Examinando por Materia "urban area"

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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
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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.; 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.
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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.; 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.
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    Prediction of landslide occurrence in urban areas located on volcanic ash soils in Pereira, Colombia
    (Springer Verlag, 2004-01-01) Rios, D.A.; Hermelin, M.; Rios, D.A.; Hermelin, M.; Universidad EAFIT. Departamento de Ciencias; Geología Ambiental y Tectónica
    As a result of the 25 January 1999 Armenia earthquake, the city of Pereira (400,000 inhabitants), located on a volcanic ash-covered alluvial fan in the western limit of the Central Cordillera (Colombia), suffered 250 slope movements. After a complete inventory, a monitoring process of unstable areas was designed, based on repeated topographic surveys, soil pore saturation levels and visual inspections. The participation of the communities was crucial and permitted the prediction of slope movements between 2 weeks and 3 months in advance and the evacuation of the inhabitants. Three specific examples are discussed. The method could be improved by excavating observation trenches and observing in detail local rainfall. In all cases, the strong involvement of the community was considered indispensable for the success of the process. © Springer-Verlag 2004.
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    Using remote sensing to assess the relationship between crime and the urban layout
    (ELSEVIER SCI LTD, 2014-12-01) Patino, Jorge E.; Duque, Juan C.; Pardo-Pascual, Josep E.; Ruiz, Luis A.; Universidad EAFIT. Departamento de Economía y Finanzas; Research in Spatial Economics (RISE)
    The link between place and crime is at the base of social ecology theories of crime that focus in the relationship of the characteristics of geographical areas and crime rates. The broken windows theory states that visible cues of physical and social disorder in a neighborhood can lead to an increase in more serious crime. The crime prevention through environmental design (CPTED) planning approach seeks to deter criminal behavior by creating defensible spaces. Based on the premise that a settlement's appearance is a reflection of the society, we ask whether a neighborhood's design has a quantifiable imprint when seen from space using urban fabric descriptors computed from very high spatial-resolution imagery. We tested which land cover, structure and texture descriptors were significantly related to intra-urban homicide rates in Medellin, Colombia, while controlling for socioeconomic confounders. The percentage of impervious surfaces other than clay roofs, the fraction of clay roofs to impervious surfaces, two structure descriptors related to the homogeneity of the urban layout, and the uniformity texture descriptor were all statistically significant. Areas with higher homicide rates tended to have higher local variation and less general homogeneity; that is, the urban layouts were more crowded and cluttered, with small dwellings with different roofing materials located in close proximity to one another, and these regions often lacked other homogeneous surfaces such as open green spaces, wide roads, or large facilities. These results seem to be in agreement with the broken windows theory and CPTED in the sense that more heterogeneous and disordered urban layouts are associated with higher homicide rates. © 2014 Elsevier Ltd.