Preprint Review Version 2 Preserved in Portico This version is not peer-reviewed

Earthquakes Reconnaissance Data Sources, a Literature Review

Version 1 : Received: 28 June 2021 / Approved: 29 June 2021 / Online: 29 June 2021 (22:42:45 CEST)
Version 2 : Received: 1 October 2021 / Approved: 4 October 2021 / Online: 4 October 2021 (14:54:59 CEST)

How to cite: Contreras Mojica, D.M.; Wilkinson, S.; James, P. Earthquakes Reconnaissance Data Sources, a Literature Review. Preprints 2021, 2021060714 (doi: 10.20944/preprints202106.0714.v2). Contreras Mojica, D.M.; Wilkinson, S.; James, P. Earthquakes Reconnaissance Data Sources, a Literature Review. Preprints 2021, 2021060714 (doi: 10.20944/preprints202106.0714.v2).

Abstract

Earthquakes are one of the most catastrophic natural phenomena. After an earthquake, earthquake reconnaissance enables effective recovery by collecting building damage data and other impacts. This paper aims to identify state-of-the-art data sources for building damage assessment and provide guidance for more efficient data collection. We have reviewed 38 articles that indicate the sources used by different authors to collect data related to damage and post-disaster recovery progress after earthquakes between 2014 and 2021. The current data collection methods have been grouped into seven categories: fieldwork or ground surveys, omnidirectional imagery (OD), terrestrial laser scanning (TLS), remote sensing (RS), crowdsourcing platforms, social media (SM) and closed-circuit television videos (CCTV). The selection of a particular data source or collection technique for earthquake reconnaissance includes different criteria depending on what questions are to be answered by this data. We conclude that modern reconnaissance missions can not rely on a single data source and that different data sources should complement each other, validate collected data, or systematically quantify the damage. The recent increase in the number of crowdsourcing and SM platforms used to source earthquake reconnaissance data demonstrates that this is likely to become an increasingly important source of data.

Keywords

Earthquake reconnaissance; damage assessment; data sources; data collection; fieldwork surveys; closed-circuit television videos (CCTV); remote sensing (RS); crowdsourcing platforms; social media (SM)

Subject

ENGINEERING, Civil Engineering

Comments (1)

Comment 1
Received: 4 October 2021
Commenter: Diana Contreras
Commenter's Conflict of Interests: Author
Comment: Reviewer 1 
Thank you very much for your observations. You kindly spent time delving into our manuscript, and we are grateful. We have used a colour code to answer your questions.  This is a nice review paper about Earthquake reconnaissance. Indeed the authors made a good job describing the various procedures.Thank you very much for your opinion about our manuscript.   I have no major comments: the manuscript is fine as it is. Thank you very much for this statement.   However, for the sake of a more pleasant reading flow, they could put also some figures regarding at least some (not all) of the different methods. This can also provide a better understanding for non-experts, in each case. It is up to the authors to decide, but this is what I would personally do. 
Thank you very much for this suggestion. We also consider it necessary. 

Reviewer 2
Thank you very much for your observations. You kindly spent time delving into our manuscript, going carefully through each line and we are grateful for your dedication and contribution. We have used a colour code to answer your questions. Please find your comments in grey, and the respective answers in black. The corresponding paragraph in the paper is in blue . 

General comments
An interesting review, but needs moderate revision to sort out jumbled sections, cut repetition, and revise complicated technical details. Thank you very much for your opinion about our manuscript and your suggestions. We did our best to organise better the content of our manuscript based on your comments. Some images or graphic examples pulled from the reviews could make the reading more visually interesting. (considerations for citation/copyright need to be considered).We agree with the reviewer. Following your advice, we selected some images and requested the authors' permission (seven permissions already granted by authors) for inclusion in our manuscript. In the meanwhile, we created a schematic graph of the sources identified.
Needs a native English speaker to go through it.Thanks for your suggestion. The second and third authors are native speakers, and specifically, the second one went through the complete manuscript for proofreading. Needs the space between text and reference – many instances throughout the doc where this is missing.Thanks for this observation. We went again through the entire document and add spaces between text and references in lines 59, 166, 193, 205, 209, 279, 313, 329,  330, 373, 383, 406, 444, 474, 493, 505, 533, 606, 623, 647, 670, 700, 706, 734, 776, 783, 785, 792, 796, 800, 881, 952, 959, 971, 1015, 1020, 1082. ‘(…) early damage estimation [1].(…)’ ‘(…) These missions collect structural, geotechnical, seismological and damage information [2]. (…)’ ‘(…) after the 2017 Puebla-Morelos earthquake lasted three days [2]. (…)’ ‘(…) assessment within 48 hours after an earthquake [3]. (…)’ ‘(…) or why the failures occurred [2]. (…)’ ‘(…) and structural health and of affected buildings [4]. (…)’ ‘(…) (e.g.Damage grades 0-3 according to the European Macroseismic Scale-98) [5]. (…)’ ‘(…) satellite images such as Quickbird [6]. (…)’ ‘(…) Images are compared for change detection (CD) [7]. (…)’ ‘(…) but they require larger UAV platforms to carry them [8]. (…)’ ‘(…) The photos contain the data utilised to calculate inter-spatial intersections [9]. (…)’ ‘(…) The LiDAR approach has been used to estimate the damage after hurricanes [3] and earthquakes [10]. (…)’ ‘(…) the United Nations Operational SatelliteApplications Programme (UNOSAT) [11]. (…)’ ‘(…) and the first mission to deliver data on a near-real-time, open-access basis [12]. (…)’ ‘(…) days after the main shock [13]. (…)’ ‘(…) The Gorkah earthquake was mapped using InSAR and GPS data [14]. (…)’ ‘(…) the time variations of the phase [15]. (…)’ ‘(…) their smartphone is not in use [16]. (…)’ ‘(…) before any official information was released [16]. (…)’ ‘(…) (users reactions and experience on the ground) [17, 18]. (…)’ ‘(…) activated by multiple single-phone detections [19]. (…)’ ‘(…) the request form [20]. (…)’ ‘(…) the network is used for both sharing locally recorded data openly and teaching [20]. (…)’ ‘(…) for earthquake-triggered surface natural damages [21]. (…)’ ‘(…) Raspberry Shake sensors and ask interested schools to fill in the request form [20]. (…)’ ‘(…) residents using the LastQuake app [17]. (…)’ ‘(…) (STTM by its acronym in French) [22]. (…)’ ‘(…) do not comply with them [22]. (…)’ ‘(…) causes of the earthquakes [22]. (…)’ ‘(…) historical seismic data and safety measures to take [22]. (…)’ ‘(…) inspections can last for months [23]. (…)’ ‘(…) derived macroseismic datasets from DYFI [18]. (…)’ ‘(…) the support of the seismologist community [22]. (…)’ ‘(…) the Colorado floods [24]. (…)’ ‘(…) the significant ground surface changed detected by DPMs [8]. (…)’ ‘(…) manual inspections and UAV [8]. (…)’ ‘(…) These citizens as sensors [25]. (…)’  Acronyms – Full text on first occurrence and then acronym only after that – needs a thorough purgeThank you very much for reminding us of this rule. We went again through the complete manuscript. We made this time a list of acronyms (below), which is not included in the manuscript. We only repeat what the acronyms stand for after a period, but we do not include the acronym in these cases. Then we prefer that the sentence remains as it is. Advanced Land Observing Satellite-2 (ALOS-2) American Concrete Institute (ACI) American Society of Civil Engineers (ASCE) Applied Technology Council (ATC) Asian Technical Committee (ATC3) Building Safety Evaluation (BSE) Change detection (CD) Checkpoints (CPs) Closed-circuit television videos (CCTV) or Colegio de Ingenieros Civiles de Mexico (CICM) Community internet intensity maps (CIIM) Condition monitoring (CM) Critical infrastructures (CI) Damage prognosis (DP) Damage proxy maps (DPM) Department of Civil Protection (DPC) Did you feel it (DYFI) Digital elevation models (DEM) Digital single-lens reflex (DSLR) Early warning system (EEW) Earthquake damage Visualisation (EDV) Earthquake Engineering Field Investigation Team (EEFIT) Earthquake Engineering Research Institute (EERI) European Centre for Training and Research in Earthquake Engineering (EUCENTRE) European Mediterranean Seismological Centre (EMSC) Field of view (FOV) Firebase Cloud Messaging (FCM) French Geological Survey (BRGM’s by its acronym in French) Geographic information systems (GIS) Geotechnical Extreme Events Reconnaissance Association (GEER) Global positioning system (GPS) Gray Level Co-occurrence Matrix1 (GLCM) Ground control points (GCP) Horizontal transmit and Horizontal receive (HH), Horizontal transmit and Vertical receive (HV) Interferometric synthetic-aperture radar (InSAR) Internet Protocol (IP) Italian Network of University Laboratories for Earthquake Engineering (ReLUIS) Learning from Earthquakes United kingdom (LfE-UK) Light detection and ranging (LiDAR) Line-of-sight (LOS) Macroseismic intensity (MI) Main Frontal Thrust (MFT) Main Himalaya Thrust (MHT) Mayotte Earthquake Support Facebook group (STTM by its acronym in French) National Earthquake Information Centre (NEIC) New Zealand Society for Earthquake Engineering (NZSEE) Non-destructive evaluation (NDE), Object-based image analysis (OBIA) Omnidirectional imagery (OD) Rapid visual surveys (RVS) Raspberry Shake 1D (RS1D) Red, green and blue (RGB) Remotes sensing (RS) Scale-invariant feature transformation (SIFT) Signal-to-noise ratio (SNR) Small unmanned aerial vehicles (sUAV) Social media (SM) Statistical process control (SPC) Structural damage assessment (SDA) Structural health monitoring (SHM) Structure from Motion (SfM) Support vector machine (SVM) Synthetic aperture radar (SAR) Synthetic aperture radar (SAR) Taiwan Scientific Earthquake Reporting (TSER) Terrestrial laser scanning (TLS) Twitter Earthquake Detection (TED) United Kingdom (UK) United Nations Operational SatelliteApplications Programme (UNOSAT) United States (US) United States Geological Survey (USGS) Universidad Autonoma de Metropolitana (UAM) University College of London (UCL) Unmanned aerial vehicles (UAVs) Very high resolution (VHR) Virtual earthquake reconnaissance teams (VERT) Volunteer management system (VMS) World heritage (WH) Many of the more technical sections sound like the author is not really familiar with the technical aspects of the methodology – it is better to keep it simple and not just throw jargon down, or just lifting segments out of the reviewed texts. Some paragraphs need a thorough simplification (do we really need to know the algorithm names – better to know what it does) or better explanation. The reviewer is right in this observation. We are not particularly familiar with some remote sensing (RS) sources: SAR, InSAR and RAR. This fact made us challenging to explain in the manuscript. Then following your recommendation, we simplified their description according to our understanding; however, there are technical aspects that we do need to keep to explain the process of data acquisition. Please check the description of the methods already simplified:3.4.4. High-resolution synthetic aperture radar (SAR)This technique is a sort of radar used to generate two-dimensional images or three-dimensional reconstructions of landscapes. To generate a SAR image, successive pulses of radio waves are sent to ‘illuminate’ a target, and the echo of each pulse is received and recorded. The pulses are transmitted, and the echoes are received using a single beam-forming antenna, which location changes as the SAR device on board the spacecraft or aircraft moves [26]. Synthetic aperture radar is essential for obtaining disaster information given its strong penetrability through tree canopies, speediness, comprehensive coverage, and all-time/all-weather imaging capabilities. It is often used to detect not only building damage but also to monitor ground and structural deformations [27]. In 2010, after the earthquake in Haiti, heavily damaged urban areas were assessed using SAR intensity images and building footprints [28]. Pre- and post-event high-resolution SAR images and their characteristics are depicted in Figure 8. The backscattering coefficient between pre-and post-earthquake images changes more in collapsed buildings than in less damaged ones because the rubble has a stronger reflection. This analysis has managed to detect almost 75% of the damaged buildings correctly [12]. Figure 8. Pre- and post-event TerraSAR-X (TSX) images in target area observed in descending Orbit. Source: [28]. Figure 3, page 595.    Post-event dual polarimetric SAR images for earthquake damage assessment were used for the 2015 Nepal earthquake. Polarimetry refers to the vector nature of electromagnetic waves. Radar polarimetry is the science of acquiring, processing and analysing the polarization state of an electromagnetic wave in radar applications [29]. In radar physics, the backscattering coefficient signifies the backscattering ability of them scattered on the ground to the received radar electromagnetic wave. According to polarization, there are three typical scattering types: surface, double-bounce and volume. Undamaged buildings present a high backscattering coefficient given the double-bounce scattering, whereas damaged presents a low one. The radar scattering characteristics of undamaged and damaged urban areas are compared using polarimetric features derived from PALSAR-2 and Sentinel 1 images [11]. Supervised classification, feature selection and split-based image analysis were utilized on the images [11]. Pearson’s correlation coefficient is adopted as a criterion to select high correlation features utilized to construct classifiers. The outcome of this process indicated that texture features derived from the backscattering coefficient were the most appropriate to identify the building damage.  Higher correlations were found between the damaged and texture features derived from the intensity cross-polarization than other features considered [11]. Support vector machine (SVM) and K-Nearest Neighbor ((KNN) classifiers were applied for supervised classification. Employing 30% data for testing and 70% of the data for training, the support vector machine (SVM) classifier reached an accuracy of 80.5%. Earthquake damage Visualization (EDV) use SAR data for rapid detection of earthquake damage considering three parameters. The first parameter is the normalized difference between pre-seismic and co-seismic coherences, and vice versa, to calculate the forward (from pre-seismic to co-seismic) and backward (from co-seismic to pre-seismic) change parameters, correspondingly [30]. The second parameter is the backward change added to visualize the changes not caused by the earthquake. The third parameter is the average values of the pre-seismic and co-seismic coherence maps. These three parameters were eventually merged into the EDV as a red, green and blue (RGB) composite imagery [30]. The earthquake damage is visualized efficiently through the EDV employing Horizontal transmit and Horizontal receive (HH), and Horizontal transmit and Vertical receive (HV) polarizations data from the Advanced Land Observing Satellite-2 (ALOS-2) [30]. The performance of the EDV was tested in the Kathmandu Valley, struck by the 2015 Nepal earthquake [31]. The cross-validation results indicated that the EDV is more sensitive to building damage than other methods and its utilization in other earthquakes is feasible [30]. In the last years, SAR based techniques have been also applied to recognize earthquake-related surface deformation and ruptures, massive landslides and subsidence [32]. High-resolution synthetic aperture radar data from Sentinel-1 and near-field GPS data from four stations was utilised to investigate the coseismic and postseismic surface displacement associated with the Gorkha earthquake [14]. The deformation map generated revealed an upliftment of about 1 m near Khatmandu and a subsidence of about 0.8 m toward the north [14].  3.4.5. Interferometric Synthetic-Aperture Radar (InSAR)This technique uses SAR images of the same area obtained at different times [33] to map ground deformation [34]. Comparing InSAR coherence maps from and after an extreme event can produce damage proxy maps (DPM) [8, 35]. Radar and optical images were combined to measure ground displacements and determine the kinematics and geometry of thrust faulting for the Himalayas after the Mw 7.8 Gorkha, Nepal [12, 36, 37]. The InSAR data from the European Space Agency(ESA) was processed to derive surface offsets and surface LOS ground motion from the correlation of amplitude images from Landsat-8 and SAR [13]. These observations were complemented with other published surface displacements from ALOS-2 SAR satellite and GPS coseismic offsets. Up to 2 metres of south-southwest motion and almost 1 metre of uplift in Kathmandu basin and the surrounding Lesser Himalaya were observed, whereas north of this, a large region of the higher Himalaya subsided by about 0.6 metres [13]. Nevertheless, a triggered near-surface slip was found with the Sentinel-1 coseismic interferograms along 26- km-long discontinuity, 10 km north of the MFT. Broadly consistent surface offsets, peaking 60 millimetres of surface motion towards the radar, were showed by independent interferograms on two overlapping descending tracks [13]. Interferometric Synthetic-Aperture Radar and GPS were utilised to study coseismic and early postseismic deformation associated with the Gorkha earthquake by mapping done by different groups of researchers [14]. The analysis of data collected found coseismic and early postseismic (4-88 days) surface displacement. The same geodetic data was inverted for coseismic and postseismic slip on the Main Himalaya Thrust (MHT), providing a detailed slip distribution pattern on the causative fault. The postseismic GPS displacement supports the InSAR observations and the inverted coseismic deformation closely matches the observed InSAR and GPS deformations. The InSAR data pair (29/04/2015-11/05/2015), along with the GPS-derived velocity for a window time of 13 days, was chosen for afterslip inversion as it represents early postseismic movements (4-16 days after the Gorkha earthquake) [14].  3.4.6. Real Aperture Radar (RAR)The RAR technology can be used for rapid building condition screening to provide almost real-time, trustworthy information about its condition and performance. The radar device can detect and range objects by acquiring echoes from different targets contained in its antenna field of view (FOV). This RAR and other pieces can be observed in Figure 9a). A radar observation utilises the time elapsed between the transmission and reception of an electromagnetic waveform to provide a signal called range profile. This signal consists of peaks of different amplitudes that identifies the observed structure's main reflecting parts [15]. When a good signal-to-noise ratio (SNR) is obtained, an interferometric algorithm can be used to look at the changes in the phase of the reflected signal so that the time-shift can be extracted, and hence the vibration within the damaged building can be estimated. Numerical modelling and RAR were used to record the displacement response of a selected building severely damaged due to the MW5.1 2011 Lorca earthquake (Spain), as it is portrayed in Figure 9b). The IBIS-S, a radar device with interferometric capability, a sensor module, a control PC, a power supply and data procession software were employed to monitor the displacement time history of the vibration of the surveyed building [15]. The objective of this test was to determine the feasibility of the RAR-based method to identify the safe state of a damaged building after an earthquake, avoiding accessing unsafe structures [15]. The result shows a good consistency between the experimental and numerical approaches and the observed damage, demonstrating that RAR is a supplementary RS method to safely report a building's operative conditions and structural health after an earthquake [15].  Figure 9. RAR. a) RAR device and b) RAR device and building. Photos: Dr. Ramón Gonzalez-Drigo. Source: [15]. Figure 2, page 18.  State of the Art – most of the methods you have described have been around for many years – far longer than the length of your review. However, algorithms may well have been significantly advanced in this time-frame. As for various crowd sourcing and SM app – how long with they be around? This paper aims to identify state-of-the-art data sources for building damage assessment and guide more efficient data. We agree with the reviewer that data sources based on fieldwork missions and RS have been used for decades; however, they are still used. In both cases, there are currently advances. Nowadays, fieldwork missions use apps to record the building damage assessments instead of paper forms.  Remote sensing methods use satellites with more functionalities and devices with higher resolutions, precision and accuracy. Something similar happens with crowdsourcing platforms, DYFI has existed since 1999 [38]; the Earthquake network started in 2012 [16]; the LastQuake app existed before 2015[17]; Raspberry shake [39], and TSER system [21] were launched in 2016; MyShake network detection algorithm was tested in 2018 [19] and the first reference about Quickdeform dates from 2020 [27]. Facebook and Twitter were launched in 2004 and 2006, respectively. However, none of these crowdsourcing and SM platforms currently work as they worked when they were created. All of them have evolved based on the feedback based on their users' experience. This fact is the reason to consider them as part of state of the art. We include this paragraph in the conclusion section:‘(…) We know that data sources based on fieldwork missions and RS have been used for decades; however, they are still evolving in terms of their capabilities and their use. Nowadays, fieldwork missions use apps to record the building damage assessments instead of paper forms and increasingly include RS methods with more functionalities and devices with higher resolutions, precision and accuracy. Something similar happens with crowdsourcing platforms, DYFI has existed since 1999 [38]; the Earthquake network started in 2012 [16]; the LastQuake app existed before 2015 [17]; Raspberry shake [39] and TSER system [21] were launched in 2016; MyShake network detection algorithm was tested in 2018 [19] and the first reference about Quickdeform dates from 2020 [27]. Although Facebook and Twitter were launched in 2004 and 2006, these SM platforms have evolved in response to the feedback from their users and their ability to generate income for the owners. For this reason, and their expanding use in providing data from disasters, we argue that they can be considered to be part of state of the art (…)’.  The sections on social media and crowd sourcing – while there are very interesting new developments here, there is a lot of repetition and these sections are very long without adding new stuff per site – could be condensed in to an overview of the functionality of the each of the platforms.This comment is very much related with the previous one. We elaborate on the evolution of these crowdsourcing and SM platforms to explain why they are still considered state of the art, despite some of them being developed 20 years ago, such as DYFI. The mentions of how crowdsourcing platforms and SM were interrelated were eliminated to avoid repeat content. We revised both sections to remove not relevant content.3.5. Crowdsourcing platforms Eyewitness reports have always been part of seismology, and large volumes of eyewitnesses observations can boost rapid situational awareness [18]. Online tools offer the possibility that ‘citizen engineers’ and volunteers to analyse large amounts of data to quickly provide a qualitative assessment of damage degree and different types of buildings post-earthquake [5]. We identified in our literature review seven crowdsourcing platforms used for earthquake reconnaissance: DYFI, Earthquake network, LastQuake, MyShake, Raspberry Shake, QuickDeform and The TSERSystem. The crowdsourcing platforms identified are listed below in alphabetical order. 3.5.1. Did You Feel It (DYFI)The United States Geological Survey (USGS) developed DYFI in 1999. ‘Did You Feel It’ is an online system to collect macroseismic intensity (MI) data, shaking and damage reports from earthquakes eyewitnesses to process them automatically [18] for generating intensity maps right after an earthquake [38]. This system distributed questionnaires after earthquakes to collect information about their impacts [21]. In the case of earthquakes outside the United States, DYFI data rapidly confirm their occurrence for seismic analysis, and scientists at the USGS National Earthquake Information Centre (NEIC) can use this to indicate the impact of shaking effects quickly. Intensity data collected through DYFI are utilized to provide shaking constraints input for the USGS Global ShakeMap system. This information allows the USGS to alert agencies and users worldwide of significant earthquakes and their likely impact [38].   Incoming entries from multiple web servers are processed and aggregated over postal ZIP codes (in the US), and 1-km and 10 km aggregated boxes for each earthquake to make interactive maps and plots served via the USGS Earthquake Program web pages [38]. The DYFI is a program for community internet intensity maps (CIIM) developed based on the questionnaires filled in that assigns average intensity level to each ZIP code [40]. Two relevant questionnaire indices are combined with other users within their community to produce an intensity calculation [38]. Did You Feel It contributors usually select the most recent earthquake when contributing or searching for information [18, 38]. Some of them still contribute for months after an earthquake; therefore, DYFI maps result from aggregated MI models that change over time. The DYFI questionnaire includes questions about user’s situation, experiences, and behaviours, going beyond the calculation of MI. This data makes the DYFI database, the repository of millions of comments relevant for social sciences [38]. Essentially, DYFI depends on entries from the general public, being citizen-based science. Shakemap and DYFI have significantly facilitated the use of MI in the US, training citizens to think in terms of varying intensities produced by an earthquake [38].  3.5.2. Earthquake networkEarthquake Network is a citizen science research project implementing an earthquake early warning system (EEW) based on smartphone crowdsourcing [22]. People install this smartphone application and receive real-time alerts when the smartphone network detects earthquakes [16]. The Earthquake Network app is, at the same time, the instrument to detect earthquakes. When the smartphone is unused and charging, the app monitors the accelerometer for detecting vibrations likely due to an earthquake. If a seismic movement is detected, a signal is sent to a server that collects signals from smartphones [16, 22]. Following the algorithm, the server decides in real-time if an earthquake is happening. If confirmed, the server infrastructure sends an EEW to the users located in the affected area. Hence, the Earthquake network makes an early warning service available to users interested in making their smartphones available for detection when their smartphone is not in use [16]. The Earthquake Network app interface is depicted in Figure 10. For real-time detection of earthquakes, the Earthquake network sends signals to a server located in Europe. The infrastructure is currently based on nine servers that receive a large number of signals from the network and the numerous users opening the app when they experience the earthquake. Whatever signal received by the server infrastructure activates a statistical algorithm that determines if an earthquake is happening. Then the analysis is done at a global scale and in real-time [16]. Users can report the impact of an earthquake pushing a button in the app interface, reporting the earthquake's impact considering only three levels: mild, strong, and very strong, to make it fast. Spatial coordinates of the smartphones are automatically sent with the felt report. If several reports are received from a specific area simultaneously, a notification is sent to the smartphone users through the Firebase Cloud Messaging (FCM) platform. Usually, users receive first the EEW activated by smartphones, and within one minute, they receive the notification activated by users. The user is redirected to a map showing all felt reports by clicking on the notifications. As an example, reports were collected in Puerto Rico within 60 seconds after the 3.6 Magnitude earthquake; thus, users were aware of the low impact of the earthquake before any official information was released [16]. Earthquake Network app has a second strategy to send smartphone coordinates by SMS or email to a list of trusted contacts when an EEW is received. The aim is that SMS/e-mail will be sent before lifelines (phone network/Internet) are affected and the EEW received before the shaking starts. However, the smartphone must be on at the time of the earthquake. After receiving the SMS/e-mail, users can report their status by pressing a button in the app's user interface indicating: ‘I am fine’ or ‘I need help’[16]. Figure 10. User interface of the Earthquake Network app for sending felt reports (Left) and for asking help if involved in an earthquake (Right). Source: [16] Figure 3, page 5. 3.5.3. LastQuake appThe European Mediterranean Seismological Centre (EMSC) developed a multichannel rapid information system consisting of websites, a Twitter quakebot called LastQuake, and an eponym smartphone app [16, 18, 22, 38] at the interface between global users looking for information the EMSC. Components of this multichannel rapid information system are presented in Figure 11. This multichannel has two objectives: To offer practical information in regions where an earthquake is felt and to collect reports from users containing direct and indirect observation related to the intensity and damages caused by the earthquake [17]. LastQuake app also monitors people activity soon after the earthquake [16]. LastQuake app users’ engagement is built on the rapid provision of tremor detection. Users’ behaviour is similar to real-time seismic sensors when they enter the EMSC websites or the smartphone app. An earthquake can generate several automatic tweets published by the quakebot and updates on the website. These tweets and updates describe earthquake parameters, epicentral plot, maps, felt reports, historical seismicity, tsunami information and links to the user's comments [17]. Figure 11. Components of EMSC’s multichannel rapid information system. Source:[17] Figure 3, page 35.  There are two kinds of data collected from users: crowdsourced data (felt reports, geotagged pictures, videos and open comments) and crowdsourced detections (users reactions and experience on the ground) [17, 18]. Then it is only necessary to identify the geographic origin of the website visitors through their Internet Protocol (IP) to determine where the earthquake was felt without the need for any seismological data [17]. There are two other complementary crowdsourced earthquake detection methods in operation: Twitter Earthquake Detection (TED). This approach identifies a surge in tweets, including the keyword ‘earthquake’ in various languages and the traffic analysis generated by LasQuake app launches, which works like the website traffic analysis. If many people in the same area open the app simultaneously, there is a high probability of an earthquake, and hence an alert is sent [16]. A push notification is submitted to LastQuake app users once the crowdsourced detection has been confirmed. Felt reports are collected through a multilingual online macroseismic questionnaire available on the EMS website or using the 12 cartoons depicting different shaking and damage levels [21]. Each cartoon represents an intensity level of the European Macroseismic Scale 1988 [18]. These cartoons have made more efficient the collection of reports at a global scale [17]. Plotting the location of the LastQuake app launches is possible to map the felt area automatically.  3.5.4. MyShake projectThe ‘Myshake’ project aims to build a global smartphone seismic network to develop a large-scale EEW and other applications to boost the power of crowdsourcing [16, 19]. This project implemented by UC Berkeley Seismological Laboratory [16] is based on MyShake mobile application, which first detects earthquake shaking on a single phone. Then this detection is confirmed on the MyShake servers using a ‘network detection’ algorithm activated by multiple single-phone detections [19]. This app is utilised to continuously monitor the smartphone accelerometer to measure earthquakes and send alerts [16]. It is assumed that 0.1% of the population in each region has the MyShake mobile application installed on their smartphone. The system works better (alerts generated between 4 and 6 seconds, errors are within to 0:5 magnitude units, and epicentres are typically within 10 km of true locations) in high-density populations and onshore regions with an upper crustal earthquake with a magnitude higher than M7:0. In the opposite case, in low-density populated regions and in offshore areas, alerts are slower, and the uncertainty in magnitude and location soar. The system still works where only 0.01% of the population use the MyShake app, in highly populated regions and earthquakes with a magnitude higher than M 5.5 [19]. 3.5.5. Raspberry Shake Raspberry Shake is a crowdsourcing operation based on affordable seismic sensors that can be easily installed in houses or schools and used in several citizen seismology projects [22]. After the 2015 magnitude 7.8 Ghorka earthquake in Nepal, it was found that the population was not aware of their country's high seismic hazard. Hence, an initiative to introduce seismology in schools, focusing on education and citizen seismology, was implemented in Nepal [20]. Considering its performance in a laboratory test, suitability for the field conditions, the seismic sensor installed in schools was a Raspberry Shake 1D (RS1D). Additional criteria to select this sensor were low cost (below 500 USD), easily applicable for educational purposes, high sensitivity to detect local earthquakes, ease to handle and the possibility of recording data without an additional computer. These seismometers installed in 22 schools develop the Nepal School Seismology Network delivering online data openly [20]. This program started in Western Nepal because people there have limited opportunities to learn about earthquakes. There has been no significant earthquake for 500 years in this region [20, 41, 42], and there is an acceptable travel time by vehicle or short walk between different sites where seismometers were installed. Facebook and Twitter were used in Nepal to spread information about the Nepal School Seismology Network and ask interested schools to fill in the request form [20]. The criteria to select the 22 schools among 100 that submitted the request form were: number of students, motivation, feasibility to instal the seismometer on the ground floor, 200 m as minimum distance to road or highway to avoid anthropogenic noise, own internet connection, alternate power supply and reachability of the school [20]. Each seismometer installed in a school inform students about earthquakes, waveforms, distance and magnitude of the event. Thus, the network is used for both sharing locally recorded data openly and teaching [20]. 3.5.6. QuickDeformNear real-time ground deformation maps generated after earthquakes are essential for hazard assessment and usually take a couple of hours or longer to be generated by conventional means. A near-real-time coseismic ground deformation map generation system to assist emergency response is developed by Zhao, Liu and Xu [27]. This system adopts source parameters published by the USGS-NEIC and empirical equations to generate the real-time (within seconds) coseismic ground deformation maps [27]. This method integrates seismic deformation maps into WebGIS for real-time disaster evaluation and emergency response. The seismic ground deformation maps are integrated as self-adapting spatial data fusion using the Okada rectangular dislocation model [43] and empirical equations of fault [27]. Later the result is visualised on an interactive WebGIS platform named: QuickDeform. This GIS user-oriented platform provides real-time evaluation and emergency response information by viewing, searching, and customizing the seismic deformation [27]. 3.5.7. The Taiwan Scientific Earthquake Reporting (TSER) System                      The TSER is a crowdsourcing system designed for acquiring quantitative data collected by trained volunteers for earthquake-triggered surface damages [21]. This TSER is an experimental program launched by the Institute of Earth Sciences, Academia Sinica, and Taiwan's Seismological centre in 2016. These institutions incorporated in the program: (1) computer-aided volunteer management system (VMS), (2) educational training course and (3) online report and mapping platform [21]. Additionally, volunteers were trained to ensure the quantity, completeness, quality and reliability of the data collected when they report the damages produced by earthquakes. The TSER was developed in the framework of a citizen seismology program that includes training courses and VMS with the web GIS-based platform to crowdsource scientific users reports for earthquake-triggered surface natural damages [21]. The on-site field reports complement the ground observations from the field with real-time instrumental data and results to better understand the surface damages and geohazards produced by earthquakes and support SAR activities and later social impacts [21]. The TSER system was constructed adopting the pre-existing Ushahidi mapping platform, which was widely used successfully for several purposes [21]. Ushahidi platform is an open-source software application and an integrated data crowdsourcing and mapping tool that allows people to collect, manage and analyse data from their communities [44]. The VMS notifies a trained volunteer of a potentially damaging earthquake through email. These notifications indicated the epicentral area to carry out field surveys. Volunteers must log into the TSER platform. To report earthquake-induced surface damages, they must identify the ground damage category from a menu, describe it, locate it, and upload the corresponding picture [21]. The collected information is made available to the public after being checked by the on-duty scientist. The TSER platform was tested after the 2018 Mw 6.4 Hualien earthquake providing the distribution of the surface ruptures, rupture orientation, type of faulting and offset dimension [21].3.6. Social media (SM) Social platforms and smartphone apps are playing an increasing role during disasters[45]. They offer not only public participation but also constitute backchannel communication. Considering that the internet and SM have become the digital nervous system of our planet [17], various forms of SM are valuable tools for quickly collecting large amounts of data relating to disasters. It offers first-hand data, observations, sentiments, and perspectives [46]. This data can range from photos, videos, and comments uploaded to various internet platforms and Facebook, Twitter, Instagram and Youtube. The first fieldwork or ground survey that reported have extracted data from SM was the mission deployed by Stanford’s John A. Blume Earthquake Engineering Centre for the 2017 Puebla-Morelos earthquake. This mission complemented the collapsed buildings' database with data from newspapers and SM [47]. Nowadays, SM platforms have become another medium to share early scientific analysis, forming collaboration bases among multidisciplinary teams. We identified in our literature review three SM platforms used for earthquake reconnaissance: Earthquake Network, Facebook and Twitter. The SM platforms identified are organized below in alphabetical order. 3.6.1. Earthquake NetworkProbably the first social network about earthquakes was Earthquake Network. This platform has chatrooms in 10 languages, where users can share information right after an earthquake, either in private messages or in the public space. Chat moderators in the public space keep the discussion focused on relevant issues and block users that misbehave [16]. Earthquake Network supports people after they have experienced an earthquake by enabling discussions with others to reduce the anxiety and fear caused by it. Those users active in the chatrooms tend to keep the app installed for months to years [16].3.6.2. Facebook                                                                         Facebook is the most popular SM platform [22, 23] for user-generated content. Lastquake app users have the option to share their comments on their Facebook account [17]. In May 2018, the French Island of Mayotte in the Indian Ocean between Madagascar and the coast of Mozambique was struck by a series of earthquakes. At the beginning of the seismic swarm, there was a gap in seismic data and explanations of the phenomenon and a mistrust of the scientific community. Residents using the LastQuake app [17], created their citizen seismology network based on a Facebook group named: Mayotte Earthquake Support (STTM by its acronym in French) [22]. The members of this group on Facebook expressed their scepticism about the lack of information from the seismologist community. After the first earthquake, they started to discuss its effects on the road traffic group. Following this, one group administrator decided to create a group dedicated to earthquakes [22]. Citizens created this group for citizens to exchange knowledge and feelings about earthquakes. Generally, after an earthquake, citizens post messages indicating when and where they felt it and, in some cases, ask for additional information such as magnitude. They also show and ask for emotional support, commenting on each others’ posts and questions, uploading pictures of cracks or trees on the roads, and discussing the earthquakes' potential causes [22]. Over time, this group took a more scientific direction, and some of the members shared reliable seismic information in an understandable way for their fellow citizens. The information shared was about seismology in general, seismological concepts (magnitude and intensity) felt earthquakes, earthquake causes, comparisons with other earthquakes swarms, historical seismic data and safety measures to take [22]. These citizens collected data, reviewed scientific literature, and produced collated forms of knowledge, listing all felt earthquakes mentioned in the group and comparing them to the French Geological Survey (BRGM’s by its acronym in French) seismic reports. Furthermore, a few months after the earthquakes, one of the users suggested equipping the island with a Raspberry shake seismometer [20, 22].  3.6.3. Twitter The Palu MW 7.5 earthquake and tsunami in Indonesia and other events have demonstrated how Twitter quickly generates knowledge in the minutes to hours and days following an event developing an efficient exchange of information and active discussion between the public and scientists and between scientists themselves [23]. In the aftermath of an earthquake, it is essential to promptly establish its geological and geophysical characteristics to be able to explain it to the media and stakeholders and evaluate the risk of secondary effects [23]. In Hurricane Sandy, the Twitter activity was correlated with damage and, therefore, useful for impact assessment and response [17, 48]. Correlation between the number of tweets and the intensity of an earthquake was observed for the first time in 2010. During the Tohoku earthquake, researchers observed a high correlation between the number of tweets and the earthquake’s intensity in some locations [49, 50]. Informative tweets about geophysical data were made for the 2018 Palu - Sulawesi island, Indonesia. Based on these tweets, a timeline was built of the rapid progress of understanding the earthquake rupture and its effects. Published papers, maps about the seismotectonic context in Indonesia, teleseismic data, local seismic waveforms, high-resolution optical satellite images, SAR, tide gauge records, and field observations from both science groups and local residents were shared on Twiter, getting rapid and varied feedback from fellow researchers [23]. The correlation between activity on Twitter and Mercalli intensity was demonstrated in Napa, California [40], Japan, and Chile [51]. The activity on Twitter related to recent earthquakes is plotted in Figure 12.Figure 12. Twitter activity related to a) 2019 Albania earthquake; b) 2020 Zagreb earthquake; c) 2020 Aegean earthquake. Source: TweetBinder. Specific corrections Figure 1: I don’t really get the point of this figure, its not telling me anything that I didn’t take away from the text. I presume the colours of the boxes signify something, but it is not clear what.Figure 1  is The flow diagram of the methodology applied to select the references for the literature review. We apologise for not communicating the message of this graph properly. This time we include conventions: the grey box contains the framework of the research; blue boxes contain the parameters used to identify the references; and red boxes contain the results of the application of those parameters showing the number of references identified and, finally, reviewed. Please check below the modified figure.Figure 1. Flow diagram of the methodology applied to select the references for the literature review. Line 135, might help to say where Mayotte is - I had to look it upWe have already included the previous version: ‘In May 2018, the French Islan of Mayotte in the Indian Ocean’. Now, to be more specific with the location of this case study area, we added more details to the sentence:‘(…) In May 2018, the French Islan of Mayotte in the Indian Ocean between Madagascar and the coast of Mozambique was struck by a series of earthquakes (…)’. Line162, I can’t imagine all these people are “volunteers” – these sort of people look more like the self-organised teams as mentioned in line 179-181We agree with the reviewer that EEFIT, GEER, EERI, EUCENTRE, ReLUIS and NZSEE are self-organised teams but consisting of volunteers. Actually, the first and second authors of this manuscript are part of the training sub-committee of EEFIT, being the first author, the leader of this sub-committee. The second author has led  earthquake reconnaissance missions after the 2015 Nepal and the 2011 Christchurch earthquakes. Our work is volunteer considering that we paid for the affiliation to EEFIT, and we do not have any monetary compensation for our contributions.  Line 164, seismological specialistsThank you for noticing this spelling mistake. It is corrected accordingly. ‘(…) researchers with experience in building instrumentation, geotechnical and seismological specialists [2] (…)’ Paragraph lines 161-189: please be consistent with naming and locating these organisations – each one should have a country of base mentioned – ok a lot are in the US, but I only know that from googling them. Likewise line 186 Azcapotzalco – why not Mexico? I don’t need to know the suburb of Mexico City.Thanks for your observation. Considering that these organisations undertake earthquake reconnaissance missions globally, and some of them have affiliated members all over the globe,  we did not consider it relevant to mention the location of their administrative offices, in case they have one. Still, for the cases of EUCENTRE and ReLuis is already mentioned in the text that these institutions are located in Italy. In the case of NZSEE, ATC3, ASCE, ACI, and CICM, their location is included in explaining the acronym. However, we admit that the location of EEFIT,  EERI, GEER and ATC can be unknown to the reader; therefore we added the location of their administrative offices in the text.The Universidad Autonoma de Metropolitana (UAM) in Mexico has five units; each unit has different academic divisions that address different fields of knowledge.  ‘Azcapotzalco’ is the UAM’s unit that cooperated with NZEE, ACI, CICM and Stanford’s John A. Blume Earthquake Engineering Centre to deploy a team for the 2017 Mexico earthquake, not any other unit. However, UAMS has other three units that also host the division of basic’s sciences and engineering; maybe that is why the authors of this reference [52] considered it important to mention the UAM unit that collaborated with them.‘(…) Fieldwork or ground surveys are a traditional approach to estimate the spatial distribution of earthquake impacts to building clusters, performed by volunteer groups consisting of structural engineers, architects, researchers with experience in building instrumentation, geotechnical and seismological specialists [2] and undergraduate students of these fields. These missions collect structural, geotechnical, seismological and damage information [2]. Earthquake reconnaissance missions are undertaken by national or international organisations such as the Earthquake Engineering Field Investigation Team (EEFIT) [3, 5, 53-56] in United Kingdom (UK), the Geotechnical Extreme Events Reconnaissance Association (GEER)[3, 8, 9, 54, 56, 57], and Earthquake Engineering Research Institute (EERI) [3, 5, 8] in the United States (US). In Italy, the European Centre for Training and Research in Earthquake Engineering (EUCENTRE) and the Italian Network of University Laboratories for Earthquake Engineering (ReLUIS) have organized earthquake reconnaissance missions and conducted follow-on seismic policy analyses. For six decades, the New Zealand Society for Earthquake Engineering (NZSEE) has supported reconnaissance research of earthquakes and major tsunamis in the world [3]. The Asian Technical Committee (ATC3) ‘Geotechnology for natural hazards’, the Building Research Institute of Japan and the Nepalese Engineering Society has conducted reconnaissance missions in Asia after natural phenomena. Another organization that have supported reconnaissance missions in US is the American Society of Civil Engineers (ASCE). Additionally, sometimes, self-organized teams with a focused-hypothesis driven research question or inquiry are formed to collect data [3]. In the 2017 Puebla-Morelos earthquake case, also known as the 2017 Puebla earthquake or the 2017 Mexico earthquake, the Applied Technology Council (ATC) from US deployed a team to Mexico city sponsored by the ATC Endowment Fund. This team was joined by practising architects, engineers, professors and local agencies [2]. The NZSEE, in collaboration with the Universidad Autonoma de Metropolitana (UAM) Azcapotzalco, the American Concrete Institute (ACI) Disaster Reconnaissance team, and the Colegio de Ingenieros Civiles de Mexico (CICM) also deployed a team for the same earthquake in Mexico [52] and the team of the Stanford’s John A. Blume Earthquake Engineering Centre as well [47] (…)’.   Line 183 Mexico CityThanks for this observation. It was corrected accordingly:‘(…) deployed a team to Mexico City sponsored by the ATC Endowment Fund.(…)’ Line 187 also replace with “all” Thanks for this observation. It was replaced accordingly:‘(…)The NZSEE, in collaboration with the Universidad Autonoma de Metropolitana (UAM) Azcapotzalco, the American Concrete Institute (ACI) Disaster Reconnaissance team, and the Colegio de Ingenieros Civiles de Mexico (CICM) all deployed a team for the same earthquake in Mexico [52] and the team of the Stanford’s John A. Blume Earthquake Engineering Centre as well [47] (…)’. Line 188 – include the standard team in the previous list – ugly sentence as it currently readsWe agree with the reviewer that the paragraph must be fixed. Please find the new version below:‘(…) The NZSEE, in collaboration with the Universidad Autonoma de Metropolitana (UAM) Azcapotzalco, the American Concrete Institute (ACI) Disaster Reconnaissance team, the Colegio de Ingenieros Civiles de Mexico (CICM) and the team of the Stanford’s John A. Blume Earthquake Engineering Centre [47] all deployed a team for the same earthquake in Mexico [52] (…). Line192 really? one week for a survey? Is this prescriptive – make this more generalised or give more examples In a foot-on-ground survey, the assessment is conducted manually [34], one building after another [26], each reconnaissance mission on field takes one week.Thanks for this question. We must clarify that the week refers only to the time in the field.  Earthquake reconnaissance missions start before, collecting data about the study area, contacting local organisations that support the missions and selecting the right members for the fieldwork. After the fieldwork, it is necessary to write a report with findings. Then as the reviewer highlight, and earthquake reconnaissance missions take more than one week.It is out of the scope of fieldwork or ground surveys to inspect the entire area affected by an earthquake; therefore, during data collection, the inspection area is limited to that area where the causes of failures of buildings can be appreciated within a safe environment. Earthquake reconnaissance missions members are usually volunteers that can not expend more than one week far from their daily business.‘(…) In a foot-on-ground survey, the assessment is conducted manually [58], one building after another [59]; each reconnaissance mission on the field takes one week. Considering that there is a preliminary data collection to limit the inspection area where the causes of failures of buildings can be observed within a safe environment. Earthquake reconnaissance missions members are usually trained volunteers affiliated with one of the organisations mentioned before who cannot spend more than one week far from their daily business. Even the ATC’s reconnaissance mission after the 2017 Puebla-Morelos earthquake lasted three days [2]. (…)’. Line 197 – preliminary data collection – such as ?? give examplesPreliminary data collection refers to seismic information, size of the affected area, building typologies, injuries and casualties, local institutions, accessibility, safety and security aspects, local traditions, and any information supporting the fieldwork planning. Please check the addition to the paragraph: ‘(…) Usually, preliminary data collection before fieldwork includes seismic information, size of the affected area, building typologies, injuries and casualties, local institutions, accessibility, safety and security aspects, local traditions, and any information supporting the fieldwork planning [8] (…)’. Line 198 – only smart phones? No tablets or other media? Maybe “smart technology”?Thanks for the question. The reviewer is correct; the proper term is“smart technology”. It is changed in the text accordingly: ‘(…) Traditionally paper forms were utilised, but nowadays, smart technologies are used as tools to complete investigation forms (…)’ Line 201-203 “EERI first professional organisation to organise reconnaissance mission to significant seismic event” this sounds unreal – please justify with dates and place. The sentences about the EEFUT and EERI in this area disrupt the description of the ATC’s mission to Mexico – suggest taking them out of this paragraph. Thanks for the comment and the suggestion. Please find below the exact quotation from: Wartman, J., et al., Research Needs, Challenges, and Strategic Approaches for Natural Hazards and Disaster Reconnaissance. Frontiers in Built Environment, 2020. 6: p. 17. Pag. 2 ‘(…)The EERI was one of the first professional organizations to formalize regular reconnaissance investigations of major seismic events by establishing the Learning from Earthquakes (LFE) program in 1973 [3] (…)’.Following your suggestion, we relocated the paragraph and included the date mentioned by the original authors: ‘(…) Earthquake reconnaissance missions are undertaken by national or international organisations such as the Earthquake Engineering Field Investigation Team (EEFIT) [3, 5, 53-56] in the United Kingdom (UK), the Geotechnical Extreme Events Reconnaissance Association (GEER)[3, 8, 9, 54, 56, 57], and Earthquake Engineering Research Institute (EERI) [3, 5, 8] in the United States (US). The EERI, through its program Learning from Earthquakes (LFE) established in 1973 was the first professional organization to organize reconnaissance missions to significant seismic events. This organization recently has formed a virtual earthquake reconnaissance teams (VERT) to conduct ‘virtual’ (i.e., not on-site) assessment within 48 hours after an earthquake [3]. In Italy, the European Centre for Training and Research in Earthquake Engineering (EUCENTRE) and the Italian Network of University Laboratories for Earthquake Engineering (ReLUIS) have organized earthquake reconnaissance missions and conducted follow-on seismic policy analyses. For six decades, the New Zealand Society for Earthquake Engineering (NZSEE) has supported reconnaissance research of earthquakes and major tsunamis in the world [3](…)’.  Line 211 integrated? Not sure you mean integrated – composed of ? conducted by? initiated by? We are afraid that we do not understand which word the reviewer is referring to, maybe ‘instrumented’?. If it is the case, the word means the accelerometers put into the building to collect damage and geotechnical data and earthquake ground motions. Please check:  ‘(…) This ATC’s reconnaissance mission collected damage and geotechnical data, earthquake ground motions from several suites and ambient vibration recordings from buildings instrumented by the team during the reconnaissance trip [2]. This ATC mission instrumented seven of the inspected buildings with an array of accelerometers [2].  Line 273 presume the acronym ECEMS applies?Thanks for the suggestion, but we did not find any indication that ECEMS stands for European Commission Copernicus Emergency Management Service in our research.  Line 274 – RS – here we have the remote part of the survey – the visual (near field) the OD – slightly more removed and the RS – you could say something about the correlation between the scales of remoteness.As written in the manuscript, the data collected in small scale from RS is completed with the data collected in situ data on a large scale and if the data is rightly collected, there must be correlation. Please check:‘(…) These maps were delineated based on the timely geospatial information derived from RS and completed with available open data sources in situ for emergency response [5] (…)’.  Line 190 – can be done - NO – you don’t do a landslide – rephraseThank you very much for this observation. Landslide can be induced, but this is not the case. Therefore we corrected the sentence accordingly:‘(…) Detailed structural and geotechnical surveys can be done to obtain high-resolution digital elevation models (DEM) using TLS[3] (…)’.  Line 295 off of – remove the “of”Thank you very much for pointing up this mistake. Hence it was removed. ‘(…) This data collection method uses a scanner that reflects a laser off a rotating mirror to acquire a sphere of measurements from one central point of view (…)’.  Line 301 – surveying-based what? Correct the English pleaseThanks for the question. In this case makes reference to damage assessment. Please find the addition of the text accordingly‘(…) The laser scanner integrates a global positioning system (GPS) and correlates individual scans in post-processing, making it suitable for surveying based damage assessment (…)’.  Line 302 Structure from Motion – capital SThanks for this observation. The capital S is added accordingly. ‘(…) Another technique for detailed survey is Structure from Motion (SfM); this photogrammetric technique uses two-dimensional images taken from multiple viewpoints to compute a 3D representation of the scene being surveyed (…)’.  Line 313-315 – please correct the grammar and punctuation so that the sentence makes sense.Thank you for your observation. Please find the modified sentence below: ‘(…)The use of imaging technology has been increasing rapidly as data gathering tool boosting mission capabilities and ensure their safe deployment in areas affected by earthquakes [35] (…)’.  Line 326 – data used to Thank you for your suggestion. The word ‘used’ was added to the sentence:‘(…) Aerial photography was the source of damage data used to estimate the size of the impact area quickly, before the availability of very high resolution (VHR) satellite images (…)’.  Line 327 – remove commaThe comma was removed accordingly. ‘(…) Aerial photography was the source of damage data used to estimate the size of the impact area quickly before the availability of very high resolution (VHR) satellite images (…)’  Line 332 – GIS – give us the full title at the first occurrence of an acronymThanks for this reminder. Please find the full title of the acronym in the modified sentence below: ‘(…) visual or manual interpretation with the support of geographic information systems (GIS) software [6, 60] (…)’.  Line 334 – Very high-resolution – add acronym here (VHR)Thanks for the suggestion, but after a period to initiate another sentence, we prefer not to include the acronym, rather the full title without including the acronym.   Line 352 – justify the statement about sensorWe complemented the sentence to justify the statement about the sensor. Please check the modified sentence below:‘(…) The spectral information is a crucial aspect of the change detection problem under study, depending on the sensor loaded on the UAV platform and less on the reference image that can be selected later to be compared to the images produced by the UAV sensor [7] (…)’.  Line 353 captures, line 354 assessesThank you very much for pointing up to this mistake.‘(…) UAVs capture multi-perspective high-resolution imagery, are easy to transport, deploy and fly, accesses easily destroyed areas and are cost-effective than traditional airborne approaches [7, 61] (…)’.  Line 357 – suggested rearrangement - The GEER mission for the Central Italy case study selected UAVs as one of the principal data collection tools to take images for assessing potential landslides based on their portability, rapidity in data collection and superior field of view [32].Thank you very much for the suggestion for the rearrangement. The sentence was modified accordingly.   Line 367 – move the LiDAR sent
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