Ellingwood (2005) was an early proponent of bringing the science of remote sensing to the field of infrastructure management and specifically discussed the benefits for condition assessment of civil infrastructure. Ellingwood (2005) emphasized that decisions about maintenance, rehabilitation, and continued use of infrastructure need to be supported by quantitative evidence about factors such as structural strength or stiffness deterioration, and that remote sensing technology can provide information to supplement engineering judgment. Hausamann et al. (2005) also recognized early on the value of remote sensing for pipeline infrastructure, a sector that was an early adopter of the technology for operational condition and threat assessment. Fekete et al. (2015) discuss the value of remote sensing for critical infrastructure disaster risk governance in terms of how it helps with multiple stages of the disaster management cycle: mitigation, preparedness, response, and recovery. Fekete et al. (2015)emphasize the importance of having a time series of remotely sensed data such that pre- and post-disaster conditions can be compared. Gomez and Purdie (2016) suggest that, in the context of natural disasters, society expects critical infrastructure to be immediately available, which puts a lot of pressure on emergency and continuity managers, who would benefit from rapid collection of high-resolution geospatial data provided by remote sensing.
Schweizer et al. (2018) argue that while we can’t likely improve our ability to predict the timing and impact of incidents, we can improve our response and preparation and suggest that remote sensing is uniquely valuable since it enables safe access to critical infrastructure post-event and provides a spatially comprehensive, synoptic view. Schweizer et al. (2018) propose, though, that to maximize the unique abilities, rapid deployment is required and for this to happen, people need to have already established their competence and invested in the systems and processes; only then will people be able to rapidly process and disseminate appropriate remote sensing products for efficient and informed decision-making. Stow et al. (2018) identify the factors that hyper-critical infrastructure owners must understand to maximize the benefit of remote sensing: 1. the elements of the system that are indeed critical; 2. the probable failure modes and how hazards and threats will impact the critical infrastructure; and 3. the types of decisions that need to be made and how remote sensing results can inform those decisions.
Singhroy (2020) provides an overview and introduction to current applications of remote sensing of infrastructure with a focus on monitoring deformation of transportation, energy, and mining infrastructure with radar, multispectral and hyperspectral optical, thermal-infrared, and LiDAR (Light Detection and Ranging). Singhroy (2020) covers the increased use of remote sensing to monitor infrastructure and provides examples of the technology being a powerful tool because of its increasingly high spatial and temporal resolution and ease of integration with other data to improve infrastructure monitoring. There is a recurring theme in Singhroy (2020) of the value of remote sensing in its ability to aggregate cascading hazards that could interact to magnify a disaster, something that individual discrete ground-based measurements and observations might not be able to do as well.
2.1 UAV-based Remote Sensing
Kerle et al. (2019) observe that one of the fastest growing UAV-based remote sensing applications is critical infrastructure monitoring for early warning of vulnerabilities, noting the growth in use for roads, bridges, and tunnels. Neocleous et al. (2016) refer to the challenges of corrosion of coastal urban structures and examined UAV-based remote sensing for its ability to provide early warning for deterioration of reinforced concrete of buildings. Sharma’s book (2019) of case studies demonstrate the use of UAV-based remote sensing for a range of applications, including infrastructure, and Shakhatreh et al. (2019) review the many uses for civil applications, including real-time monitoring, search and rescue, delivery of goods, precision agriculture, and remote sensing for civil infrastructure inspection. Politis et al. (2020)conducted a network-level pavement condition assessment using UAV-based remote sensing to build an index to score overall condition of road network segments. An application of UAV-based remote sensing discussed in Singhroy (2020) was for rail network change detection over time with repetitive, flexible, and inexpensive data acquisition to monitor the speed and acceleration of ground movement, which enabled early identification of displacement-related risk. Pulella and Sica (2021) conducted a study of airport situational awareness with UAV-based remote sensing noting the value of spatial-temporal data to provide insights regarding airport infrastructure condition.
Besada et al. (2018) discuss the continuous market growth of UAV usage due to cost reductions and familiarity with the value of remote sensing, but highlight how UAV-based infrastructure inspection, maintenance, and security applications are complex and challenging. Regardless, because of the increased popularity and demand, more UAV-based remote sensing tools and systems are available to help define the mission scope, select appropriate sensors, and support the integration of the remotely sensed data with other relevant data in a geographic information system (Besada et al. 2021). Notwithstanding the complex mission planning and execution, UAV-based remote sensing provides high resolution data and flexibility for damage mapping and has evolved from simple visual descriptive overviews to more sophisticated sensors and data analysis with multi-temporal and multi-perspective methods (Kerle et al. 2019). Notably, a practical challenge highlighted in the literature is related to training or hiring staff with appropriate skills and experience to implement UAV-based remote sensing for infrastructure monitoring and assessment (Sharma, 2019). Colomina and Molina (2014) recognized this challenge early and discuss the importance of devoting time and expertise to UAV mission planning with respect to sensor choice, flight path and trajectory, wind conditions. Nagasawa et al. (2021) highlight the operational importance of regular practice and innovation with UAV mission planning because a delay is not acceptable when critical infrastructure is disrupted following an incident.
2.2 Remote Sensing of Port Infrastructure
There are very few references in the literature to the use of remote sensing of port infrastructure. Tang et al. (2020) evaluated UAV-based remote sensing for cranes used in a port environment for their flexible maneuverability with respect to assessing the vertical structures for cracks and rust. Both Tang et al. (2020) and Liu et al. (2020) reported that port cranes are good candidates for UAV-based remote assessment because cranes are critical to service and vulnerable to the huge loads, heavy use, and aggressive coastal conditions related to wind, salt, and seawater dynamics; furthermore, both report that remote sensing is attractive since traditional assessment is dangerous, time consuming, and labour-intensive.
Jofre-Briceno et al. (2021) noted that facility management and maintenance in port environments can be difficult due to the harsh coastal dynamics and progressive and variable deterioration of the infrastructure, so they proposed a method for creating a port infrastructure tool that is able to incorporate digital surveys collected via UAV for a baseline elevation model. Alshammari and Mohammed (2022) used UAV-based remote sensing to monitor deformation and stability of a breakwater protecting harbour entry and exit channels and determined that the technology provides crucial information for early warning of vulnerabilities; they also noted that the technology is helpful for gaining a better understanding of failure mechanisms, especially in the context of changing ocean conditions with climate change.
Yang (2019) surveyed port sector executives regarding their intention to use UAV technology for maritime transportation applications and conducted a factor analysis to identify the top expected applications as environmental issue detection and delivery of goods to offshore ships. Poling (2021) provides a comprehensive overview of remote sensing for marine shipping activity surveillance and activity detection.
2.3 Remote Sensing of Bridge Infrastructure
The paucity of work published related to remote sensing of port infrastructure indicates that it is an underdeveloped area of application. Bridges, however, have been a prominent focus for development of methodologies, selection of appropriate sensors, and experimentation with data integration and analysis. Endsley et al. (2012) discussed the value of UAV-based remote sensing for asset management and structural health monitoring of bridges to evaluate indicators for early warning and diagnosis. Harris et al. (2016) suggested that bridge conditions could be assessed more frequently, comprehensively, and with less service interruption by fusing remotely sensed measurements from multiple sources with traditional surveys. Harris et al. (2016) noted that some sensors, such as thermal-infrared, radar and acoustic, can assess the internal condition of bridge components, such as the location of rebar deterioration and corrosion, deformation, and delamination, that are not visible in traditional surveys. Omar and Nehdi (2017) and Mac et al. (2019) further examined thermal-infrared technology for bridge condition assessment via UAV and noted that delamination due to the corrosion effect of steel rebar in reinforced concrete is a dangerous form of deterioration because it is not usually visible, so this type of remote sensing gives early warning and enables proactive maintenance to address infrastructure vulnerability. Mac et al. (2019) determined that cement delamination up to 4 cm below the surface was detectable using UAV-based remote sensing.
In their paper published on crack inspection using UAV-based remote sensing, Ayele et al. (2020) concluded that the technology reduces time and cost of bridge infrastructure inspection. Feroz and Abu Dabous (2021) reviewed 65 journal and conference papers on remote sensing of bridge infrastructure and summarized specific methodologies and sensors used for bridge condition assessment. Aliyari et al. (2022) focused on the use of UAV remote sensing for hazard identification and risk assessment for bridge inspections. Cusson et al. (2021) explored satellite-based remote sensing of infrastructure using Interferometric Synthetic Aperture Radar (InSAR) and examined integration of the data for 3D visualization and early warning of unexpected bridge displacements. The authors also studied the concept of thermal sensitivity and its use to monitor bridge behavior for early warning of abnormal bridge displacements to help avoid failures (Cusson et al. 2021).
In their investigations of the value of radar remote sensing for monitoring bridge infrastructure, Cusson et al. (2021) suggest that bridges have the benefit over other types of infrastructure of having well defined structures, materials, and designs. There are elements of port infrastructure that may be more heterogeneous than bridges, which could complicate remote sensing for condition assessment, especially as related to port foundation, moisture content, closeness to water body, and type of substrate. Structurally, however, ports are not dissimilar to bridges in that there is extensive and complex use of steel and concrete; it has been demonstrated with bridges that not only can remote sensing technology pinpoint defects like cracks in steel and fractures in concrete, but it can do so earlier than possible with visual inspections.