We offer a position as an

Early Stage Researcher (M/F/D)

in the Marie Skłodowska-Curie Innovative Training Network Project MENELAOS^NT in the field

Civil works, operation and maintenance of urban infrastructure (ESR13)

Intelligent Infrastructure based on digitalization and Big Data solutions for smart mobility. The key factor for smart infrastructures is the predictive maintenance based on connected and digitalized infrastructures. This project will focus on the data measured by low-cost mobile devices for the continuous monitoring and digitalizing for different transport infrastructures and cities. The mobile devices will integrate sensors with and without compressed sensing strategies of different nature for the acquisition of georeferenced data. Several sensors will be used; image and LiDAR sensors, with and without compressed sensing, among others. So, the specific objective of this project will be the automatic compressed data processing for the monitoring and extraction of relevant semantic information existing in the transport infrastructure, which will be used as base to build a Digital Twin and the BIM for infrastructure.

The challenge of the project is focused on the monitoring with mobile devices moving on non-dedicated vehicles, to digitalize the most relevant linear components of transport infrastructures and detect any changes in them. This information should be integrated on BIM for Infrastructure models specifically designed and will be the performance base of the Digital Twin appropriate for predictive maintenance purposes.

This challenge will allow to develop applications for SmartMobility, SmartLogistics, SmartAsset and SmartAdmin of linear structures and cities. The data will be acquired from different kind of low-cost sensors (GNSS, inertial units, LiDAR, RGB, thermographic y multispectral images). These devices will be mounted on non-dedicated mobile vehicles, that regularly circulate through the area under study (buses, taxis, trams, dust carts, maintenance vehicles).

The development addressed to the transport infrastructures (railway, road and urban space) is looking for the automatic way to detect and identify relevant information as geometry (positioning and size) and semantic (materials, presence of structural pathologies, thermal and structural properties if known). The works will address mainly linear elements, such as lanes, lane lines, shoulders, curbs and barriers, or rails, catenaries and cables along, so as traffic signals, or lights inventory as well.

Photogrammetry provides high-quality, high-definition images of the surveyed areas. Light-detection-and-ranging (LIDAR) technology is much faster than conventional technologies and provides high-quality cloud points. The project aims will be to process acquired data based on Big Data and heuristic techniques for the case of standard sensors, and to solve inference problems in signal processing from compressed measurements without full signal reconstruction. Results will imply the detection and classification of linear elements, so as any detected change.

At the end of the project, the automatic Digital Twin or BIM for infrastructure model, should allow to analyze the performance, carry out a predictive analysis, test scenarios and review the planned maintenance of the linear structures. So, this should arise as key component of any Intelligent Infrastructure systems mainly for Smart Mobility and Smart Logistics.

Mobile platforms that will be used to carry out test and validation:

• Car or Van with a high-end navigation system
• Railway dresin
• UAV platform
• Portable system based on simultaneous localization and mapping (SLAM)

The successful candidate will be employed for a maximum period of three years full-time equivalent and receives a generous financial package plus an additional mobility and family allowance according to the rules for Early Stage Researchers (ESRs) in an EU Marie Sklodowska-Curie Actions Innovative Training Networks (ITN). A career development plan will be prepared for each fellow in accordance with his/her supervisor and will include training, planned secondments and outreach activities in partner institutions of the network. The ESR fellows are supposed to complete their PhD thesis by the end of the 3rd year of their employment. For more information please visit the Marie Sklodowska-Curie Actions Innovative Training Networks website.

 YOUR TASKS

• Select the sensors and study the appropriateness of the CS approach for each platform.
• Investigate and implement solutions for integration and fusion of sensor data and information, including geometric calibration and radiometric characterization to obtain a CS signatur.e
• Study and develop applications for CS validation in operating environments and particularly for urban asset modelling, indoor mapping and SLAM and forestry management.
• Validate implemented solutions using the present technologies at INSITU as a ground truth.

PROFILE

• A Master of Science in Computer Science Science is required. It could comprise to the full range of mathematical, physical, engineering and technology disciplines related to sensor data acquisition and programming.
• ESRs must demonstrate that their English proficiency is in both written and spoken English sufficiently high for them to derive the full benefit from the network training. Spanish recommendable.
• Previous experience with LiDAR and RGB image processing, sensor fusion, Compressive Sensing or real time processing will be valued.
• Knowledge of C++, Python, Machine/Deep Learning developments, deep background of Neuronal Networks such as KERAS, or Tensorflow libraries, specific Object Detection Systems like YOLO, SSD and Faster R-CNN

PLANNED SECONDMENTS

• CEOSpaceTech, Bucharest, Romania, Dr. D. Coltuc, 6 months, implementation and evaluation of data mining algorithms.
• CITIUS, Santiago de Compostela, Spain, Dr. P. López Martínez, 4 months, evaluate AIC feasibility of the solutions.