Articles | Volume 23, issue 2
https://doi.org/10.5194/nhess-23-789-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-789-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Feb 2023
Research article |
| 23 Feb 2023
| 23 Feb 2023
Earthquake building damage detection based on synthetic-aperture-radar imagery and machine learning
Anirudh Rao
CORRESPONDING AUTHOR
Seismic Risk Team, Global Earthquake Model Foundation, Pavia, Italy
Jungkyo Jung
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Vitor Silva
Seismic Risk Team, Global Earthquake Model Foundation, Pavia, Italy
Giuseppe Molinario
World Bank Group, Washington, DC, USA
Sang-Ho Yun
Earth Observatory of Singapore, Nanyang Technological University, Singapore
Asian School of the Environment, Nanyang Technological University, Singapore
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Related authors
No articles found.
Eleanor Tennant, Susanna F. Jenkins, Victoria Miller, Richard Robertson, Bihan Wen, Sang-Ho Yun, and Benoit Taisne
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-81, https://doi.org/10.5194/nhess-2024-81, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
After a volcanic eruption, assessing building damage quickly is vital for response and recovery. Traditional post-event damage assessment methods such as ground surveys, are often time-consuming and resource-intensive, hindering rapid response and recovery efforts. To overcome this, we have developed an automated approach that uses UAV acquired optical images and deep learning to rapidly generate spatial building damage information.
Irina Dallo, Michèle Marti, Nadja Valenzuela, Helen Crowley, Jamal Dabbeek, Laurentiu Danciu, Simone Zaugg, Fabrice Cotton, Domenico Giardini, Rui Pinho, John F. Schneider, Céline Beauval, António A. Correia, Olga-Joan Ktenidou, Päivi Mäntyniemi, Marco Pagani, Vitor Silva, Graeme Weatherill, and Stefan Wiemer
Nat. Hazards Earth Syst. Sci., 24, 291–307, https://doi.org/10.5194/nhess-24-291-2024, https://doi.org/10.5194/nhess-24-291-2024, 2024
Short summary
Short summary
For the release of cross-country harmonised hazard and risk models, a communication strategy co-defined by the model developers and communication experts is needed. The strategy should consist of a communication concept, user testing, expert feedback mechanisms, and the establishment of a network with outreach specialists. Here we present our approach for the release of the European Seismic Hazard Model and European Seismic Risk Model and provide practical recommendations for similar efforts.
John Douglas, Helen Crowley, Vitor Silva, Warner Marzocchi, Laurentiu Danciu, and Rui Pinho
EGUsphere, https://doi.org/10.5194/egusphere-2023-991, https://doi.org/10.5194/egusphere-2023-991, 2023
Preprint withdrawn
Short summary
Short summary
Estimates of the earthquake ground motions expected during the lifetime of a building or the length of an insurance policy are frequently calculated for locations around the world. Estimates for the same location from different studies can show large differences. These differences affect engineering, financial and risk management decisions. We apply various approaches to understand when such differences have an impact on such decisions and when they are expected because data are limited.
Ekbal Hussain, John R. Elliott, Vitor Silva, Mabé Vilar-Vega, and Deborah Kane
Nat. Hazards Earth Syst. Sci., 20, 1533–1555, https://doi.org/10.5194/nhess-20-1533-2020, https://doi.org/10.5194/nhess-20-1533-2020, 2020
Short summary
Short summary
Many of the rapidly expanding cities around the world are located near active tectonic faults that have not produced an earthquake in recent memory. But these faults are generally small, and so most previous seismic-hazard analysis has focussed on large, more distant faults. In this paper we show that a moderate-size earthquake on a fault close to the city of Santiago in Chile has a greater impact on the city than a great earthquake on the tectonic boundary in the ocean, about a 100 km away.
Byung-Hun Choe, Sergey V. Samsonov, and Jungkyo Jung
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-116, https://doi.org/10.5194/tc-2020-116, 2020
Revised manuscript not accepted
Short summary
Short summary
This study proposes a methodology to monitor the growth and displacement of landfast ice in the Mackenzie Delta. 3-dimensional speckle offsets were reconstructed with ascending and descending orbit SAR data. Horizontal and vertical displacements caused by landfast ice breakups and pressure ridges were observed. Cumulative vertical offsets of approximately −1 to −2 m were observed, which is due to longer radar penetration into the ice-water interface with increasing landfast ice thickness.
Dennis Wagenaar, Alex Curran, Mariano Balbi, Alok Bhardwaj, Robert Soden, Emir Hartato, Gizem Mestav Sarica, Laddaporn Ruangpan, Giuseppe Molinario, and David Lallemant
Nat. Hazards Earth Syst. Sci., 20, 1149–1161, https://doi.org/10.5194/nhess-20-1149-2020, https://doi.org/10.5194/nhess-20-1149-2020, 2020
Short summary
Short summary
This invited perspective paper addresses how machine learning may change flood risk and impact assessments. It goes through different modelling components and provides an analysis of how current assessments are done without machine learning, current applications of machine learning and potential future improvements. It is based on a 2-week-long intensive collaboration among experts from around the world during the Understanding Risk Field lab on urban flooding in June 2019.
Related subject area
Databases, GIS, Remote Sensing, Early Warning Systems and Monitoring Technologies
AscDAMs: advanced SLAM-based channel detection and mapping system
Shoreline and land use–land cover changes along the 2004-tsunami-affected South Andaman coast: understanding changing hazard susceptibility
Dynamical changes of seismic properties prior to, during, and after 2014–2015 Holuhraun Eruption, Iceland
A methodology to compile multi-hazard interrelationships in a data-scarce setting: an application to Kathmandu Valley, Nepal
Insights into the development of a landslide early warning system prototype in an informal settlement: the case of Bello Oriente in Medellín, Colombia
The World Wide Lightning Location Network (WWLLN) over Spain
Exploring drought hazard, vulnerability, and related impacts to agriculture in Brandenburg
Tsunami hazard perception and knowledge of alert: early findings in five municipalities along the French Mediterranean coastlines
Review article: Physical Vulnerability Database for Critical Infrastructure Multi-Hazard Risk Assessments – A systematic review and data collection
Exploiting radar polarimetry for nowcasting thunderstorm hazards using deep learning
Machine-learning-based nowcasting of the Vögelsberg deep-seated landslide: why predicting slow deformation is not so easy
Fixed photogrammetric systems for natural hazard monitoring with high spatio-temporal resolution
A neural network model for automated prediction of avalanche danger level
Brief communication: Landslide activity on the Argentinian Santa Cruz River mega dam works confirmed by PSI DInSAR
Impact of topography on in situ soil wetness measurements for regional landslide early warning – a case study from the Swiss Alpine Foreland
Assessing riverbank erosion in Bangladesh using time series of Sentinel-1 radar imagery in the Google Earth Engine
Quantifying unequal urban resilience to rainfall across China from location-aware big data
Comparison of machine learning techniques for reservoir outflow forecasting
Development of black ice prediction model using GIS-based multi-sensor model validation
Forecasting vegetation condition with a Bayesian auto-regressive distributed lags (BARDL) model
A dynamic hierarchical Bayesian approach for forecasting vegetation condition
Using a single remote-sensing image to calculate the height of a landslide dam and the maximum volume of a lake
Enhancing disaster risk resilience using greenspace in urbanising Quito, Ecuador
Gridded flood depth estimates from satellite-derived inundations
ProbFire: a probabilistic fire early warning system for Indonesia
Index establishment and capability evaluation of space–air–ground remote sensing cooperation in geohazard emergency response
Brief communication: Monitoring a soft-rock coastal cliff using webcams and strain sensors
Multiscale analysis of surface roughness for the improvement of natural hazard modelling
EUNADICS-AV early warning system dedicated to supporting aviation in the case of a crisis from natural airborne hazards and radionuclide clouds
Are sirens effective tools to alert the population in France?
UAV survey method to monitor and analyze geological hazards: the case study of the mud volcano of Villaggio Santa Barbara, Caltanissetta (Sicily)
Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria
CHILDA – Czech Historical Landslide Database
Review article: Detection of actionable tweets in crisis events
Long-term magnetic anomalies and their possible relationship to the latest greater Chilean earthquakes in the context of the seismo-electromagnetic theory
HazMapper: a global open-source natural hazard mapping application in Google Earth Engine
Opportunities and risks of disaster data from social media: a systematic review of incident information
Online urban-waterlogging monitoring based on a recurrent neural network for classification of microblogging text
Predicting power outages caused by extratropical storms
Near-real-time automated classification of seismic signals of slope failures with continuous random forests
Assessing the accuracy of remotely sensed fire datasets across the southwestern Mediterranean Basin
Responses to severe weather warnings and affective decision-making
The object-specific flood damage database HOWAS 21
A spaceborne SAR-based procedure to support the detection of landslides
GIS-based DRASTIC and composite DRASTIC indices for assessing groundwater vulnerability in the Baghin aquifer, Kerman, Iran
Review article: The spatial dimension in the assessment of urban socio-economic vulnerability related to geohazards
Design and implementation of a mobile device app for network-based earthquake early warning systems (EEWSs): application to the PRESTo EEWS in southern Italy
CCAF-DB: the Caribbean and Central American active fault database
Evaluation of a combined drought indicator and its potential for agricultural drought prediction in southern Spain
Study on real-time correction of site amplification factor
Tengfei Wang, Fucheng Lu, Jintao Qin, Taosheng Huang, Hui Kong, and Ping Shen
Nat. Hazards Earth Syst. Sci., 24, 3075–3094, https://doi.org/10.5194/nhess-24-3075-2024, https://doi.org/10.5194/nhess-24-3075-2024, 2024
Short summary
Short summary
Harsh environments limit the use of drone, satellite, and simultaneous localization and mapping technology to obtain precise channel morphology data. We propose AscDAMs, which includes a deviation correction algorithm to reduce errors, a point cloud smoothing algorithm to diminish noise, and a cross-section extraction algorithm to quantitatively assess the morphology data. AscDAMs solves the problems and provides researchers with more reliable channel morphology data for further analysis.
Vikas Ghadamode, Aruna Kumari Kondarathi, Anand K. Pandey, and Kirti Srivastava
Nat. Hazards Earth Syst. Sci., 24, 3013–3033, https://doi.org/10.5194/nhess-24-3013-2024, https://doi.org/10.5194/nhess-24-3013-2024, 2024
Short summary
Short summary
In 2004-tsunami-affected South Andaman, tsunami wave propagation, arrival times, and run-up heights at 13 locations are computed to analyse pre- and post-tsunami shoreline and land use–land cover changes to understand the evolving hazard scenario. The LULC changes and dynamic shoreline changes are observed in zones 3, 4, and 5 owing to dynamic population changes, infrastructural growth, and gross state domestic product growth. Economic losses would increase 5-fold for a similar tsunami.
Maria R.P. Sudibyo, Eva P. S. Eibl, Sebastian Hainzl, and Matthias Ohrnberger
EGUsphere, https://doi.org/10.5194/egusphere-2024-1445, https://doi.org/10.5194/egusphere-2024-1445, 2024
Short summary
Short summary
We assessed the performance of Permutation Entropy (PE), Phase Permutation Entropy (PPE), and Instantaneous Frequency (IF), which are estimated from a single seismic station, to detect changes before, during and after the 2014/2015 Holuhraun eruption in Iceland. We show that these three parameters are sensitive to the pre-and eruptive processes. Finally, we discuss their potential and limitations in eruption monitoring.
Harriet E. Thompson, Joel C. Gill, Robert Šakić Trogrlić, Faith E. Taylor, and Bruce D. Malamud
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-101, https://doi.org/10.5194/nhess-2024-101, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
We describe a methodology to systematically gather evidence of the breadth of single natural hazards and their multi-hazard interrelationships in data-scarce urban settings. We apply this methodology to Kathmandu Valley, Nepal, where we find evidence of 21 single hazard types, and 83 multi-hazard interrelationships. This evidence is supplemented with multi-hazard scenarios developed by practitioner stakeholders engaged in disaster risk reduction research and practice in Kathmandu Valley.
Christian Werthmann, Marta Sapena, Marlene Kühnl, John Singer, Carolina Garcia, Tamara Breuninger, Moritz Gamperl, Bettina Menschik, Heike Schäfer, Sebastian Schröck, Lisa Seiler, Kurosch Thuro, and Hannes Taubenböck
Nat. Hazards Earth Syst. Sci., 24, 1843–1870, https://doi.org/10.5194/nhess-24-1843-2024, https://doi.org/10.5194/nhess-24-1843-2024, 2024
Short summary
Short summary
Early warning systems (EWSs) promise to decrease the vulnerability of self-constructed (informal) settlements. A living lab developed a partially functional prototype of an EWS for landslides in a Medellín neighborhood. The first findings indicate that technical aspects can be manageable, unlike social and political dynamics. A resilient EWS for informal settlements has to achieve sufficient social and technical redundancy to maintain basic functionality in a reduced-support scenario.
Enrique A. Navarro, Jorge A. Portí, Alfonso Salinas, Sergio Toledo-Redondo, Jaume Segura-García, Aida Castilla, Víctor Montagud-Camps, and Inmaculada Albert
EGUsphere, https://doi.org/10.5194/egusphere-2024-704, https://doi.org/10.5194/egusphere-2024-704, 2024
Short summary
Short summary
The Worldwide Lightning Location Network (WWLLN) operates a planetary distributed network of stations which detect lightning signals at a planetary scale. A detection efficiency of 38 % with a location accuracy between 2 and 3 km is obtained for the area of Spain by comparing data with those of the Meteorological Spanish Agency. The capability to resolve convective-storm cells generated in a Cut-off Low Pressure is also demonstrated in the west of Mediterranean sea.
Fabio Brill, Pedro Henrique Lima Alencar, Huihui Zhang, Friedrich Boeing, Silke Hüttel, and Tobia Lakes
EGUsphere, https://doi.org/10.5194/egusphere-2024-1149, https://doi.org/10.5194/egusphere-2024-1149, 2024
Short summary
Short summary
Droughts are a threat to agricultural crops, but different factors influence how much damage occurs. This is important to know to create meaningful risk maps and to evaluate adaptation options. We investigate the years 2013–2022 in Brandenburg, Germany, and find in particular the soil quality and meteorological drought in June to be statistically related to the observed damage. Measurement of crop health from satellites are also related to soil quality, and not necessarily to anomalous yields.
Johnny Douvinet, Noé Carles, Pierre Foulquier, and Matthieu Peroche
Nat. Hazards Earth Syst. Sci., 24, 715–735, https://doi.org/10.5194/nhess-24-715-2024, https://doi.org/10.5194/nhess-24-715-2024, 2024
Short summary
Short summary
This study provided an opportunity to assess both the perception of the tsunami hazard and the knowledge of alerts in five municipalities located along the French Mediterranean coastlines. The age and location of the respondents explain several differences between the five municipalities surveyed – more so than gender or residence status. This study may help local authorities to develop future tsunami awareness actions and to identify more appropriate strategies to be applied in the short term.
Sadhana Nirandjan, Elco E. Koks, Mengqi Ye, Raghav Pant, Kees C. H. van Ginkel, Jeroen C. J. H. Aerts, and Philip J. Ward
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-208, https://doi.org/10.5194/nhess-2023-208, 2024
Revised manuscript accepted for NHESS
Short summary
Short summary
Critical infrastructures (CI) are exposed to natural hazards, which may result in significant damage and burden society. The vulnerability is a key determinant for reducing these risks, yet crucial information is scattered in literature. Our study reviews over 1,250 fragility and vulnerability curves for CI assets, creating a unique publicly available physical vulnerability database that can directly be used for hazard risk assessments, including floods, earthquakes, windstorms and landslides.
Nathalie Rombeek, Jussi Leinonen, and Ulrich Hamann
Nat. Hazards Earth Syst. Sci., 24, 133–144, https://doi.org/10.5194/nhess-24-133-2024, https://doi.org/10.5194/nhess-24-133-2024, 2024
Short summary
Short summary
Severe weather such as hail, lightning, and heavy rainfall can be hazardous to humans and property. Dual-polarization weather radars provide crucial information to forecast these events by detecting precipitation types. This study analyses the importance of dual-polarization data for predicting severe weather for 60 min using an existing deep learning model. The results indicate that including these variables improves the accuracy of predicting heavy rainfall and lightning.
Adriaan L. van Natijne, Thom A. Bogaard, Thomas Zieher, Jan Pfeiffer, and Roderik C. Lindenbergh
Nat. Hazards Earth Syst. Sci., 23, 3723–3745, https://doi.org/10.5194/nhess-23-3723-2023, https://doi.org/10.5194/nhess-23-3723-2023, 2023
Short summary
Short summary
Landslides are one of the major weather-related geohazards. To assess their potential impact and design mitigation solutions, a detailed understanding of the slope is required. We tested if the use of machine learning, combined with satellite remote sensing data, would allow us to forecast deformation. Our results on the Vögelsberg landslide, a deep-seated landslide near Innsbruck, Austria, show that the formulation of such a machine learning system is not as straightforward as often hoped for.
Xabier Blanch, Marta Guinau, Anette Eltner, and Antonio Abellan
Nat. Hazards Earth Syst. Sci., 23, 3285–3303, https://doi.org/10.5194/nhess-23-3285-2023, https://doi.org/10.5194/nhess-23-3285-2023, 2023
Short summary
Short summary
We present cost-effective photogrammetric systems for high-resolution rockfall monitoring. The paper outlines the components, assembly, and programming codes required. The systems utilize prime cameras to generate 3D models and offer comparable performance to lidar for change detection monitoring. Real-world applications highlight their potential in geohazard monitoring which enables accurate detection of pre-failure deformation and rockfalls with a high temporal resolution.
Vipasana Sharma, Sushil Kumar, and Rama Sushil
Nat. Hazards Earth Syst. Sci., 23, 2523–2530, https://doi.org/10.5194/nhess-23-2523-2023, https://doi.org/10.5194/nhess-23-2523-2023, 2023
Short summary
Short summary
Snow avalanches are a natural hazard that can cause danger to human lives. This threat can be reduced by accurate prediction of the danger levels. The development of mathematical models based on past data and present conditions can help to improve the accuracy of prediction. This research aims to develop a neural-network-based model for correlating complex relationships between the meteorological variables and the profile variables.
Guillermo Tamburini-Beliveau, Sebastián Balbarani, and Oriol Monserrat
Nat. Hazards Earth Syst. Sci., 23, 1987–1999, https://doi.org/10.5194/nhess-23-1987-2023, https://doi.org/10.5194/nhess-23-1987-2023, 2023
Short summary
Short summary
Landslides and ground deformation associated with the construction of a hydropower mega dam in the Santa Cruz River in Argentine Patagonia have been monitored using radar and optical satellite data, together with the analysis of technical reports. This allowed us to assess the integrity of the construction, providing a new and independent dataset. We have been able to identify ground deformation trends that put the construction works at risk.
Adrian Wicki, Peter Lehmann, Christian Hauck, and Manfred Stähli
Nat. Hazards Earth Syst. Sci., 23, 1059–1077, https://doi.org/10.5194/nhess-23-1059-2023, https://doi.org/10.5194/nhess-23-1059-2023, 2023
Short summary
Short summary
Soil wetness measurements are used for shallow landslide prediction; however, existing sites are often located in flat terrain. Here, we assessed the ability of monitoring sites at flat locations to detect critically saturated conditions compared to if they were situated at a landslide-prone location. We found that differences exist but that both sites could equally well distinguish critical from non-critical conditions for shallow landslide triggering if relative changes are considered.
Jan Freihardt and Othmar Frey
Nat. Hazards Earth Syst. Sci., 23, 751–770, https://doi.org/10.5194/nhess-23-751-2023, https://doi.org/10.5194/nhess-23-751-2023, 2023
Short summary
Short summary
In Bangladesh, riverbank erosion occurs every year during the monsoon and affects thousands of households. Information on locations and extent of past erosion can help anticipate where erosion might occur in the upcoming monsoon season and to take preventive measures. In our study, we show how time series of radar satellite imagery can be used to retrieve information on past erosion events shortly after the monsoon season using a novel interactive online tool based on the Google Earth Engine.
Jiale Qian, Yunyan Du, Jiawei Yi, Fuyuan Liang, Nan Wang, Ting Ma, and Tao Pei
Nat. Hazards Earth Syst. Sci., 23, 317–328, https://doi.org/10.5194/nhess-23-317-2023, https://doi.org/10.5194/nhess-23-317-2023, 2023
Short summary
Short summary
Human activities across China show a similar trend in response to rains. However, urban resilience varies significantly by region. The northwestern arid region and the central underdeveloped areas are very fragile, and even low-intensity rains can trigger significant human activity anomalies. By contrast, even high-intensity rains might not affect residents in the southeast.
Orlando García-Feal, José González-Cao, Diego Fernández-Nóvoa, Gonzalo Astray Dopazo, and Moncho Gómez-Gesteira
Nat. Hazards Earth Syst. Sci., 22, 3859–3874, https://doi.org/10.5194/nhess-22-3859-2022, https://doi.org/10.5194/nhess-22-3859-2022, 2022
Short summary
Short summary
Extreme events have increased in the last few decades; having a good estimation of the outflow of a reservoir can be an advantage for water management or early warning systems. This study analyzes the efficiency of different machine learning techniques to predict reservoir outflow. The results obtained showed that the proposed models provided a good estimation of the outflow of the reservoirs, improving the results obtained with classical approaches.
Seok Bum Hong, Hong Sik Yun, Sang Guk Yum, Seung Yeop Ryu, In Seong Jeong, and Jisung Kim
Nat. Hazards Earth Syst. Sci., 22, 3435–3459, https://doi.org/10.5194/nhess-22-3435-2022, https://doi.org/10.5194/nhess-22-3435-2022, 2022
Short summary
Short summary
This study advances previous models through machine learning and multi-sensor-verified results. Using spatial and meteorological data from the study area (Suncheon–Wanju Highway in Gurye-gun), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with the geographic information system (m2). Based on the model results, multiple sensors were buried at four selected points in the study area, and the model was compared with sensor data.
Edward E. Salakpi, Peter D. Hurley, James M. Muthoka, Adam B. Barrett, Andrew Bowell, Seb Oliver, and Pedram Rowhani
Nat. Hazards Earth Syst. Sci., 22, 2703–2723, https://doi.org/10.5194/nhess-22-2703-2022, https://doi.org/10.5194/nhess-22-2703-2022, 2022
Short summary
Short summary
The devastating effects of recurring drought conditions are mostly felt by pastoralists that rely on grass and shrubs as fodder for their animals. Using historical information from precipitation, soil moisture, and vegetation health data, we developed a model that can forecast vegetation condition and the probability of drought occurrence up till a 10-week lead time with an accuracy of 74 %. Our model can be adopted by policymakers and relief agencies for drought early warning and early action.
Edward E. Salakpi, Peter D. Hurley, James M. Muthoka, Andrew Bowell, Seb Oliver, and Pedram Rowhani
Nat. Hazards Earth Syst. Sci., 22, 2725–2749, https://doi.org/10.5194/nhess-22-2725-2022, https://doi.org/10.5194/nhess-22-2725-2022, 2022
Short summary
Short summary
The impact of drought may vary in a given region depending on whether it is dominated by trees, grasslands, or croplands. The differences in impact can also be the agro-ecological zones within the region. This paper proposes a hierarchical Bayesian model (HBM) for forecasting vegetation condition in spatially diverse areas. Compared to a non-hierarchical model, the HBM proved to be a more natural method for forecasting drought in areas with different land covers and
agro-ecological zones.
Weijie Zou, Yi Zhou, Shixin Wang, Futao Wang, Litao Wang, Qing Zhao, Wenliang Liu, Jinfeng Zhu, Yibing Xiong, Zhenqing Wang, and Gang Qin
Nat. Hazards Earth Syst. Sci., 22, 2081–2097, https://doi.org/10.5194/nhess-22-2081-2022, https://doi.org/10.5194/nhess-22-2081-2022, 2022
Short summary
Short summary
Landslide dams are secondary disasters caused by landslides, which can cause great damage to mountains. We have proposed a procedure to calculate the key parameters of these dams that uses only a single remote-sensing image and a pre-landslide DEM combined with landslide theory. The core of this study is a modeling problem. We have found the bridge between the theory of landslide dams and the requirements of disaster relief.
C. Scott Watson, John R. Elliott, Susanna K. Ebmeier, María Antonieta Vásquez, Camilo Zapata, Santiago Bonilla-Bedoya, Paulina Cubillo, Diego Francisco Orbe, Marco Córdova, Jonathan Menoscal, and Elisa Sevilla
Nat. Hazards Earth Syst. Sci., 22, 1699–1721, https://doi.org/10.5194/nhess-22-1699-2022, https://doi.org/10.5194/nhess-22-1699-2022, 2022
Short summary
Short summary
We assess how greenspaces could guide risk-informed planning and reduce disaster risk for the urbanising city of Quito, Ecuador, which experiences earthquake, volcano, landslide, and flood hazards. We use satellite data to evaluate the use of greenspaces as safe spaces following an earthquake. We find disparities regarding access to and availability of greenspaces. The availability of greenspaces that could contribute to community resilience is high; however, many require official designation.
Seth Bryant, Heather McGrath, and Mathieu Boudreault
Nat. Hazards Earth Syst. Sci., 22, 1437–1450, https://doi.org/10.5194/nhess-22-1437-2022, https://doi.org/10.5194/nhess-22-1437-2022, 2022
Short summary
Short summary
The advent of new satellite technologies improves our ability to study floods. While the depth of water at flooded buildings is generally the most important variable for flood researchers, extracting this accurately from satellite data is challenging. The software tool presented here accomplishes this, and tests show the tool is more accurate than competing tools. This achievement unlocks more detailed studies of past floods and improves our ability to plan for and mitigate disasters.
Tadas Nikonovas, Allan Spessa, Stefan H. Doerr, Gareth D. Clay, and Symon Mezbahuddin
Nat. Hazards Earth Syst. Sci., 22, 303–322, https://doi.org/10.5194/nhess-22-303-2022, https://doi.org/10.5194/nhess-22-303-2022, 2022
Short summary
Short summary
Extreme fire episodes in Indonesia emit large amounts of greenhouse gasses and have negative effects on human health in the region. In this study we show that such burning events can be predicted several months in advance in large parts of Indonesia using existing seasonal climate forecasts and forest cover change datasets. A reliable early fire warning system would enable local agencies to prepare and mitigate the worst of the effects.
Yahong Liu and Jin Zhang
Nat. Hazards Earth Syst. Sci., 22, 227–244, https://doi.org/10.5194/nhess-22-227-2022, https://doi.org/10.5194/nhess-22-227-2022, 2022
Short summary
Short summary
Through a comprehensive analysis of the current remote sensing technology resources, this paper establishes the database to realize the unified management of heterogeneous sensor resources and proposes a capability evaluation method of remote sensing cooperative technology in geohazard emergencies, providing a decision-making basis for the establishment of remote sensing cooperative observations in geohazard emergencies.
Diego Guenzi, Danilo Godone, Paolo Allasia, Nunzio Luciano Fazio, Michele Perrotti, and Piernicola Lollino
Nat. Hazards Earth Syst. Sci., 22, 207–212, https://doi.org/10.5194/nhess-22-207-2022, https://doi.org/10.5194/nhess-22-207-2022, 2022
Short summary
Short summary
In the Apulia region (southeastern Italy) we are monitoring a soft-rock coastal cliff using webcams and strain sensors. In this urban and touristic area, coastal recession is extremely rapid and rockfalls are very frequent. In our work we are using low-cost and open-source hardware and software, trying to correlate both meteorological information with measures obtained from crack meters and webcams, aiming to recognize potential precursor signals that could be triggered by instability phenomena.
Natalie Brožová, Tommaso Baggio, Vincenzo D'Agostino, Yves Bühler, and Peter Bebi
Nat. Hazards Earth Syst. Sci., 21, 3539–3562, https://doi.org/10.5194/nhess-21-3539-2021, https://doi.org/10.5194/nhess-21-3539-2021, 2021
Short summary
Short summary
Surface roughness plays a great role in natural hazard processes but is not always well implemented in natural hazard modelling. The results of our study show how surface roughness can be useful in representing vegetation and ground structures, which are currently underrated. By including surface roughness in natural hazard modelling, we could better illustrate the processes and thus improve hazard mapping, which is crucial for infrastructure and settlement planning in mountainous areas.
Hugues Brenot, Nicolas Theys, Lieven Clarisse, Jeroen van Gent, Daniel R. Hurtmans, Sophie Vandenbussche, Nikolaos Papagiannopoulos, Lucia Mona, Timo Virtanen, Andreas Uppstu, Mikhail Sofiev, Luca Bugliaro, Margarita Vázquez-Navarro, Pascal Hedelt, Michelle Maree Parks, Sara Barsotti, Mauro Coltelli, William Moreland, Simona Scollo, Giuseppe Salerno, Delia Arnold-Arias, Marcus Hirtl, Tuomas Peltonen, Juhani Lahtinen, Klaus Sievers, Florian Lipok, Rolf Rüfenacht, Alexander Haefele, Maxime Hervo, Saskia Wagenaar, Wim Som de Cerff, Jos de Laat, Arnoud Apituley, Piet Stammes, Quentin Laffineur, Andy Delcloo, Robertson Lennart, Carl-Herbert Rokitansky, Arturo Vargas, Markus Kerschbaum, Christian Resch, Raimund Zopp, Matthieu Plu, Vincent-Henri Peuch, Michel Van Roozendael, and Gerhard Wotawa
Nat. Hazards Earth Syst. Sci., 21, 3367–3405, https://doi.org/10.5194/nhess-21-3367-2021, https://doi.org/10.5194/nhess-21-3367-2021, 2021
Short summary
Short summary
The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986).
Johnny Douvinet, Anna Serra-Llobet, Esteban Bopp, and G. Mathias Kondolf
Nat. Hazards Earth Syst. Sci., 21, 2899–2920, https://doi.org/10.5194/nhess-21-2899-2021, https://doi.org/10.5194/nhess-21-2899-2021, 2021
Short summary
Short summary
This study proposes to combine results of research regarding the spatial inequalities due to the siren coverage, the political dilemma of siren activation, and the social problem of siren awareness and trust for people in France. Surveys were conducted using a range of complementary methods (GIS analysis, statistical analysis, questionnaires, interviews) through different scales. Results show that siren coverage in France is often determined by population density but not risks or disasters.
Fabio Brighenti, Francesco Carnemolla, Danilo Messina, and Giorgio De Guidi
Nat. Hazards Earth Syst. Sci., 21, 2881–2898, https://doi.org/10.5194/nhess-21-2881-2021, https://doi.org/10.5194/nhess-21-2881-2021, 2021
Short summary
Short summary
In this paper we propose a methodology to mitigate hazard in a natural environment in an urbanized context. The deformation of the ground is a precursor of paroxysms in mud volcanoes. Therefore, through the analysis of the deformation supported by a statistical approach, this methodology was tested to reduce the hazard around the mud volcano. In the future, the goal is that this dangerous area will become both a naturalistic heritage and a source of development for the community of the area.
Doris Hermle, Markus Keuschnig, Ingo Hartmeyer, Robert Delleske, and Michael Krautblatter
Nat. Hazards Earth Syst. Sci., 21, 2753–2772, https://doi.org/10.5194/nhess-21-2753-2021, https://doi.org/10.5194/nhess-21-2753-2021, 2021
Short summary
Short summary
Multispectral remote sensing imagery enables landslide detection and monitoring, but its applicability to time-critical early warning is rarely studied. We present a concept to operationalise its use for landslide early warning, aiming to extend lead time. We tested PlanetScope and unmanned aerial system images on a complex mass movement and compared processing times to historic benchmarks. Acquired data are within the forecasting window, indicating the feasibility for landslide early warning.
Michal Bíl, Pavel Raška, Lukáš Dolák, and Jan Kubeček
Nat. Hazards Earth Syst. Sci., 21, 2581–2596, https://doi.org/10.5194/nhess-21-2581-2021, https://doi.org/10.5194/nhess-21-2581-2021, 2021
Short summary
Short summary
The online landslide database CHILDA (Czech Historical Landslide Database) summarises information about landslides which occurred in the area of Czechia (the Czech Republic). The database is freely accessible via the https://childa.cz/ website. It includes 699 records (spanning the period of 1132–1989). Overall, 55 % of all recorded landslide events occurred only within 15 years of the extreme landslide incidence.
Anna Kruspe, Jens Kersten, and Friederike Klan
Nat. Hazards Earth Syst. Sci., 21, 1825–1845, https://doi.org/10.5194/nhess-21-1825-2021, https://doi.org/10.5194/nhess-21-1825-2021, 2021
Short summary
Short summary
Messages on social media can be an important source of information during crisis situations. This article reviews approaches for the reliable detection of informative messages in a flood of data. We demonstrate the varying goals of these approaches and present existing data sets. We then compare approaches based (1) on keyword and location filtering, (2) on crowdsourcing, and (3) on machine learning. We also point out challenges and suggest future research.
Enrique Guillermo Cordaro, Patricio Venegas-Aravena, and David Laroze
Nat. Hazards Earth Syst. Sci., 21, 1785–1806, https://doi.org/10.5194/nhess-21-1785-2021, https://doi.org/10.5194/nhess-21-1785-2021, 2021
Short summary
Short summary
We developed a methodology that generates free externally disturbed magnetic variations in ground magnetometers close to the Chilean convergent margin. Spectral analysis (~ mHz) and magnetic anomalies increased prior to large Chilean earthquakes (Maule 2010, Mw 8.8; Iquique 2014, Mw 8.2; Illapel 2015, Mw 8.3). These findings relate to microcracks within the lithosphere due to stress state changes. This physical evidence should be thought of as a last stage of the earthquake preparation process.
Corey M. Scheip and Karl W. Wegmann
Nat. Hazards Earth Syst. Sci., 21, 1495–1511, https://doi.org/10.5194/nhess-21-1495-2021, https://doi.org/10.5194/nhess-21-1495-2021, 2021
Short summary
Short summary
For many decades, natural disasters have been monitored by trained analysts using multiple satellite images to observe landscape change. This approach is incredibly useful, but our new tool, HazMapper, offers researchers and the scientifically curious public a web-accessible
cloud-based tool to perform similar analysis. We intend for the tool to both be used in scientific research and provide rapid response to global natural disasters like landslides, wildfires, and volcanic eruptions.
Matti Wiegmann, Jens Kersten, Hansi Senaratne, Martin Potthast, Friederike Klan, and Benno Stein
Nat. Hazards Earth Syst. Sci., 21, 1431–1444, https://doi.org/10.5194/nhess-21-1431-2021, https://doi.org/10.5194/nhess-21-1431-2021, 2021
Short summary
Short summary
In this paper, we study when social media is an adequate source to find metadata about incidents that cannot be acquired by traditional means. We identify six major use cases: impact assessment and verification of model predictions, narrative generation, recruiting citizen volunteers, supporting weakly institutionalized areas, narrowing surveillance areas, and reporting triggers for periodical surveillance.
Hui Liu, Ya Hao, Wenhao Zhang, Hanyue Zhang, Fei Gao, and Jinping Tong
Nat. Hazards Earth Syst. Sci., 21, 1179–1194, https://doi.org/10.5194/nhess-21-1179-2021, https://doi.org/10.5194/nhess-21-1179-2021, 2021
Short summary
Short summary
We trained a recurrent neural network model to classify microblogging posts related to urban waterlogging and establish an online monitoring system of urban waterlogging caused by flood disasters. We manually curated more than 4400 waterlogging posts to train the RNN model so that it can precisely identify waterlogging-related posts of Sina Weibo to timely determine urban waterlogging.
Roope Tervo, Ilona Láng, Alexander Jung, and Antti Mäkelä
Nat. Hazards Earth Syst. Sci., 21, 607–627, https://doi.org/10.5194/nhess-21-607-2021, https://doi.org/10.5194/nhess-21-607-2021, 2021
Short summary
Short summary
Predicting the number of power outages caused by extratropical storms is a key challenge for power grid operators. We introduce a novel method to predict the storm severity for the power grid employing ERA5 reanalysis data combined with a forest inventory. The storms are first identified from the data and then classified using several machine-learning methods. While there is plenty of room to improve, the results are already usable, with support vector classifier providing the best performance.
Michaela Wenner, Clément Hibert, Alec van Herwijnen, Lorenz Meier, and Fabian Walter
Nat. Hazards Earth Syst. Sci., 21, 339–361, https://doi.org/10.5194/nhess-21-339-2021, https://doi.org/10.5194/nhess-21-339-2021, 2021
Short summary
Short summary
Mass movements constitute a risk to property and human life. In this study we use machine learning to automatically detect and classify slope failure events using ground vibrations. We explore the influence of non-ideal though commonly encountered conditions: poor network coverage, small number of events, and low signal-to-noise ratios. Our approach enables us to detect the occurrence of rare events of high interest in a large data set of more than a million windowed seismic signals.
Luiz Felipe Galizia, Thomas Curt, Renaud Barbero, and Marcos Rodrigues
Nat. Hazards Earth Syst. Sci., 21, 73–86, https://doi.org/10.5194/nhess-21-73-2021, https://doi.org/10.5194/nhess-21-73-2021, 2021
Short summary
Short summary
This paper aims to provide a quantitative evaluation of three remotely sensed fire datasets which have recently emerged as an important resource to improve our understanding of fire regimes. Our findings suggest that remotely sensed fire datasets can be used to proxy variations in fire activity on monthly and annual timescales; however, caution is advised when drawing information from smaller fires (< 100 ha) across the Mediterranean region.
Philippe Weyrich, Anna Scolobig, Florian Walther, and Anthony Patt
Nat. Hazards Earth Syst. Sci., 20, 2811–2821, https://doi.org/10.5194/nhess-20-2811-2020, https://doi.org/10.5194/nhess-20-2811-2020, 2020
Patric Kellermann, Kai Schröter, Annegret H. Thieken, Sören-Nils Haubrock, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 20, 2503–2519, https://doi.org/10.5194/nhess-20-2503-2020, https://doi.org/10.5194/nhess-20-2503-2020, 2020
Short summary
Short summary
The flood damage database HOWAS 21 contains object-specific flood damage data resulting from fluvial, pluvial and groundwater flooding. The datasets incorporate various variables of flood hazard, exposure, vulnerability and direct tangible damage at properties from several economic sectors. This paper presents HOWAS 21 and highlights exemplary analyses to demonstrate the use of HOWAS 21 flood damage data.
Giuseppe Esposito, Ivan Marchesini, Alessandro Cesare Mondini, Paola Reichenbach, Mauro Rossi, and Simone Sterlacchini
Nat. Hazards Earth Syst. Sci., 20, 2379–2395, https://doi.org/10.5194/nhess-20-2379-2020, https://doi.org/10.5194/nhess-20-2379-2020, 2020
Short summary
Short summary
In this article, we present an automatic processing chain aimed to support the detection of landslides that induce sharp land cover changes. The chain exploits free software and spaceborne SAR data, allowing the systematic monitoring of wide mountainous regions exposed to mass movements. In the test site, we verified a general accordance between the spatial distribution of seismically induced landslides and the detected land cover changes, demonstrating its potential use in emergency management.
Mohammad Malakootian and Majid Nozari
Nat. Hazards Earth Syst. Sci., 20, 2351–2363, https://doi.org/10.5194/nhess-20-2351-2020, https://doi.org/10.5194/nhess-20-2351-2020, 2020
Short summary
Short summary
The present study estimated the Kerman–Baghin aquifer vulnerability using DRASTIC and composite DRASTIC (CDRASTIC) indices with the aid of geographic information system (GIS) techniques. The aquifer vulnerability maps indicated very similar results, identifying the north-west parts of the aquifer as areas with high to very high vulnerability. According to the results, parts of the studied aquifer have a high vulnerability and require protective measures.
Diana Contreras, Alondra Chamorro, and Sean Wilkinson
Nat. Hazards Earth Syst. Sci., 20, 1663–1687, https://doi.org/10.5194/nhess-20-1663-2020, https://doi.org/10.5194/nhess-20-1663-2020, 2020
Short summary
Short summary
The socio-economic condition of the population determines their vulnerability to earthquakes, tsunamis, volcanic eruptions, landslides, soil erosion and land degradation. This condition is estimated mainly from population censuses. The lack to access to basic services, proximity to hazard zones, poverty and population density highly influence the vulnerability of communities. Mapping the location of this vulnerable population makes it possible to prevent and mitigate their risk.
Simona Colombelli, Francesco Carotenuto, Luca Elia, and Aldo Zollo
Nat. Hazards Earth Syst. Sci., 20, 921–931, https://doi.org/10.5194/nhess-20-921-2020, https://doi.org/10.5194/nhess-20-921-2020, 2020
Short summary
Short summary
We developed a mobile app for Android devices which receives the alerts generated by a network-based early warning system, predicts the expected ground-shaking intensity and the available lead time at the user position, and provides customized messages to inform the user about the proper reaction to the alert. The app represents a powerful tool for informing in real time a wide audience of end users and stakeholders about the potential damaging shaking in the occurrence of an earthquake.
Richard Styron, Julio García-Pelaez, and Marco Pagani
Nat. Hazards Earth Syst. Sci., 20, 831–857, https://doi.org/10.5194/nhess-20-831-2020, https://doi.org/10.5194/nhess-20-831-2020, 2020
Short summary
Short summary
The Caribbean and Central American region is both tectonically active and densely populated, leading to a large population that is exposed to earthquake hazards. Until now, no comprehensive fault data covering the region have been available. We present a new public fault database for Central America and the Caribbean that synthesizes published studies with new mapping from remote sensing to provide fault sources for the CCARA seismic hazard and risk analysis project and to aid future research.
María del Pilar Jiménez-Donaire, Ana Tarquis, and Juan Vicente Giráldez
Nat. Hazards Earth Syst. Sci., 20, 21–33, https://doi.org/10.5194/nhess-20-21-2020, https://doi.org/10.5194/nhess-20-21-2020, 2020
Short summary
Short summary
A new combined drought indicator (CDI) is proposed that integrates rainfall, soil moisture and vegetation dynamics. The performance of this indicator was evaluated against crop damage data from agricultural insurance schemes in five different areas in SW Spain. Results show that this indicator was able to predict important droughts in 2004–2005 and 2011–2012, marked by crop damage of between 70 % and 95 % of the total insured area. This opens important applications for improving insurance schemes.
Quancai Xie, Qiang Ma, Jingfa Zhang, and Haiying Yu
Nat. Hazards Earth Syst. Sci., 19, 2827–2839, https://doi.org/10.5194/nhess-19-2827-2019, https://doi.org/10.5194/nhess-19-2827-2019, 2019
Short summary
Short summary
This paper evaluates a new method for modeling the site amplification factor. Through implementing this method and making simulations for different cases, we find that this method shows better performance than the previous method and JMA report. We better understand the advantages and disadvantages of this method, although there are some problems that need to be considered carefully and solved; it shows good potential to be used in future earthquake early warning systems.
Cited articles
Advanced Rapid Imaging and Analysis (ARIA): ARIA Damage Proxy Map for the 2015 Gorkha earthquake, Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology [data set], https://aria-share.jpl.nasa.gov/20150425-Nepal_EQ/DPM/, last access: 15 February 2023a. a
Advanced Rapid Imaging and Analysis (ARIA): ARIA Damage Proxy Map for the 2017 Puebla earthquake, Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology [data set], https://aria-share.jpl.nasa.gov/20170919-M7.1_Raboso_Mexico_EQ/DPM/, last access: 15 February 2023b. a
Advanced Rapid Imaging and Analysis (ARIA): ARIA Damage Proxy Map for the 2020 Puerto Rico earthquake, Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology [data set], https://aria-share.jpl.nasa.gov/20200106-Puerto_Rico_EQ/DPM/, last access: 15 February 2023c. a
Advanced Rapid Imaging and Analysis (ARIA): ARIA Damage Proxy Map for the 2020 Zagreb earthquake, Advanced Rapid Imaging and Analysis (ARIA) team at NASA's Jet Propulsion Laboratory and California Institute of Technology [data set], https://aria-share.jpl.nasa.gov/20200322_Zagreb_EQ/DPM/, last access: 15 February 2023d. a
Bai, Y., Adriano, B., Mas, E., and Koshimura, S.: Machine learning based building damage mapping from the ALOS-2/PALSAR-2 SAR imagery: Case study of 2016 Kumamoto earthquake, Journal of Disaster Research, 12, 646–655, https://doi.org/10.20965/jdr.2017.p0646, 2017. a, b, c
Breiman, L.: Random forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/A:1010933404324, 2001. a
Brodersen, K. H., Ong, C. S., Stephan, K. E., and Buhmann, J. M.: The balanced accuracy and its posterior distribution, in: International Conference on Pattern Recognition, 23–26 August 2010, Istanbul, Turkey, IEEE, 3121–3124, https://doi.org/10.1109/ICPR.2010.764, 2010. a
Buendía Sánchez, L. M. and Angulo, E. R.: Análisis de los daños en viviendas y edificios comerciales durante la ocurrencia del sismo del 19 de septiembre de 2017, Revista de Ingeniería
Sísmica No, 101, 19–35, https://doi.org/10.18867/ris.101.508, 2017. a, b
Capella Space: SAR Imagery Products, https://www.capellaspace.com/data/sar-imagery-products/, last access: 30 December 2022. a
Copernicus Emergency Management Service and The European Commission: Copernicus rapid damage assessment and mapping service, https://emergency.copernicus.eu/mapping/ems/damage-assessment (last access: 30 December 2022), 2012. a
Cotrufo, S., Sandu, C., Giulio Tonolo, F., and Boccardo, P.: Building damage assessment scale tailored to remote sensing vertical imagery, Eur. J. Remote Sens., 51, 991–1005,
https://doi.org/10.1080/22797254.2018.1527662, 2018. a
Dell'Acqua, F. and Gamba, P.: Remote sensing and earthquake damage assessment: Experiences, limits, and perspectives, P. IEEE, 100,
2876–2890, https://doi.org/10.1109/JPROC.2012.2196404, 2012. a, b
Dong, L. and Shan, J.: A comprehensive review of earthquake-induced building damage detection with remote sensing techniques, ISPRS J. Photogramm., 84, 85–99, https://doi.org/10.1016/j.isprsjprs.2013.06.011, 2013. a
Esri: Enhanced Damage Assessment Solution Improves Collaboration,
https://www.esri.com/arcgis-blog/products/arcgis-solutions/local-government/enhanced-damage-assessment-solution-released/ (last access: 30 December 2022), 2021. a
European Sesimological Commission: European Macroseismic Scale 1998 (EMS-98), Tech. rep., Centre Europèen de Géodynamique et de Séismologie,
Luxembourg, ISBN 2-87977-008-4, 1998. a
Federal Emergency Management Agency: FEMA-4473-DR. Preliminary Damage Assessment Report, Puerto Rico Earthquakes, Tech. rep., Federal Emergency Management Agency, https://www.fema.gov/sites/default/files/2020-03/FEMA4473DRPR.pdf (last access: 30 December 2022), 2020. a
FEMA and ASCE: FEMA 356: Prestandard and commentary for the seismic
rehabilitation of buildings, Tech. rep., Federal Emergency Management
Agency, Washington, DC, ISBN 978-1484027554, 2000. a
FEMA Geospatial Resource Center: Puerto Rico Mw 6.4 Earthquake Preliminary Damage Assessments Dashboard, Federal Emergency Management Agency [data set], https://gis-fema.hub.arcgis.com/apps/FEMA::puerto-rico-m-6-4-earthquake-preliminary-damage-assessments-dashboard/explore (last access: 15 February 2023), 2020. a
Feng, Y., Zhou, M., and Tong, X.: Imbalanced classification: A paradigm-based review, Stat. Anal. Data Min., 14, 383–406,
https://doi.org/10.1002/sam.11538, 2021. a
Ge, P., Gokon, H., and Meguro, K.: Building Damage Assessment Using Intensity SAR Data with Different Incidence Angles and Longtime Interval, Journal of Disaster Research, 14, 456–465, https://doi.org/10.20965/JDR.2019.P0456, 2019. a
Ge, P., Gokon, H., and Meguro, K.: A review on synthetic aperture radar-based building damage assessment in disasters, Remote Sens. Environ., 240, 111693, https://doi.org/10.1016/j.rse.2020.111693, 2020. a, b
Gholamy, A., Kreinovich, V., and Kosheleva, O.: Why 70/30 or 80/20 Relation Between Training and Testing Sets: A Pedagogical Explanation, Departmental Technical Reports (CS), Department of Computer Science, The University of Texas at El Paso, 1–6, https://scholarworks.utep.edu/cs_techrep/1209/ (last access: 30 December 2022), 2018. a
Government of Croatia: Croatia earthquake: Rapid Damage and Needs Assessment, Tech. rep., Government of Croatia, Zagreb, Croatia, https://documents1.worldbank.org/curated/en/311901608097332728/pdf/Croatia-Earthquake-Rapid-Damage-and-Needs-Assessment-2020.pdf (last access: 15 February 2023), 2020. a
Grinsztajn, L., Oyallon, E., and Varoquaux, G.: Why do tree-based models still outperform deep learning on tabular data?, arXiv [preprint], https://doi.org/10.48550/arXiv.2207.08815, 18 July 2022. a
Humanitarian OpenStreetMap Team (HOT) and OpenStreetMap contributors: Nepal Buildings, Humanitarian Data Exchange, Humanitarian OpenStreetMap Team (HOT) and OpenStreetMap contributors [data set], https://data.humdata.org/dataset/hotosm_npl_buildings (last access: 15 February 2023), 2020a. a
Humanitarian OpenStreetMap Team (HOT) and OpenStreetMap contributors: Puerto Rico Buildings, Humanitarian Data Exchange, Humanitarian OpenStreetMap Team (HOT) and OpenStreetMap contributors [data set], https://data.humdata.org/dataset/hotosm_pri_buildings (last access: 15 February 2023), 2020b. a
ICEYE: A Revolution in Synthetic Aperture Radar (SAR) Data Earth Observation, https://www.iceye.com/hubfs/Downloadables/SAR_Data_Brochure_ICEYE.pdf, last access: 30 December 2022. a
Ji, M., Liu, L., and Buchroithner, M. F.: Identifying collapsed buildings using post-earthquake satellite imagery and convolutional neural networks: A
case study of the 2010 Haiti Earthquake, Remote Sens., 10, 1689,
https://doi.org/10.3390/rs10111689, 2018. a
Ji, M., Liu, L., Du, R., and Buchroithner, M. F.: A comparative study of
texture and convolutional neural network features for detecting collapsed
buildings after earthquakes using pre- and post-event satellite imagery,
Remote Sens., 11, 1202, https://doi.org/10.3390/rs11101202, 2019. a
Ji, M., Liu, L., Zhang, R., and Buchroithner, M. F.: Discrimination of
Earthquake-Induced Building Destruction from Space Using a Pretrained CNN
Model, Appl. Sci., 10, 602, https://doi.org/10.3390/app10020602, 2020. a
Jung, J., Kim, D. J., Lavalle, M., and Yun, S.-H.: Coherent Change Detection
Using InSAR Temporal Decorrelation Model: A Case Study for Volcanic Ash
Detection, IEEE T. Geosci. Remote, 54, 5765–5775, https://doi.org/10.1109/TGRS.2016.2572166, 2016. a
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu,
T. Y.: LightGBM: A highly efficient gradient boosting decision tree, in:
Advances in Neural Information Processing Systems, 4–9 December 2017, Long Beach, California, USA, Neural Information Processing Systems Foundation, Inc. (NeurIPS), ISBN 9781510860964, https://github.com/Microsoft/LightGBM (last access: 30 December 2022), 2017. a
Kellogg, K., Rosen, P., Barela, P., Hoffman, P., Edelstein, W., Standley, S.,
Dunn, C., Guerrero, A. M., Harinath, N., Shaffer, S., Baker, C., and
Xaypraseuth, P.: NASA-ISRO Synthetic Aperture Radar (NISAR) Mission, in:
IEEE Aerospace Conference, 7–14 March 2020, Big Sky, USA, IEEE, https://doi.org/10.1109/AERO47225.2020.9172638, 2020. a, b
Krawczyk, B., Woźniak, M., and Schaefer, G.: Cost-sensitive decision tree ensembles for effective imbalanced classification, Appl. Soft Comput. J., 14, 554–562, https://doi.org/10.1016/j.asoc.2013.08.014, 2014. a
Lee, J., Xu, J. Z., Sohn, K., Lu, W., Berthelot, D., Gur, I., Khaitan, P., Ke-Wei, Huang, Koupparis, K., and Kowatsch, B.: Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques, in: NeurIPS 2020 Artificial Intelligence for Humanitarian Assistance and Disaster
Response Workshop, 6–12 December 2020, virtual, 1–10, arXiv [preprint], https://doi.org/10.48550/arXiv.2011.14004, 24 November 2020. a
Loos, S., Lallemant, D., Baker, J. W., McCaughey, J., Yun, S.-H., Budhathoki, N., Khan, F., and Singh, R.: G-DIF: A geospatial data integration framework
to rapidly estimate post-earthquake damage, Earthq. Spectra, 36,
1695–1718, https://doi.org/10.1177/8755293020926190, 2020. a
Mangalathu, S., Sun, H., Nweke, C. C., Yi, Z., and Burton, H. V.: Classifying Earthquake Damage to Buildings Using Machine Learning, Earthq. Spectra, 36, 183–208, https://doi.org/10.1177/8755293019878137, 2020. a, b
Microsoft: Open dataset of machine extracted buildings in Uganda and Tanzania, GitHub, https://github.com/microsoft/Uganda-Tanzania-Building-Footprints (last access: 30 December 2022), 2018a. a
Microsoft: Open dataset of machine extracted buildings in Uganda and Tanzania, Microsoft, https://www.microsoft.com/en-us/maps/building-footprints (last access: 30 December 2022), 2018b. a
Miura, H., Midorikawa, S., and Matsuoka, M.: Building damage assessment using high-resolution satellite SAR images of the 2010 Haiti earthquake, Earthq. Spectra, 32, 591–610, https://doi.org/10.1193/033014EQS042M, 2016. a
Motohka, T., Kankaku, Y., Miura, S., and Suzuki, S.: ALOS-4 L-Band SAR Observation Concept and Development Status, in: International Geoscience and
Remote Sensing Symposium (IGARSS), 26 September–2 October 2020, virtual, 3792–3794, https://doi.org/10.1109/IGARSS39084.2020.9323701, 2020. a
Moya, L., Mas, E., Adriano, B., Koshimura, S., Yamazaki, F., and Liu, W.: An integrated method to extract collapsed buildings from satellite imagery, hazard distribution and fragility curves, Int. J. Disast. Risk Re., 31, 1374–1384, https://doi.org/10.1016/j.ijdrr.2018.03.034, 2018a. a, b
Moya, L., Perez, L. R., Mas, E., Adriano, B., Koshimura, S., and Yamazaki, F.: Novel unsupervised classification of collapsed buildings using satellite
imagery, hazard scenarios and fragility functions, Remote Sens., 10, 296,
https://doi.org/10.3390/rs10020296, 2018b. a, b
National Planning Commission: Nepal earthquake 2015: Post-disaster need
assessment, Tech. rep., Government of Nepal, Kathmandu, Nepal,
https://doi.org/10.1007/978-981-13-6573-7_2, 2015. a
Natsuaki, R., Nagai, H., Tomii, N., and Tadono, T.: Sensitivity and limitation in damage detection for individual buildings using InSAR coherence – A case study in 2016 Kumamoto earthquakes, Remote Sens., 10, 245,
https://doi.org/10.3390/rs10020245, 2018. a
Nex, F., Duarte, D., Tonolo, F. G., and Kerle, N.: Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in
Operational Conditions, Remote Sens., 11, 2765, https://doi.org/10.3390/rs11232765, 2019. a, b
Office for Strategic Planning and City Development for the City of Zagreb: ZG3D: 3D model Grada Zagreba, Office for Strategic Planning and City Development for the City of Zagreb [data set], https://zagreb.gdi.net/zg3d/ (last access: 15 February 2023), 2021. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É.:
Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, https://doi.org/10.4018/978-1-5225-9902-9.ch008, 2011. a
Plank, S.: Rapid damage assessment by means of multi-temporal SAR-A
comprehensive review and outlook to Sentinel-1, Remote Sens., 6,
4870–4906, https://doi.org/10.3390/rs6064870, 2014. a
Probst, P., Wright, M. N., and Boulesteix, A. L.: Hyperparameters and tuning strategies for random forest, WIRES Data Min. Knowl., 9, 1–15, https://doi.org/10.1002/widm.1301, 2019. a, b
Rao, A.: GEMScienceTools/eo-damage-detection: v1.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.7578961, 2023. a
Reinoso, E., Quinde, P., Buendía, L., and Ramos, S.: Intensity and
damage statistics of the September 19, 2017 Mexico earthquake: Influence of
soft story and corner asymmetry on the damage reported during the
earthquake, Earthq. Spectra, 37, 1875–1899,
https://doi.org/10.1177/8755293020981981, 2021. a, b
Roeslin, S., Ma, Q., and García, H. J.: Damage assessment on buildings following the 19th September 2017 puebla, Mexico earthquake, Frontiers in Built Environment, 4, 1–18, https://doi.org/10.3389/fbuil.2018.00072, 2018. a
Roeslin, S., Ma, Q., Juárez-Garcia, H., Gómez-Bernal, A., Wicker, J., and Wotherspoon, L.: A machine learning damage prediction model for the
2017 Puebla-Morelos, Mexico, earthquake, Earthq. Spectra, 36, 314–339,
https://doi.org/10.1177/8755293020936714, 2020. a, b
Sextos, A., De Risi, R., Pagliaroli, A., Foti, S., Passeri, F., Ausilio, E., Cairo, R., Capatti, M. C., Chiabrando, F., Chiaradonna, A., Dashti, S., De Silva, F., Dezi, F., Durante, M. G., Giallini, S., Lanzo, G., Sica, S., Simonelli, A. L., and Zimmaro, P.: Local site effects and incremental damage
of buildings during the 2016 Central Italy Earthquake sequence, Earthq.
Spectra, 34, 1639–1669, https://doi.org/10.1193/100317EQS194M, 2018. a
Sirko, W., Kashubin, S., Ritter, M., Annkah, A., Bouchareb, Y. S. E., Dauphin, Y., Keysers, D., Neumann, M., Cisse, M., and Quinn, J.: Continental-Scale Building Detection from High Resolution Satellite Imagery, arXiv [preprint], https://doi.org/10.48550/arXiv.2107.12283, 26 July 2021. a
Stephenson, O. L., Kohne, T., Zhan, E., Cahill, B. E., Yun, S.-H., Ross, Z. E., and Simons, M.: Deep Learning-Based Damage Mapping With InSAR Coherence Time Series, IEEE T. Geosci. Remote, 60, 1–17,
https://doi.org/10.1109/tgrs.2021.3084209, 2021. a
Tilon, S., Nex, F., Kerle, N., and Vosselman, G.: Post-disaster building
damage detection from earth observation imagery using unsupervised and
transferable anomaly detecting generative adversarial networks, Remote
Sensi., 12, 1–27, https://doi.org/10.3390/rs12244193, 2020. a
United Nations Development Programme: Guidance Note: Household and Building Damage Assessment (HBDA), UNDP, https://data.undp.org/wp-content/uploads/2021/10/HBDA_Handbook_English-2.pdf (last access: 16 February 2023), 2021. a
United Nations Institute for Training and Research: UNOSAT Rapid Mapping Service, https://www.unitar.org/maps/unosat-rapid-mapping-service (last access: 2 April 2022), 2003. a
University of Zagreb and The City of Zagreb: The Database of Post-Earthquake Building Usability Classification, Croatian Centre of Earthquake Engineering (HCPI – Hrvatski Centar Za Potresno Inženjerstvo), Faculty of Civil Engineering, University of Zagreb and The City of Zagreb [data set], https://www.hcpi.hr/rezultati-procjena-ostecenja-gradevina-nakon-potresa-31
(last access: 10 April 2021), 2020. a
U.S. Geological Survey: USGS ShakeMap for the 2015 Gorkha earthquake, ShakeMap – Earthquake Ground Motion and Shaking Intensity Maps: U.S. Geological Survey [data set], https://earthquake.usgs.gov/earthquakes/eventpage/us20002926/shakemap/pga (last access: 15 February 2023), 2017a. a
U.S. Geological Survey: USGS ShakeMap for the 2017 Puebla earthquake, ShakeMap – Earthquake Ground Motion and Shaking Intensity Maps: U.S. Geological Survey [data set], https://earthquake.usgs.gov/earthquakes/eventpage/us2000ar20/shakemap/pga (last access: 15 February 2023), 2017b. a
U.S. Geological Survey: USGS ShakeMap for the 2020 Puerto Rico earthquake, ShakeMap – Earthquake Ground Motion and Shaking Intensity Maps: U.S. Geological Survey [data set], https://earthquake.usgs.gov/earthquakes/eventpage/us70006vll/shakemap/pga (last access: 15 February 2023), 2017c. a
U.S. Geological Survey: USGS ShakeMap for the 2020 Zagreb earthquake, ShakeMap – Earthquake Ground Motion and Shaking Intensity Maps: U.S. Geological Survey [data set], https://earthquake.usgs.gov/earthquakes/eventpage/us70008dx7/shakemap/pga (last access: 15 February 2023), 2017d. a
Wald, D. J., Worden, C. B., Thompson, E. M., and Hearne, M. G.: ShakeMap
operations, policies, and procedures, Earthq. Spectra, 38, 756–777,
https://doi.org/10.1177/87552930211030298, 2022. a
Wieland, M., Liu, W., and Yamazaki, F.: Learning change from Synthetic Aperture Radar images: Performance evaluation of a Support Vector Machine to
detect earthquake and tsunami-induced changes, Remote Sens., 8, 792,
https://doi.org/10.3390/rs8100792, 2016. a, b
Xie, B., Xu, J., Jung, J., Yun, S.-H., Zeng, E., Brooks, E. M., Dolk, M., and
Narasimhalu, L.: Machine Learning on Satellite Radar Images to Estimate
Damages After Natural Disasters, in: 28th International Conference on
Advances in Geographic Information Systems, 3–6 November 2020, Seattle, WA, USA, edited by: Lu, C.-T., Wang, F.,
Trajcevski, G., Huang, Y., Newsam, S., and Xiong, L., 461–464,
Association for Computing Machinery, New York, NY, United States, Seattle,
Washington, https://doi.org/10.1145/3397536.3422349, 2020.
a
Xu, H., Kinfu, K. A., LeVine, W., Panda, S., Dey, J., Ainsworth, M., Peng, Y.-C., Kusmanov, M., Engert, F., White, C. M., Vogelstein, J. T., and Priebe,
C. E.: When are Deep Networks really better than Decision Forests at small
sample sizes, and how?, arXiv [preprint], https://doi.org/10.48550/arXiv.2108.13637, 31 August 2021. a
Xu, J. Z., Lu, W., Li, Z., Khaitan, P., and Zaytseva, V.: Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks, in: 33rd Conference on Neural Information Processing Systems, NeurIPS 2019, 10–14 December 2019, Vancouver, Canada, arXiv [preprint], https://doi.org/10.48550/arXiv.1910.06444, 14 October 2019. a
Yun, S.-H., Fielding, E., Webb, F., and Simons, M.: Damage Proxy Map From Interferometric Synthetic Aperture Radar Coherence, Patent Public Search, https://image-ppubs.uspto.gov/dirsearch-public/print/downloadPdf/9207318 (last access: 15 February 2023), 2015. a
Yun, S.-H., Hudnut, K. W., Owen, S., Webb, F., Simons, M., Sacco, P., Gurrola, E., Manipon, G., Liang, C., Fielding, E., Milillo, P., Hua, H., and Coletta, A.: Rapid damage mapping for the 2015 Mw 7.8 Gorkha Earthquake Using synthetic aperture radar data from COSMO-SkyMed and ALOS-2 satellites, Seismol. Res. Lett., 86, 1549–1556, https://doi.org/10.1785/0220150152,
2015c. a
Short summary
This article presents a framework for semi-automated building damage assessment due to earthquakes from remote-sensing data and other supplementary datasets including high-resolution building inventories, while also leveraging recent advances in machine-learning algorithms. For three out of the four recent earthquakes studied, the machine-learning framework is able to identify over 50 % or nearly half of the damaged buildings successfully.
This article presents a framework for semi-automated building damage assessment due to...
Altmetrics
Final-revised paper
Preprint