Examinando por Materia "natural disaster"

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    Automatic detection of building typology using deep learning methods on street level images
    (PERGAMON-ELSEVIER SCIENCE LTD, 2020-03-20) Duque, J.; Gonzalez, D.; Rueda Plata, Diego; Acevedo, A.; Ramos, R.; Betancourt, A.; García, S.; Mecánica Aplicada
    An exposure model is a key component for assessing potential human and economic losses from natural disasters. An exposure model consists of a spatially disaggregated description of the infrastructure and population of a region under study. Depending on the size of the settlement area, developing such models can be a costly and time-consuming task. In this paper we use a manually annotated dataset consisting of approximately 10,000 photos acquired at street level in the urban area of Medellín to explore the potential for using a convolutional neural network (CNN) to automatically detect building materials and types of lateral-load resisting systems, which are attributes that define a building's structural typology (which is a key issue in exposure models for seismic risk assessment). The results of the developed model achieved a precision of 93% and a recall of 95% when identifying nonductile buildings, which are the buildings most likely to be damaged in an earthquake. Identifying fine-grained material typology is more difficult, because many visual clues are physically hidden, but our model matches expert level performances, achieving a recall of 85% and accuracy scores ranging from 60% to 82% on the three most common building typologies, which account for 91% of the total building population in Medellín. Overall, this study shows that a CNN can make a substantial contribution to developing cost-effective exposure models. © 2020 Elsevier Ltd
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    Automatic detection of building typology using deep learning methods on street level images
    (PERGAMON-ELSEVIER SCIENCE LTD, 2020-03-20) Duque, J.; Gonzalez, D.; Rueda Plata, Diego; Acevedo, A.; Ramos, R.; Betancourt, A.; García, S.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)
    An exposure model is a key component for assessing potential human and economic losses from natural disasters. An exposure model consists of a spatially disaggregated description of the infrastructure and population of a region under study. Depending on the size of the settlement area, developing such models can be a costly and time-consuming task. In this paper we use a manually annotated dataset consisting of approximately 10,000 photos acquired at street level in the urban area of Medellín to explore the potential for using a convolutional neural network (CNN) to automatically detect building materials and types of lateral-load resisting systems, which are attributes that define a building's structural typology (which is a key issue in exposure models for seismic risk assessment). The results of the developed model achieved a precision of 93% and a recall of 95% when identifying nonductile buildings, which are the buildings most likely to be damaged in an earthquake. Identifying fine-grained material typology is more difficult, because many visual clues are physically hidden, but our model matches expert level performances, achieving a recall of 85% and accuracy scores ranging from 60% to 82% on the three most common building typologies, which account for 91% of the total building population in Medellín. Overall, this study shows that a CNN can make a substantial contribution to developing cost-effective exposure models. © 2020 Elsevier Ltd
  • No hay miniatura disponible
    Ítem
    Automatic detection of building typology using deep learning methods on street level images
    (PERGAMON-ELSEVIER SCIENCE LTD, 2020-03-20) Duque, J.; Gonzalez, D.; Rueda Plata, Diego; Acevedo, A.; Ramos, R.; Betancourt, A.; García, S.; Duque, J.; Gonzalez, D.; Rueda Plata, Diego; Acevedo, A.; Ramos, R.; Betancourt, A.; García, S.; Universidad EAFIT. Departamento de Ingeniería de Producción; Materiales de Ingeniería
    An exposure model is a key component for assessing potential human and economic losses from natural disasters. An exposure model consists of a spatially disaggregated description of the infrastructure and population of a region under study. Depending on the size of the settlement area, developing such models can be a costly and time-consuming task. In this paper we use a manually annotated dataset consisting of approximately 10,000 photos acquired at street level in the urban area of Medellín to explore the potential for using a convolutional neural network (CNN) to automatically detect building materials and types of lateral-load resisting systems, which are attributes that define a building's structural typology (which is a key issue in exposure models for seismic risk assessment). The results of the developed model achieved a precision of 93% and a recall of 95% when identifying nonductile buildings, which are the buildings most likely to be damaged in an earthquake. Identifying fine-grained material typology is more difficult, because many visual clues are physically hidden, but our model matches expert level performances, achieving a recall of 85% and accuracy scores ranging from 60% to 82% on the three most common building typologies, which account for 91% of the total building population in Medellín. Overall, this study shows that a CNN can make a substantial contribution to developing cost-effective exposure models. © 2020 Elsevier Ltd
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    Community participation in natural risk prevention: Case histories from Colombia
    (GEOLOGICAL SOC PUBLISHING HOUSE, 2008-01-01) Hermelin, M.; Bedoya, G.; Universidad EAFIT. Departamento de Geología; Ciencias del Mar
    More than 75% of Colombia's 42 million people live in urban areas located in the mountains and are exposed to numerous natural hazards: floods, flash floods, landslides, earthquakes and volcanism. The Armero disaster of 1985 triggered the creation of the National System for Disaster Prevention and Relief. National, regional and local committees started to operate across the country, accompanied by education commissions that produced diverse audiovisual materials to help educate people living in these areas. The experiences of working with local committees gained during the last two decades are presented here. Case histories are from cities such as Pereira, Manizales and Medellín, where the local committees are run by people with little or no formal education but who understand that they must participate as a group to prevent or mitigate the effects of natural disasters. The co-operation between technical experts and trained residents represents an outstanding example of good communication and co-operation for urban populations living in dangerous areas. Although many problems have yet to be resolved, these case histories show that this type of organization seems to be more effective than direct intervention from national government agencies. The models of community participation and communication developed and refined here may have application to similar social environments in other countries. © 2008 Geological Society of London.
  • No hay miniatura disponible
    Ítem
    Community participation in natural risk prevention: Case histories from Colombia
    (GEOLOGICAL SOC PUBLISHING HOUSE, 2008-01-01) Hermelin, M.; Bedoya, G.; Hermelin, M.; Bedoya, G.; Universidad EAFIT. Departamento de Ciencias; Geología Ambiental y Tectónica
    More than 75% of Colombia's 42 million people live in urban areas located in the mountains and are exposed to numerous natural hazards: floods, flash floods, landslides, earthquakes and volcanism. The Armero disaster of 1985 triggered the creation of the National System for Disaster Prevention and Relief. National, regional and local committees started to operate across the country, accompanied by education commissions that produced diverse audiovisual materials to help educate people living in these areas. The experiences of working with local committees gained during the last two decades are presented here. Case histories are from cities such as Pereira, Manizales and Medellín, where the local committees are run by people with little or no formal education but who understand that they must participate as a group to prevent or mitigate the effects of natural disasters. The co-operation between technical experts and trained residents represents an outstanding example of good communication and co-operation for urban populations living in dangerous areas. Although many problems have yet to be resolved, these case histories show that this type of organization seems to be more effective than direct intervention from national government agencies. The models of community participation and communication developed and refined here may have application to similar social environments in other countries. © 2008 Geological Society of London.