) was developed for predicting the orientation angle of road directional arrows, and the method applied to create it is described. The dataset can be used for training and evaluating the performance of future model implementations; it is hosted by the Zenodo repository [5] and can be downloaded under a CC-BY 4.0 licence.…”

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Convolutional Neural Networks Adapted for Regression Tasks: Predicting the Orientation of Straight Arrows on Marked Road Pavement Using Deep Learning and Rectified Orthophotography

Cira,

Díaz-Álvarez,

Serradilla

et al. 2023

Electronics

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Arrow signs found on roadway pavement are an important component of modern transportation systems. Given the rise in autonomous vehicles, public agencies are increasingly interested in accurately identifying and analysing detailed road pavement information to generate comprehensive road maps and decision support systems that can optimise traffic flow, enhance road safety, and provide complete official road cartographic support (that can be used in autonomous driving tasks). As arrow signs are a fundamental component of traffic guidance, this paper aims to present a novel deep learning-based approach to identify the orientation and direction of arrow signs on marked roadway pavements using high-resolution aerial orthoimages. The approach is based on convolutional neural network architectures (VGGNet, ResNet, Xception, and DenseNet) that are modified and adapted for regression tasks with a proposed learning structure, together with an ad hoc model, specially introduced for this task. Although the best-performing artificial neural network was based on VGGNet (VGG-19 variant), it only slightly surpassed the proposed ad hoc model in the average values of the R2 score, mean squared error, and angular error by 0.005, 0.001, and 0.036, respectively, using the training set (the ad hoc model delivered an average R2 score, mean squared error, and angular error of 0.9874, 0.001, and 2.516, respectively). Furthermore, the ad hoc model’s predictions using the test set were the most consistent (a standard deviation of the R2 score of 0.033 compared with the score of 0.042 achieved using VGG19), while being almost eight times more computationally efficient when compared with the VGG19 model (2,673,729 parameters vs VGG19′s 20,321,985 parameters).

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mentioning

confidence: 99%

Convolutional Neural Networks Adapted for Regression Tasks: Predicting the Orientation of Straight Arrows on Marked Road Pavement Using Deep Learning and Rectified Orthophotography

Cira,

Díaz-Álvarez,

Serradilla

et al. 2023

Electronics

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Dataset containing orthoimages tagged with road information covering approximately 8650 km2 of the Spanish territory (SROADEX)

et al. 2022

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State-Level Mapping of the Road Transport Network from Aerial Orthophotography: An End-to-End Road Extraction Solution Based on Deep Learning Models Trained for Recognition, Semantic Segmentation and Post-Processing with Conditional Generative Learning

et al. 2023

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Most existing road extraction approaches apply learning models based on semantic segmentation networks and consider reduced study areas, featuring favorable scenarios. In this work, an end-to-end processing strategy to extract the road surface areas from aerial orthoimages at the scale of the national territory is proposed. The road mapping solution is based on the consecutive execution of deep learning (DL) models trained for ① road recognition, ② semantic segmentation of road surface areas, and ③ post-processing of the initial predictions with conditional generative learning, within the same processing environment. The workflow also involves steps such as checking if the aerial image is found within the country’s borders, performing the three mentioned DL operations, applying a decision limit to the class predictions, or considering only the central 75% of the image to reduce prediction errors near the image boundaries. Applying the proposed road mapping solution translates to operations aimed at checking if the latest existing cartographic support (aerial orthophotos divided into tiles of 256 × 256 pixels) contains the continuous geospatial element, to obtain a linear approximation of its geometry using supervised learning, and to improve the initial semantic segmentation results with post-processing based on image-to-image translation. The proposed approach was implemented and tested on the openly available benchmarking SROADEX dataset (containing more than 527,000 tiles covering approximately 8650 km2 of the Spanish territory) and delivered a maximum increase in performance metrics of 10.6% on unseen, testing data. The predictions on new areas displayed clearly higher quality when compared to existing state-of-the-art implementations trained for the same task.

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Dataset containing orthoimages tagged with road information covering approximately 8650 km2 of the Spanish territory (SROADEX)

Cited by 5 publications

References 7 publications

Convolutional Neural Networks Adapted for Regression Tasks: Predicting the Orientation of Straight Arrows on Marked Road Pavement Using Deep Learning and Rectified Orthophotography

Convolutional Neural Networks Adapted for Regression Tasks: Predicting the Orientation of Straight Arrows on Marked Road Pavement Using Deep Learning and Rectified Orthophotography

Dataset containing orthoimages tagged with road information covering approximately 8650 km2 of the Spanish territory (SROADEX)

State-Level Mapping of the Road Transport Network from Aerial Orthophotography: An End-to-End Road Extraction Solution Based on Deep Learning Models Trained for Recognition, Semantic Segmentation and Post-Processing with Conditional Generative Learning

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