Pedro Arias

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COMPARATIVE STUDY OF ROAD AND URBAN OBJECT CLASSIFICATION BASED ON MOBILE LASER SCANNERS

The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences (2023)

Rabia Rashdi Jesús Balado J. Martínez-Sánchez Pedro Arias

Abstract. Recently, the rapid development of new laser technologies has led to the continuous evolution of mobile laser systems, resulting in even greater capabilities for transport infrastructure. However, the market offers numerous MLS systems with varying specifications for global navigation satellite systems (GNSS), inertial measurement units (IMU), and laser scanners, which can result in different accuracies, resolutions, and densities. In this regard, this paper aims to compare two different MLS system, integrated with different GNSS and IMU for mapping in road and urban environments. The study evaluates the performance of these sensors using different classifiers and neighborhood sizes to determine which sensor produces better results. Random forest was found to be the most suitable classifier with an overall accuracy of (91.81% for Optech and 94.38% for Riegl) in road environment and (86.39% for Optech and 84.21% for Riegl) in urban environment. In terms of MLS, Optech achieved the highest accuracy in the road environment, while Riegl obtained the highest accuracy in the urban environment. This study provides valuable insights into the most effective MLS systems and approaches for accurate mapping in road and urban infrastructure.

Mobile mapping

10.5194/isprs-archives-xlviii-1-w1-2023-423-2023

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Citations (3)

Transport Infrastructure Management Based on LiDAR Synthetic Data: A Deep Learning Approach with a ROADSENSE Simulator

Infrastructures (2024)

Lino Comesaña-Cebral J. Martínez-Sánchez Antón Núñez Seoane Pedro Arias

In the realm of transportation system management, various remote sensing techniques have proven instrumental in enhancing safety, mobility, and overall resilience. Among these techniques, Light Detection and Ranging (LiDAR) has emerged as a prevalent method for object detection, facilitating the comprehensive monitoring of environmental and infrastructure assets in transportation environments. Currently, the application of Artificial Intelligence (AI)-based methods, particularly in the domain of semantic segmentation of 3D LiDAR point clouds by Deep Learning (DL) models, is a powerful method for supporting the management of both infrastructure and vegetation in road environments. In this context, there is a lack of open labeled datasets that are suitable for training Deep Neural Networks (DNNs) in transportation scenarios, so, to fill this gap, we introduce ROADSENSE (Road and Scenic Environment Simulation), an open-access 3D scene simulator that generates synthetic datasets with labeled point clouds. We assess its functionality by adapting and training a state-of-the-art DL-based semantic classifier, PointNet++, with synthetic data generated by both ROADSENSE and the well-known HELIOS++ (HEildelberg LiDAR Operations Simulator). To evaluate the resulting trained models, we apply both DNNs on real point clouds and demonstrate their effectiveness in both roadway and forest environments. While the differences are minor, the best mean intersection over union (MIoU) values for highway and national roads are over 77%, which are obtained with the DNN trained on HELIOS++ point clouds, and the best classification performance in forested areas is over 92%, which is obtained with the model trained on ROADSENSE point clouds. This work contributes information on a valuable tool for advancing DL applications in transportation scenarios, offering insights and solutions for improved road and roadside management.

10.3390/infrastructures9030058

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Citations (0)

From laser point clouds to surfaces: Statistical nonparametric methods for three-dimensional reconstruction

Computer-Aided Design (2008)

Javier Roca‐Pardiñas Henrique Lorenzo Pedro Arias Julia Armesto

Smoothing

Laser Scanning

Regularization

Reverse engineering

Kernel (algebra)

10.1016/j.cad.2008.03.002

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Citations (45)

Terrestrial laser technology in sporting craft 3D modelling.

CompIMAGE (2006)

Pedro Arias Julia Armesto Henrique Lorenzo Celestino Ordóñez

Craft

Source

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Citations (1)

Performance testing of LiDAR exploitation software

Computers & Geosciences (2012)

M. Varela-González H. González-Jorge B. Riveiro Pedro Arias

10.1016/j.cageo.2012.12.001

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Citations (21)

Application of Close Range Photogrammetry to Deck Measurement in Recreational Ships

Sensors (2009)

Celestino Ordóñez B. Riveiro Pedro Arias Julia Armesto

In this article, we present results that demonstrate the utility of close range photogrammetry in the measurement of decks in recreational craft as an alternate measurement system to the one based on direct acquisition of coordinates. The areas of deck covered with teakwood for aesthetic or security reasons were measured. Both methods were compared in terms of precision of measurements, time consumption, equipment cost, and ease of manipulation and equipment transportation. Based on the results, we conclude that photogrammetry has advantages in almost every aspect with respect to the direct method. Consequently, photogrammetry is suggested as a suitable method for coordinate measurement of decks in recreational ships. However, in some special circumstances, where ships have wide corridors with few obstacles the direct method can be more appropriate than the photogrammetric method.

10.3390/s90906991

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Citations (19)

Methods for documenting historical agro-industrial buildings: a comparative study and a simple photogrammetric method

Journal of Cultural Heritage (2006)

Pedro Arias Celestino Ordóñez Henrique Lorenzo José Herráez

10.1016/j.culher.2006.09.002

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Citations (58)

A novel methodology for the structural assessment of stone arches based on geometric data by integration of photogrammetry and ground-penetrating radar

Engineering Structures (2012)

Mercedes Solla José Carlos Caamaño B. Riveiro Pedro Arias

Ground-Penetrating Radar

10.1016/j.engstruct.2011.11.004

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Citations (40)

Regional shares in commodity consumption growth, 2016-26

OECD agricultural outlook .../OECD-FAO agricultural outlook (2017)

Koen Deconinck Annelies Deuss Armelle Elasri Gen Furuhashi Hubertus Gay

The food and agriculture sector is faced with a critical global challenge: to ensure access to safe, healthy, and nutritious food for a growing world population, while at the same time using natural resources more sustainably and making an effective contribution to climate change adaptation and mitigation.Through this annual collaboration and other studies, the Organisation for Economic Co-operation and Development (OECD) and the Food and Agriculture Organization of the United Nations (FAO) are working together to provide information, analysis and advice, to help governments achieve these essential objectives.This is the 13th joint edition of the OECD-FAO Agricultural Outlook.It provides ten-year projections to 2026 for the major agricultural commodities, as well as for biofuels and fish.The pooling of market and policy information from experts in a wide range of participating countries provides a benchmark necessary for assessing the opportunities and threats to the sector.This year's Agricultural Outlook includes a special focus on Southeast Asia, a region where agriculture and fisheries have developed rapidly and undernourishment has been significantly decreased, but also a region that is on the front line of the effects of climate change and where there are rising pressures on natural resources.The Agricultural Outlook comes in the context of a wider set of international efforts to address food security and agricultural issues.Two global initiatives stand out:• The UN Sustainable Development Goals (SDGs) set ambitious targets to be achieved by 2030.Among these, the first goal is to end poverty in all its forms everywhere, while the second goalTThe Agricultural Outlook, 2017-2026, is a collaborative effort of the Organisation for Economic Co-operation and Development (OECD) and the Food and Agriculture Organization (FAO) of the United Nations.It brings together the commodity, policy and country expertise of both organisations and input from collaborating member countries to provide an annual assessment of prospects for the coming decade of national, regional and global agricultural commodity markets.The baseline projection is not a forecast about the future, but rather a plausible scenario based on specific assumptions regarding macroeconomic conditions, agriculture and trade policy settings, weather conditions, longer term productivity trends and international market developments.

Consumption

10.1787/agr_outlook-2017-graph3-en

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Citations (0)

PATH PLANNING FOR INDOOR CONTACT INSPECTION TASKS WITH UAVS

The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences (2020)

L. M. González-deSantos J. Martínez-Sánchez H. González-Jorge Pedro Arias

Abstract. UAV technology has become a useful tool for the inspection of infrastructures. Structural Health Monitoring methods are already implementing these vehicles to obtain information about the condition of the structure. Several systems based on close range remote sensing and contact sensors have been developed. In both cases, in order to perform autonomous missions in hard accessible areas or with obstacles, a path planning algorithm that calculates the trajectory to be followed by the UAV to navigate these areas is mandatory. This works presents a UAV path planning algorithm developed to navigate indoors and outdoors. This algorithm does not only calculate the waypoints of the path, but the orientation of the vehicle for each location. This algorithm will support a specific UAV-based contact inspection of vertical structures. The required input data consist of a point cloud of the environment, the initial position of the UAV and the target point of the structure where the contact inspection will be performed.

Position (finance)

10.5194/isprs-archives-xliii-b4-2020-345-2020

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Citations (2)