Digital Photogrammetry and Machine Learning for Infrastructure Inspection

Dear Colleagues,

Infrastructures, such as utility tower, wind turbines, oil or natural gas storages/pipelines, bridges, overpasses, underpasses, culverts, railways, roadways, airstrip, electrical grids, tunnel, dams, levees, telecom asset, and solar farms, are generally made using concrete with rebar, asphalt, rocks, steel, etc. Owing to environmental changes, such as earthquakes, temperature, wind, and humidity, their conditions may be degraded and cause deteriorations, such as concrete cracks, rusty, concrete spalling, damage, or even collapse. Infrastructure inspection is crucial for maintaining structure usage and safety conditions. It is necessary to detect and evaluate these defects for maintenance purposes. However, the conventional in-situ inspection procedure is expensive, time-consuming, and dangerous. Thus, an efficient and effective way to monitor these infrastructures frequently is necessary. Remote sensing techniques that utilize various sensors for this purpose have been developed in recent years. In this Special Issue, we would like to invite you to submit original research papers that cover all aspects of the advanced applications of digital photogrammetry and machine learning for infrastructure inspections. The images (visible or thermal) or point cloud data (laser ranging or depth sensor) may be collected from different platforms, such as UAV (drones), robots, or a mobile mapping system. The targets of inspection can be categorized into geometrical or thematic parts. Such as deformation measurement, defects detection, and recognition using object-based image analysis or deep/machine learning techniques. The deterioration types may cover cracks, spalling, efflorescence, rusty, etc. Specific topics of interest include, but are not limited to, the following:

• The design of data collection platforms for infrastructure inspection purposes, including the integration and calibration of different positioning/orientation/camera/laser ranging/depth sensors.
• UAV positioning and navigation sensors/algorithms under GNSS-denied environments, particularly under a bridge or tunnel.
• Auto/Semi-auto UAV mission planning algorithms or tools for bridge cracks inspection using a high-resolution camera.
• Positioning of defects with regard to the whole infrastructure or individual component.
• Machine/deep learning algorithms for the detection and recognition of different deterioration types.
• High-resolution deformation measurement sensors/algorithms/methods/workflow.
• Infrastructure damage detection and recognition.
• Road surface, pavement, or asphalt condition monitoring/assessment.
• Application of VR/AR for infrastructure management or assessment.
• Deep learning-based identification of defects for infrastructure health monitoring.

The following Special Issue is jointly organized between “Remote Sensing” and “Infrastructures” journals. Contributors are required to check the website below and follow the specific instructions for authors:
https://0-www-mdpi-com.brum.beds.ac.uk/journal/remotesensing/instructions
https://0-www-mdpi-com.brum.beds.ac.uk/journal/infrastructures/instructions 

You may choose our Joint Special Issue in Infrastructures.

Dr. Jiann-Yeou Rau
Dr. Jyun-Ping Jhan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Infrastructure inspection
• Digital photogrammetry
• Computer vision and image analysis
• Multi-sensor data fusion
• Deep learning/machine learning
• Mobile mapping systems
• UAV/UAS/drone/robotics
• Structure health monitoring