Special Issue "Inland Transport Networks Monitoring from Remote Sensing and Photogrammetry"

A special issue of Remote Sensing (ISSN 2072-4292). 

Deadline for manuscript submissions: 31 March 2019

Special Issue Information

In the last few years there has been intense research activity regarding the exploitation of remote sensing technologies towards their adoption in civil engineering applications. In the transport sector, it is well known that transport network reliability is limited by infrastructure conditions, and consequently, knowledge about technologies and their effectiveness and costs for medium–long term monitoring are needed. Remote sensing (both terrestrial and satellite) has revealed to be a suitable approach to effectively collect data at a large scale, and with an accuracy level that inland transport networks demand. Data sources from remote sensing include: (i) satellite sensors, which provide coverage of large infrastructure with a high temporal resolution; (ii) terrestrial remote sensing technologies, which provide extremely high spatial resolution; and (iii) contact or embedded sensing that can enrich the information of the infrastructure. Data fusion together with image processing and machine learning approaches have allowed the automated modelling and interpretation of data to be integrated into the infrastructure management systems. The integration of infrastructure BIM with geospatial data, or more recently, the infrastructure information modelling (based in the well-known BIM logic) is also an emerging topic in full life-cycle infrastructure management.

This Special Issue aims at collecting new technologies, data collections and processing methodologies, and successful applications of remote sensing to inland transport monitoring. We welcome submissions which cover, but are not limited to:

  • Remote sensing technologies with potential for the monitoring of large infrastructures, including different platforms (e.g. ,Terrestrial, satellite, aerial, etc.).
  • Evaluation and integration of new 3D and 2D imaging sensors for the purpose of 3D mapping for environmental and infrastructure monitoring.
  • Automated data analysis of 3D data (segmentation, feature extraction, classification, etc.) for the massive inspection of large infrastructure networks. Specially, large-scale Machine Learning applications for transport infrastructure monitoring are envisaged.
  • InSAR applications for structural health monitoring of critical infrastructures, as well as successful applications in large areas such as other infrastructure (ports, airports, cities, etc.).
  • Use of 3D photogrammetric techniques for inspection and life cycle monitoring of infrastructures like bridges, buildings, dikes, and to improve on the integration with structural component analysis.
  • Infrastructure Information Modeling (e.g., BIM for construction projects in infrastructure, applications of existing standards: InfraGML, IFC ROAD, RoadBIM, LandInfra, etc.).
  • Crowdsourcing concepts for the real time inspection of inland transport networks including: exploitation of onboard sensors in connected vehicles, exploitation of social media infrastructure for the collaborative inventory/inspection of infrastructure assets, etc.
  • Generation and update of high-resolution 3D models and databases of road and railways, including mesh based, polyhedral, parametric and multiscale representations and (semantic) attributes. 
  • Performance evaluation of new artificial vision systems for the monitoring of dynamic processes associated to the mechanical or structural behaviour. Technologies and methods for improved deformation analysis in multiscale contexts.


Dr. Belén Riveiro, Dr. Mario Soilán, Dr. Sander Oude Elberink
Guest Editors


WG II/10








The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.

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