UAVs to Save Cultural Heritage in Emergency Scenarios


Understanding the situation is a crucial objective for emergency services when designing systems and procedures for use during or after a disaster. Situation awareness encompasses various aspects and requires a continuous flow of information, ideally in real-time, from diverse data sources. This information feeds into systems that empower emergency managers to assess the situation and make informed decisions.

Within this context, research and end-users’ needs in the realm of Cultural Heritage protection aim for integrated systems. These systems should incorporate sensors and cutting-edge platforms to provide essential information about the conditions of artifacts and the damages they may incur due to natural or man-made factors. Following this strategy, diverse and dispersed data sources should communicate with the central system, creating a continuous flow of data and information through traditional internet channels.

During the 2016 Central Italy earthquake, drones have been extensively used by the Italian National Fire Service (CNVVF) to protect Cultural Heritage buildings. In particular, their use allowed firefighters to acquire important data about the conditions of the building without exposing themselves to risk of sudden structural collapse due to earthquakes. The image shows a typical night scenario of the earthquake. (image credits: CNVVF)

In this framework, the sensor infrastructure, utilizing UAVs for surveying, diagnosing, and monitoring open-space Cultural Heritage sites, becomes a vital component of a system. This system would require innovative technologies and approaches to recognize images captured by UAVs, coupled with models and techniques for information fusion.

The steel lattice built to protect the remaining parts of the San Benedetto church in Norcia from the collapse. Data needed to design it have been acquired using drones. (image credits: CNVVF)

Leveraging complex event processing techniques and technologies, the gathered information and/or inferred domain events are aggregated and correlated. This process aims to identify potential dangerous or critical situations, including the recognition, validation, and localization of signals and events that may necessitate monitoring, surveying, or warnings for disaster prevention, along with assessing the level of risk (Surveillance & Monitoring Services, Surveying & Diagnosis Services, Quick Damage Assessment Services). As a case study, we examine the 2016 earthquake in Central Italy.

In case of earthquake, buildings can pose severe safety problems when damaged. Drones allow acquire data from the inside or in normally not accessible parts without adding risks to firefighters (image credits: CNVVF)

During the 2016 earthquake in central Italy, there was a notable increase in the utilization of drones by Italian firefighters (CNVVF) from the early stages of the emergency. The purpose was to swiftly and comprehensively assess the extent of damage suffered by major historical and artistic buildings. This activity was part of new procedures adopted to secure buildings damaged in large-scale emergencies. The drones, equipped with instrumentation for photographic surveys, enabled the acquisition of gigabytes of high-resolution images of post-seismic event locations. Specifically, drone flights assisted in evaluating the damage to historic buildings and churches of great artistic importance, located in restricted or prohibited areas. The analysis of this data was crucial for assessing the risk of further collapses and designing effective shoring systems to support unstable structures.

The aerial photogrammetric data obtained through daily drone sorties were processed using specific input software for the rapid creation of 3D models. These models were then integrated with cadastral and geomorphological data, providing valuable support for understanding the operating environment where firefighting teams conducted search and rescue operations. Additionally, this post-processing facilitated a more accurate assessment of the damage and, consequently, an early cost estimate during the initial stages of the emergency.

While the accuracy of the obtained data (e.g., point clouds, surface models, and orthophotos) may not match that of other systems like LIDAR, it serves as a valuable rescue tool, offering a good evaluation of the severity of the scenario and estimating the time required for refurbishing primary infrastructure such as roads and electrical networks.

In this specific context, special units of the Italian Fire Corps (CNVVF) with expertise in topography during rescue operations scoured the quake-affected areas. The VHF radio network of the CNVVF, equipped with a GPS module and interfaced to specific software on tablets for tracking and geo-referencing, allowed them to create maps. These maps integrated information gathered from multiple sources, processed by GIS system experts, and transformed into shapefiles or other widely used formats on platforms such as Google Maps.

In scenarios involving the assessment and restoration of safety for historic or cultural buildings, research such as that conducted in the H2020 STORM project proves invaluable. The need for a rapid and secure assessment of damage to historical or cultural buildings prompted the extensive use of UAVs by the CNVVF in the 2016 earthquake. While UAV-captured images were beneficial for emergency tasks, their utility could be enhanced by comparing data detected by LIDAR before and after the disaster event. The STORM project’s pilot scenarios aim to integrate UAVs, LIDAR images, and shared procedures between cultural heritage managers and CNVVF, allowing them to assess damages caused by natural events with the highest possible resolution.

A paper concerning  the use of drones (STORM project and the use of UAV to improve emergency management of  disasters threatening cultural heritage), presented in the UAV&SAR2017 (Rome, 29th March, 2017) Workshop  can be downloaded here: STORM_UAVSAR

Leave a Reply

Your email address will not be published. Required fields are marked *