The development of geographic information systems has grown significantly over the past decade. Simultaneously, the concept of smart cities based on the management of large volumes of data has also spread worldwide. The digital twin concept has recently been incorporated into the technological domain of urban management. However, currently, phases such as technological integration, standardization, data and process interconnection, the development of tools and utilities, professional training, and the application of digital urban development in real-world situations are converging. This paper presents the experience developed on a university campus, detailing each of the phases carried out from the initial design to a fully operational pilot phase model. The article highlights the importance of certain aspects to consider in each phase, demonstrating that there are barriers and limitations and at the same time, great strengths and opportunities in applying the digital twin model in urban management, considering aspects such as mobility, accessibility, energy management, and involving students and university administrators in the process.

We extend the conventional seismic risk assessment approach to open spaces in the 2011 Lorca earthquake scenario. Conventional approaches to seismic risk provide estimates of damaged buildings mainly related to structural failure. The damage related to the production of debris in damaged buildings and its spread in the surrounding space receives a secondary role. However, in many cases, this secondary damage is of prime importance. In this work, we consider the Mw5.2, 2011 Lorca earthquake (Spain), which caused nine fatalities associated with the fall of non-structural building parts. First, we analyze reports of emergency interventions, including those related to debris removal, and we derive their geographical and temporal distributions. Then, we introduce an extension of the conventional risk model to include the debris generated in damaged buildings and its accumulation in open spaces. We apply this risk extension model together with the conventional risk model to estimate the distributions of damaged buildings and of debris volumes related to the 2011 Lorca earthquake scenario. Results indicate differences between predicted and observed damage estimates within a half-damage degree interval and differences in debris volumes within the same order of magnitude. The approach presented is easily exportable to urban risk studies of other areas.

Conducting field surveys for exposure and seismic vulnerability evaluation is the most costly, resource-intensive task when assessing earthquake risk. During the past decade, risk analysts have been trying to alleviate this using remote sensing for building characterization. However, the use of vulnerability databases created with remote sensing had not been sufficiently validated thus far. In this paper, we have created an exposure and seismic vulnerability database in Port Prince (Haiti) using freely accessible aerial ortho-imagery and LiDAR points. We have validated this database against two reference datasets from different, independent studies. Then, we have computed an earthquake damage scenario to test whether remotely sensed data are actually valid for seismic risk evaluation. We have seen how our vulnerability database yields an accurate damage distribution with a low Mean Absolute Percentage Error of 3.78% when compared to the damage obtained with the reference vulnerability dataset. Further, we have conducted a thorough comparison of the cost that entails creating a vulnerability database using remote sensing with a traditional field survey. Twelve international experts have collaborated in the cost estimation of a typical in-field building inspection. As a result, we have found that using remote sensing techniques allows for saving up to 75% of the cost and 85% of the time. These outcomes seem to prove both, the technical and economic feasibility of remote sensing for seismic vulnerability assessment. Thus, we have proposed a 5-step approach for evaluating building vulnerability that combines both, the analysis of remotely sensed data and a reduced, targeted field survey to optimize time and cost. The final goal is to help cities reach the Sustainable Development Goal nr. 11.B to increase their resilience against disasters.

The present study proposes a statistical methodology to rate the habitability of different types of buildings after an earthquake. The first step was to rank variables that affect the vulnerability of a building and formulate a statistical study with a discrimination index that makes it possible to identify buildings as habitable or non-habitable. This ranking applied the criteria established in various international guidelines that are used to distinguish between habitable (undamaged/no structural damage) and non-habitable buildings (structural damage). The proposed methodology was applied to a database with information about buildings and damage grade experienced following the 2011 earthquake in Lorca. The approach presented could be extended to other regions where neccessary data are available.

This research focuses on the study of the ruins of a large building known as “El Torreón” (the Tower), belonging to the Ulaca oppidum (Solosancho, Province of Ávila, Spain). Different remote sensing and geophysical approaches have been used to fulfil this objective, providing a better understanding of the building’s functionality in this town, which belongs to the Late Iron Age (ca. 300–50 BCE). In this sense, the outer limits of the ruins have been identified using photogrammetry and convergent drone flights. An additional drone flight was conducted in the surrounding area to find additional data that could be used for more global interpretations. Magnetometry was used to analyze the underground bedrock structure and ground penetrating radar (GPR) was employed to evaluate the internal layout of the ruins. The combination of these digital methodologies (surface and underground) has provided a new perspective for the improved interpretation of “El Torreón” and its characteristics. Research of this type presents additional guidelines for better understanding of the role of this structure with regards to other buildings in the Ulaca oppidum. The results of these studies will additionally allow archaeologists to better plan future interventions while presenting new data that can be used for the interpretation of this archaeological complex on a larger scale.

Natural disasters affect hundreds of millions of people worldwide every year. The impact assessment of a disaster is key to improve the response and mitigate how a natural hazard turns into a social disaster. An actionable quantification of impact must be integratively multi-dimensional. We propose a rapid impact assessment framework that comprises detailed geographical and temporal landmarks as well as the potential socio-economic magnitude of the disaster based on heterogeneous data sources: Environment sensor data, social media, remote sensing, digital topography, and mobile phone data. As dynamics of floods greatly vary depending on their causes, the framework may support different phases of decision-making during the disaster management cycle. To evaluate its usability and scope, we explored four flooding cases with variable conditions. The results show that social media proxies provide a robust identification with daily granularity even when rainfall detectors fail. The detection also provides information of the magnitude of the flood, which is potentially useful for planning. Network analysis was applied to the social media to extract patterns of social effects after the flood. This analysis showed significant variability in the obtained proxies, which encourages the scaling of schemes to comparatively characterize patterns across many floods with different contexts and cultural factors. This framework is presented as a module of a larger data-driven system designed to be the basis for responsive and more resilient systems in urban and rural areas. The impact-driven approach presented may facilitate public–private collaboration and data sharing by providing real-time evidence with aggregated data to support the requests of private data with higher granularity, which is the current most important limitation in implementing fully data-driven systems for disaster response from both local and international actors.

We present a procedure for exposure and vulnerability evaluation that integrates LiDAR, orthophotos, and other ancillary datasets. It comprises three phases: (1) city stratification into homogeneous regions; (2) exposure database compilation; and (3) vulnerability allocation using predictive modelling. We have conducted two applications in Lorca (Spain) and Port-au-Price (Haiti) and here we compare them. Each phase of the method is subject to variations due mainly to data availability; however, it does not affect the final accuracy that remains high in both scenarios (over 80%). It is a flexible procedure that is able to adapt to the particular features of two different cities.

We present a procedure for assessing the urban exposure and seismic vulnerability that integrates LiDAR data with aerial images from the Spanish National Plan of Aerial Orthophotography (PNOA). It comprises three phases: first, we segment the satellite image to divide the study area into different urban patterns. Second, we extract building footprints and attributes that represent the type of building of each urban pattern. Finally, we assign the seismic vulnerability to each building using different machine-learning techniques: Decision trees, SVM, logistic regression and Bayesian networks. We apply the procedure to 826 buildings in the city of Lorca (SE Spain), where we count on a vulnerability database that we use as ground truth for the validation of results. The outcomes show that the machine learning techniques have similar performance, yielding vulnerability classification results with an accuracy of 77%-80% (F1-Score). The procedure is scalable and can be replicated in different areas. This is particularly relevant in Spain, where more than seven hundred towns have to develop seismic risk studies in the years to come, according to the General Direction of Civil Protection and Emergencies. It is especially interesting as a complement to conventional data gathering approaches for disaster risk applications in cities where field surveys need to be restricted to certain areas, dates or budget.

We present a procedure for assessing the urban exposure and seismic vulnerability that integrates aerial and satellite images with LiDAR data. It comprises three phases: first, we segment the satellite image to divide the study area into different urban patterns. Second, we extract building footprints and attributes that represent the type of building of each urban pattern. Finally, we assign the seismic vulnerability to each building using two machine learning techniques: logistic regression and Bayesian networks. We apply the procedure in the city of Lorca (SE Spain), where we count on a vulnerability database that we use as ground truth for the validation of results. The outcomes show that both machine learning techniques have similar performance, yielding vulnerability classification results with an accuracy of over 75% (F1 Score) in most of the cases, provided that the building footprints are correctly delineated. The procedure is scalable and can be replicated in different areas as a complement to conventional data gathering approaches. It is especially interesting for disaster risk applications in areas where cadastral databases are inaccessible or field surveys are not viable.

Natural disasters affect hundreds of millions of people worldwide every year. Early warning, humanitarian response and recovery mechanisms can be improved by using big data sources. Measuring the different dimensions of the impact of natural disasters is critical for designing policies and building up resilience. Detailed quantification of the movement and behaviours of affected populations requires the use of high granularity data that entails privacy risks. Leveraging all this data is costly and has to be done ensuring privacy and security of large amounts of data. Proxies based on social media and data aggregates would streamline this process by providing evidences and narrowing requirements. We propose a framework that integrates environmental data, social media, remote sensing, digital topography and mobile phone data to understand different types of floods and how data can provide insights useful for managing humanitarian action and recovery plans. Thus, data is dynamically requested upon data-based indicators forming a multi-granularity and multi-access data pipeline. We present a composed study of three cases to show potential variability in the natures of floodings,as well as the impact and applicability of data sources. Critical heterogeneity of the available data in the different cases has to be addressed in order to design systematic approaches based on data. The proposed framework establishes the foundation to relate the physical and socio-economical impacts of floods.

This study evaluates the potential of object-based image analysis in combination with supervised machine learning to identify urban structure type patterns from Landsat Thematic Mapper TM images. The main aim is to assess the influence of several critical choices commonly made during the training stage of a learning machine on the classification performance and to give recommendations for classifier-dependent intelligent training. Particular emphasis is given to assess the influence of size and class distribution of the training data, the approach of training data sampling user-guided or random and the type of training samples squares or segments on the classification performance of a Support Vector Machine SVM. Different feature selection algorithms are compared and segmentation and classifier parameters are dynamically tuned for the specific image scene, classification task, and training data. The performance of the classifier is measured against a set of reference data sets from manual image interpretation and furthermore compared on the basis of landscape metrics to a very high resolution reference classification derived from light detection and ranging lidar measurements. The study highlights the importance of a careful design of the training stage and dynamically tuned classifier parameters, especially when dealing with noisy data and small training data sets. For the given experimental set-up, the study concludes that given optimized feature space and classifier parameters, training an SVM with segment-shaped samples that were sampled in a guided manner and are balanced between the classes provided the best classification results. If square-shaped samples are used, a random sampling provided better results than a guided selection. Equally balanced sample distributions outperformed unbalanced training sets.

A new seismic zoning for Central America is proposed in this work, including significant changes in the boundary of zones proposed in previous zonations. The main changes are based on a detailed analysis of: seismotectonic framework, geological context, the update seismic catalogue and other geophysical and geodetic evidences (gravimetric maps, GPS observations). After that, we define the new seismogenic zones based on similar patterns of faulting, seismicity, and rupture mechanism inside each zone. The tectonic environment has required taking into account zones in three particular seismological regimes: a) crustal faulting, including, local faults, major fracture zones of plate boundary limits, and thrust fault deformed belts, b) subduction interplate and c) subduction intraplate or inslab. The seismicity each one has being associated with particular ranges in depth, which are variables taking into account the change in the subduction angle along the Mesoamerica Trench. In fact, the depth for the subduction zones, decrease in the northern Central America (Guatemala, El Salvador and Nicaragua) with respect the southern part (Costa Rica and Panama), and this is also incorporated in the new zonation.

Habitualmente, la estimacion de la vulnerabilidad sismica se centra en el comportamiento estructural de los edificios. Solo algunas metodologias, como el proyecto Risk‐UE,consideran la influencia de otros factores no estructurales o urbanisticos, tales como el piso blando, la irregularidad en alzado, la irregularidad en planta, etc. Estos factores,denominados tambien modificadores por comportamiento, pueden tener una incidencia en el dano observado, y la confluencia de varios de ellos puede variar sustancialmente la vulnerabilidad. Los modificadores por comportamiento se han identificado de forma empirica, a traves de la observacion de patrones de dano tipicos en terremotos, teniendo en cuenta las inspecciones visuales (ATC 21 1988, Benedetti y Petrini 1984, UNDP/UNIDO 1985) y otras propuestas (Coburn y Spence 1992). La puntuacion del modificador ha sido dada por el conocimiento de expertos en terremotos tras analizar evaluaciones de vulnerabilidad anteriores y bases de datos del dano producido en edificios. En esta comunicacion estudiaremos los modificadores que derivan de caracteristicas urbanisticas. Esta linea de investigacion considera que un parametro modificador deriva de caracteristicas urbanisticas si puede ser regulado en la Normativa Urbanistica de un Plan General de Ordenacion Urbana. Se realiza una descripcion de cada modificador segun cada metodologia o investigador (Risk‐UE, Giovinazzi, Lantada y Feriche) y una comparativa entre las distintas ponderaciones de los modificadores. Este analisis nos permite poder tener una primera vision de la posible cuantificacion de cada modificador y la tendencia que ha tenido la calibracion desde el ano 2003 con el proyecto Risk‐UE hasta el ano 2012 con la tesis de Feriche. Finalmente se presentan los resultados del estudio exploratorio de los parametros urbanisticos de tres zonas seleccionados de la ciudad de Lorca segun el tipo de suelo en el que se encuentren y se indican aquellos parametros que han podido influir en el dano provocado por el terremoto de mayo de 2011.

This study is in the frame of the cooperative line that several Spanish Universities and other foreign partners started with the Haitian government in 2010. According to our studies (Benito et al. in An evaluation of seismic hazard in La Hispaniola, after the 2010 Haiti earthquake, 33rd General Assembly of the European Seismological Commission, Moscow, Russia, 2012 ) and recent scientific literature, the earthquake hazard in Haiti remains high (Calais et al. in Nat Geosci 3:794–799, 2010 ). In view of this, we wonder whether the country is currently ready to face another earthquake. In this sense, we estimated several damage scenarios in Port-au-Prince and Cap-Haitien associated to realistic possible major earthquakes. Our findings show that almost 50 % of the building stock of both cities would result uninhabitable due to structural damage. Around 80 % of the buildings in both cities have reinforced concrete structure with concrete block infill; however, the presence of masonry buildings becomes significant (between 25 and 45 % of the reinforced concrete buildings) in rural areas and informal settlements on the outskirts, where the estimated damage is higher. The influence of the soil effect on the damage spatial distribution is evident in both cities. We have found that the percentage of uninhabitable buildings in soft soil areas may be double the percentage obtained in nearby districts located in hard soil. These results reveal that a new seismic catastrophe of similar or even greater consequences than the 2010 Haiti earthquake might happen if the earthquake resilience is not improved in the country. Nowadays, the design of prevention actions and mitigation policies is the best instrument the society has to face seismic risk. In this sense, the results of this research might contribute to define measures oriented to earthquake risk reduction in Haiti, which should be a real priority for national and international institutions.

Natural disasters affect hundreds of millions of people worldwide every year. Emergency response efforts depend upon the availability of timely information, such as information concerning the movements of affected populations. The analysis of aggregated and anonymized Call Detail Records (CDR) captured from the mobile phone infrastructure provides new possibilities to characterize human behavior during critical events. In this work, we investigate the viability of using CDR data combined with other sources of information to characterize the floods that occurred in Tabasco, Mexico in 2009. An impact map has been reconstructed using Landsat-7 images to identify the floods. Within this frame, the underlying communication activity signals in the CDR data have been analyzed and compared against rainfall levels extracted from data of the NASA-TRMM project. The variations in the number of active phones connected to each cell tower reveal abnormal activity patterns in the most affected locations during and after the floods that could be used as signatures of the floods - both in terms of infrastructure impact assessment and population information awareness. The representativeness of the analysis has been assessed using census data and civil protection records. While a more extensive validation is required, these early results suggest high potential in using cell tower activity information to improve early warning and emergency management mechanisms.

After the 2010 Haiti earthquake, that hits the city of Port-au-Prince, capital city of Haiti, a multidisciplinary working group of specialists (seismologist, geologists, engineers and architects) from different Spanish Universities and also from Haiti, joined effort under the SISMO-HAITI project (financed by the Universidad Politecnica de Madrid), with an objective: Evaluation of seismic hazard and risk in Haiti and its application to the seismic design, urban planning, emergency and resource management. In this paper, as a first step for a structural damage estimation of future earthquakes in the country, a calibration of damage functions has been carried out by means of a two-stage procedure. After compiling a database with observed damage in the city after the earthquake, the exposuremodel (building stock) has been classified and through an iteratively two-step calibration process, a specific set of damage functions for the country has been proposed. Additionally, Next Generation Attenuation Models (NGA) and Vs(30) models have been analysed to choose the most appropriate for the seismic risk estimation in the city. Finally in a next paper, these functions will be used to estimate a seismic risk scenario for a future earthquake.

Tras el catastrofico terremoto ocurrido en Haiti el 12 de enero de 2010, de magnitud Mw 7 y profundidad de 10 km, (fuente: USGS) con un epicentro proximo a la capital, Puerto Principe (15 km), el pais quedo en una situacion catastrofica y de extrema pobreza, con necesidades basicas en salud, nutricion, educacion y habitabilidad. Pocos meses despues se inicio el proyecto de cooperacion SISMO-HAITI, financiado y coordinado por el Grupo de Investigacion en Ingenieria Sismica (GIIS) de la Universidad Politecnica de Madrid (UPM), con participacion de otras universidades espanolas y del CSIC y siendo la contraparte Haitiana el Observatorio de Vulnerabilidad y Medio Ambiente (ONEV). Uno de los objetivos del proyecto es el calculo de peligrosidad sismica en la Isla de La Espanola que constituya la base para la elaboracion del primer codigo sismico del pais. El trabajo que aqui se presenta es una aplicacion web desarrollada con el Sistema de Informacion Geografica (SIG) del proyecto SISMO-HAITI. En esta aplicacion se integran los diferentes mapas generados para el calculo de la peligrosidad sismica, asi como los mapas resultantes, que pueden ser analizados e interpretados con mayor facilidad gracias a la aplicacion. Para analizar la influencia de los diferentes inputs de calculo se ha introducido el catalogo sismico, las diferentes zonificaciones sismo geneticas y las principales fallas tectonicas. Toda esta informacion se puede superponer geograficamente con posibilidad de realizar consultas cruzadas en las correspondientes bases de datos, permitiendo el analisis de sensibilidad de estos en los resultados. El desarrollo de esta aplicacion web se ha creado a traves de ArcGis Server 10

Tras el terremoto ocurrido en Haiti el 12 de enero de 2010, con un epicentro proximo a la capital, Puerto Principe (25 km), de magnitud Mw 7,0 y profundidad de 13 km, el pais ha quedado en una situacion catastrofica y de extrema pobreza, con necesidades basicas de salud, nutricion, educacion y habitabilidad. Los efectos del terremoto han sido devastadores en la poblacion, con mas de 300.000 personas que han perdido la vida, otras tantas que han resultado heridas y 1,3 millones de personas que han quedado sin hogar y viviendo en campamentos. En cuanto a los efectos materiales, el sismo ha dejado cerca de 100.000 residencias totalmente destruidas y casi 200.000 danadas fuertemente (fuente: USGS). Este terremoto ha sido el mas fuerte registrado en la zona desde el acontecido en 1770. Ademas, el sismo fue perceptible en paises cercanos como Cuba, Jamaica y Republica Dominicana, donde provoco temor y evacuaciones preventivas. La reconstruccion del pais es un tema prioritario en el marco de la cooperacion internacional y el presente proyecto, SISMO-HAITI, se ha desarrollado con el fin de aportar conocimiento e informacion para facilitar la toma de medidas preventivas ante el riesgo sismico existente, tratando de evitar que un terremoto futuro en el pais produzca una catastrofe como el recientemente vivido. En el caso de Haiti, no existia ninguna institucion responsable del monitoreo sismico, pero se ha establecido contacto directo con el Observatorio Nacional de Medio Ambiente y Vulnerabilidad de Haiti (ONEV) a traves de su director Dwinel Belizaire Ing. M. Sc. Director, que es precisamente quien ha solicitado la ayuda que ha motivado la presente propuesta. El fin ultimo de este proyecto es el estudio de acciones de mitigacion del elevado riesgo existente, contribuyendo al desarrollo sostenible de la region. Para ello, se ha evaluado la amenaza sismica en Haiti, en base a la cual se pretenden establecer criterios de diseno sismorresistente para la reconstruccion del pais, que se podran recoger en la primera normativa antisismica, asi como el riesgo sismico en Puerto Principe, cuyos resultados serviran de base para elaborar los planes de emergencia ante este riesgo natural. Los objetivos especificos alcanzados son: • Evaluacion de amenaza sismica en Haiti, resultando mapas de distintos parametros de movimiento para diferentes probabilidades de excedencia (lo que supone conocer la probabilidad asociada a movimientos por futuros terremotos). • Evaluacion del efecto local en Puerto Principe y elaboracion de un mapa de microzonacion de la ciudad. • Estudio de vulnerabilidad sismica a escala local en Puerto Principe • Estimacion del riesgo sismico en Puerto Principe • Medidas de mitigacion del riesgo y de diseno sismorresistente En este informe se resumen las actividades desarrolladas y los resultados obtenidos a lo largo del ano 2011 durante la ejecucion del presente proyecto. El grupo de trabajo es un equipo multidisciplinar, compuesto por investigadores de diferentes universidades (Universidad Politecnica de Madrid- UPM-, U. Complutense de Madrid -UCM-, U. Alicante -UA-, U. Almeria -UAL-, U. Autonoma de Santo Domingo -UASD- y U. de Mayaguez de Puerto Rico -UPRM-) que cubren todas las ramas involucradas en la ejecucion del proyecto: geologia, sismologia, ingenieria sismica, arquitectura y gestion de geoinformacion. Todos los miembros de este equipo han trabajado conjuntamente durante todo el ano, manteniendo reuniones, jornadas de trabajo y videoconferencias, ademas de realizar una visita a Puerto Principe en julio de 2011 para llevar a cabo la primera toma de datos.