Periodic Reporting for period 1 - SPATRA (SPACE-BASED APPLICATIONS FOR TRANSPORT MONITORING AND MANAGEMENT)
Periodo di rendicontazione: 2024-01-01 al 2024-12-31
The SPATRA project aims at increasing the use of satellite data in land transport by developing two new downstream applications for monitoring and management of rail and road transport and demonstrating them in operational environment. Two pilot systems will be developed in the project, Road Infrastructure Monitoring Pilot (RPD) and RailWay monitoring and prediction of rail temperature zones and early warnings Pilot (RWP).
RPD is a space data-based service for monitoring and prediction of traffic congestions and parking spaces availability in border using satellite, UAV and in-situ monitoring data. The service will assist freight and logistics operators in better informed route planning and optimization, to avoid or mitigate congestion-caused delays.
RWP is a space-data based service for prediction of rail track temperature and for estimation of rail buckling risk using thermal and optical satellite data as well as environmental sensory data. RWP will assist rail infrastructure managers and rail track operators in addressing temperature related issues such as heat induced track buckling or maintenance disruptions.
The project brings together a consortium of technology leaders, research institutions and industry experts to develop and implement innovative satellite-based solutions. Through the innovative use of the European Global Navigation Satellite System (EGNSS) and Copernicus satellites, SPATRA aims to address critical challenges in the land transport sector, such as congestion, environmental impact and infrastructure monitoring, thus contributing to the objectives of the European Green Deal.
RPD is a space data-based service for monitoring and prediction of traffic congestions and parking spaces availability in border using satellite, UAV and in-situ monitoring data. The service will assist freight and logistics operators in better informed route planning and optimization, to avoid or mitigate congestion-caused delays.
RWP is a space-data based service for prediction of rail track temperature and for estimation of rail buckling risk using thermal and optical satellite data as well as environmental sensory data. RWP will assist rail infrastructure managers and rail track operators in addressing temperature related issues such as heat induced track buckling or maintenance disruptions.
The project brings together a consortium of technology leaders, research institutions and industry experts to develop and implement innovative satellite-based solutions. Through the innovative use of the European Global Navigation Satellite System (EGNSS) and Copernicus satellites, SPATRA aims to address critical challenges in the land transport sector, such as congestion, environmental impact and infrastructure monitoring, thus contributing to the objectives of the European Green Deal.
The main results achieved in the first reporting period involve formalisation of the functional and non-functional system requirements as well as development of use-cases and specification of the supported processes for both SPATRA pilot systems: Road Infrastructure Monitoring Pilot (RPD) and RailWay monitoring and prediction of rail temperature zones and early warnings Pilot (RWP).
The algorithms for RDP, machine learning-based algorithm to detect and track trucks in satellite images and machine learning and image vision algorithm for drones to improve monitoring operations of truck detection and traffic flow at the border, were initially developed and a solid basis for further algorithms improvement was built. Also, a system for in-truck EGNSS trackers to improve the accuracy of vehicle tracking and fleet management was developed, and initial satisfactory results were achieved.
Objectives for RWP for the first reporting period were achieved. A method for rail environment temperature profile map based on Earth Observation (EO) satellite data that provides inputs to machine learning-based rail tracks temperature prediction has been developed. The initial satisfactory results of ML-based rail track prediction as well as satisfactory initial results for ML-based estimation of rail buckling risk were achieved and a solid basis for further algorithms improvements was built.
The basis for SPATRA pilots’ demonstration and evaluation activities, which will be conducted in the second project reporting period was set-up, as the plans for both pilots, RDP and RWP, were defined. Also, the evaluation protocols and related KPIs according to which pilots will be evaluated in relevant environments were specified.
The algorithms for RDP, machine learning-based algorithm to detect and track trucks in satellite images and machine learning and image vision algorithm for drones to improve monitoring operations of truck detection and traffic flow at the border, were initially developed and a solid basis for further algorithms improvement was built. Also, a system for in-truck EGNSS trackers to improve the accuracy of vehicle tracking and fleet management was developed, and initial satisfactory results were achieved.
Objectives for RWP for the first reporting period were achieved. A method for rail environment temperature profile map based on Earth Observation (EO) satellite data that provides inputs to machine learning-based rail tracks temperature prediction has been developed. The initial satisfactory results of ML-based rail track prediction as well as satisfactory initial results for ML-based estimation of rail buckling risk were achieved and a solid basis for further algorithms improvements was built.
The basis for SPATRA pilots’ demonstration and evaluation activities, which will be conducted in the second project reporting period was set-up, as the plans for both pilots, RDP and RWP, were defined. Also, the evaluation protocols and related KPIs according to which pilots will be evaluated in relevant environments were specified.
SPATRA advances beyond the state of the art through a novel use of satellite data in land transport applications. Besides this general contribution, SPATRA innovative solutions will have potential ipact to each considered mode of land transport, road and rail.
In the road transport domain, SPATRA's innovative approach to logistics and traffic flow monitoring using satellite-based EO data has the potential to revolutionize the way we manage and optimize transportation networks. While acknowledging the limitations of real-time detection, the project will integrate other open data sources such
as weather patterns to develop more sophisticated classifiers that can predict congestion with higher accuracy. By providing prediction information on traffic flows and congestion based on Sentinel-2, assisted with EGNSS-based drones and location trackers, assisted with open data, SPATRA will be capable to detect and predict the traffic flow. The project will contribute to the field of smart mobility and logistics by developing practical solutions that can be implemented in the short term while laying the groundwork for future research and development, thereby improving traffic flow management and logistics operations through the power of satellite-based monitoring, drone imaging and open data sources. This will result ultimately with reduced economic costs associated with congestion and delays while also contributing to a more sustainable and environmentally friendly transportation system.
Regarding the rail workstream, current satellite-based imagery offers solutions primarily for vegetation intrusion detection, and natural hazard risk assessments, leading to reduced needs for “manual” onsite inspections. The later besides the economic advantage, cost reduction leads to safety increase and risk reduction to personnel, as no personnel walking along the railroad line for the purpose of monitoring is required. SPATRA proposes a novel service that will offer solution to problem caused by extreme temperatures that are consequences of climate changes. In this way, SPATRA will have great impact to railway safety in current climate circumstances and great economic impacts to rail industry as reducing the unnecessary delays and improving the management.
In the road transport domain, SPATRA's innovative approach to logistics and traffic flow monitoring using satellite-based EO data has the potential to revolutionize the way we manage and optimize transportation networks. While acknowledging the limitations of real-time detection, the project will integrate other open data sources such
as weather patterns to develop more sophisticated classifiers that can predict congestion with higher accuracy. By providing prediction information on traffic flows and congestion based on Sentinel-2, assisted with EGNSS-based drones and location trackers, assisted with open data, SPATRA will be capable to detect and predict the traffic flow. The project will contribute to the field of smart mobility and logistics by developing practical solutions that can be implemented in the short term while laying the groundwork for future research and development, thereby improving traffic flow management and logistics operations through the power of satellite-based monitoring, drone imaging and open data sources. This will result ultimately with reduced economic costs associated with congestion and delays while also contributing to a more sustainable and environmentally friendly transportation system.
Regarding the rail workstream, current satellite-based imagery offers solutions primarily for vegetation intrusion detection, and natural hazard risk assessments, leading to reduced needs for “manual” onsite inspections. The later besides the economic advantage, cost reduction leads to safety increase and risk reduction to personnel, as no personnel walking along the railroad line for the purpose of monitoring is required. SPATRA proposes a novel service that will offer solution to problem caused by extreme temperatures that are consequences of climate changes. In this way, SPATRA will have great impact to railway safety in current climate circumstances and great economic impacts to rail industry as reducing the unnecessary delays and improving the management.