Earthquake: Satellites, sensors and algorithms for reconstruction

ENEA technology to characterize and manage rubble

Satellites, high resolution sensors, algorithms and machine learning techniques for post-earthquake reconstruction. Four years after the destructive earthquake which hit Central Italy, ENEA presents an innovative method that allows to characterize leftover rubble from  earthquakes and to quickly and cost-effectively assess type of materials and possible danger, as well as to locate them and estimate their extent and volumes.

Developed and tested by a multidisciplinary ENEA team of researchers on a representative sample of rubble from the historic center of Amatrice, this method combines remote sensing techniques based on data acquired from high-resolution aerial and satellite sensors, with in situ surveys for calibration of data acquired remotely.  The method is also replicable and adaptable to other contexts.

The method is described in a study published in the ISPRS International Journal of Geo-Information and was also presented at the International Conference on Computational Science and its Applications (ICCSA 2020).  In order to identify heaps of rubble and determine the extent of damage to buildings, the researchers used Sentinel-2 satellite data from the ESA's Copernicus Emergency Management (EMS) Program.

The geospatial analyses performed in a GIS environment, assisted by machine learning algorithms, made it possible to estimate both volumes and  main types of rubble like concrete (59%), natural bricks (9%) and other materials, including metal (8 %), and traces of asbestos (table 1; fig. 2 and 3).

"Leftover rubble from earthquakes and extreme events must be mapped and characterized in order to obtain crucial information for the optimal management of emergency activities and resolution of post-event problems", Sergio Cappucci at the ENEA  Sustainability of Production and Territorial Systems Department pointed out.

"The results obtained made it possible to characterize the main materials with an almost 90% accuracy and ascertain the presence of asbestos as well, in order to act in safe conditions and choose the most suitable procedures among reuse, disposal and removal ".

"In order to distinguish the materials present in the heaps, the best results were provided by the algorithm C-Support Vector Machine, which made it possible to recognize the main types (fig. 3 and 4) with a 88.8% accuracy and the algorithm Random Forest which made it possible to detect the presence of fragments of asbestos”, Maurizio Pollino at the ENEA Department of Energy Technologies and Renewable Sources, pointed out.

“The method, without excessive economic burdens, can be a replicable model also adaptable to other contexts and other types of extreme events”.

Furthermore, by putting together the results of the application of the methodology to the seismic microzonation map ¹ of the territory, the researchers created an actual "photograph" of the areas most at risk, useful for territorial planning and the safe reconstruction of areas affected by the earthquake. The outcomes of the activities will also make it possible to strengthen the Forecasting and Decision Support System and the operational base of the EISAC.it (European Infrastructure Simulation and Analysis Center), the first center in Europe for the security of strategic infrastructures, operated by ENEA and INGV: in case of extreme events, the system provides support to Civil Protection, Public Administrations and managers of critical networks in risk analysis and infrastructure protection activities, guaranteeing the continuity of essential services (communications, transport, electricity and water) and augmenting  their resilience.

For more information please contact:

Sergio Cappucci, ENEA – Sustainability of Production and Territorial Systems Department - sergio.cappucci@enea.it

Maurizio Pollino, ENEA – Department of Energy Technologies and Renewable Sources - maurizio.pollino@enea.it

Published articles:

1. CAPPUCCI S., DE CECCO L., GEMEREI F., GIORDANO L., MORETTI L., PELOSO A., POLLINO M. (2017). Earthquake’s rubble heaps volume evaluation: Expeditious approach through earth observation and geomatics techniques. In: Gervasi O. et al. (eds.) Computational Science and Its Applications – ICCSA 2017. Lecture Notes in Computer Science 10405: 261-277; https://doi.org/10.1007/978-3-319-62395-5_19
2. https://www.mdpi.com/2220-9964/9/4/262
   MAURIZIO POLLINO, SERGIO CAPPUCCI, LUDOVICA GIORDANO, DOMENICO IANTOSCA, LUIGI DE CECCO, DANILO BERSAN, VITTORIO ROSATO, FLAVIO BORFECCHIA (2020). Assessing Earthquake-induced Urban Rubble by means of multiplatform Remotely sensed data. International Journal of Geo Information, 9, 262; https://doi.org/10.3390/ijgi9040262
3. CAPPUCCI S., BUFFARINI G., GIORDANO L., HAILEMIKAEL S., MARTINI G., POLLINO M. (2020). https://doi.org/10.1007/978-3-030-58802-1_68

Figure 1. (a) Amatrice (RI); (b) WorldView-3 satellite image (Amatrice urban center inside the red box); (c) example of LiDAR point cloud and derived digital surface model (DSM); (d) Post-event 3-D rendering of the urban center of Amatrice; (e) example of delimitation of the heaps of rubble in the historic center of Amatrice

Figure 2 - analysis scheme of volumes of rubble

Figure 3 - (a)% presence of the main types of materials (cement, fired bricks, natural bricks, other) in rubble; (b)% of heaps in which the analysis indicates the presence of asbestos

Table 1 - Volumes and main constituent materials for a representative sample of heaps of rubble located in the historic center of Amatrice

Figure 4 - Estimate of the percentage distributions of the types of materials identified within the rubble heaps in the center of the city of Amatrice. The four maps refer respectively to: brick (a), other building materials (b), concrete debris (c) and natural brick (d).

Figure 5 - Degree of damage to buildings (in red those completely destroyed; in green those not damaged) superimposed on the Third Level Seismic Microzonation Map (with a period in the 0.1-0.5 s interval).

Figure 6 - Percentage of the surface occupied by the 5 classes of buildings that recorded the different degree of damage in the areas of the city of Amatrice with different seismic amplification factors (from FHa = 1.1-1.2 to FHa = 1.9-2.3)

The equations

¹ The map was created by the Seismic Microzonation Center which includes ENEA and other research bodies and universities. Seismic microzonation enables the "local seismic response" to be characterized on a detailed scale, i.e. the effect of amplification or damping of the intensity of an earthquake due to local geological, geomorphological and geotechnical conditions of the subsoil.