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  • 1
    Digitale Medien
    Digitale Medien
    Learning to Recognize Visual Dynamic Events from Examples (2000)
    Springer
    International journal of computer vision 38 (2000), S. 35-44 
    Details
    ISSN: 1573-1405
    Schlagwort(e): dynamic events ; pattern recognition ; Support Vector Machines ; computer vision systems
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik
    Notizen: Abstract This paper describes a trainable and flexible system able to recognize visual dynamic events, e.g. movements performed by different people, from a stream of images taken by a fixed camera. Each event is represented by a feature vector built from the spatio-temporal changes detected in the observed image sequence. The system neither attempts to recover the 3D structure nor assumes a prior model of the observed dynamic events. During training a supervisor identifies and labels the events of interest among those automatically detected by the system. At run time, previously unseen events are detected and classified on the basis of the available examples. Several experiments on real images are reported and the benefits of using Support Vector Machines for performing effective classification from a relatively small number of labeled examples and for building noise tolerant representations are discussed. Preliminary results indicate that the proposed system can also be applied with equally good results to the case in which the dynamic events are gestures performed by different people.
    Materialart: Digitale Medien
    URL: http://dx.doi.org/10.1023/A:1008114700759
    Standort Signatur Erwartet Verfügbarkeit
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    Artikel (Nationallizenzen)
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  • 2
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    EMCA Seismic exposure model for Tajikistan (2019)
    GFZ Data Services
    Details
    Publikationsdatum: 2021-11-17
    Beschreibung: Abstract
    Beschreibung: Multi-resolution exposure model for seismic risk assessment in Tajikistan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2020) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra (submitted). The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Tajikistan (provided as a separate file). The model integrates around 1'000 building observations (see related dataset Pittore et al. 2019a). The following specific modelling parameters have been employed: Prior strength=10, 100 Epsilon=0.001 For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
    Schlagwort(e): Earthquake Risk ; taxonomy ; RRVS ; GEM ; EMCA ; Central Asia ; geological process 〉 seismic activity ; risk 〉 natural risk ; safety 〉 risk assessment 〉 disaster preparedness ; safety 〉 risk assessment 〉 natural risk analysis ; safety 〉 risk assessment 〉 risk exposure
    Materialart: Dataset , Dataset
    Standort Signatur Erwartet Verfügbarkeit
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    DATEN GFZ
  • 3
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    EMCA Seismic exposure model for the Kyrgyz Republic (2019)
    GFZ Data Services
    Details
    Publikationsdatum: 2021-11-17
    Beschreibung: Abstract
    Beschreibung: Multi-resolution exposure model for seismic risk assessment in the Kyrgyz Republic. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of 1'175 geo-cells covering the territory of the Kyrgyz Republic. The model integrates around 6'000 building observations (see related dataset Pittore et al. 2019). The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
    Schlagwort(e): Earthquake Risk ; taxonomy ; RRVS ; GEM ; EMCA ; Central Asia ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; geological process 〉 seismic activity ; risk 〉 natural risk ; safety 〉 risk assessment 〉 disaster preparedness ; safety 〉 risk assessment 〉 natural risk analysis ; safety 〉 risk assessment 〉 risk exposure
    Materialart: Dataset , Dataset
    Standort Signatur Erwartet Verfügbarkeit
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    DATEN GFZ
  • 4
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    EMCA Seismic exposure model for Uzbekistan (2019)
    GFZ Data Services
    Details
    Publikationsdatum: 2021-11-17
    Beschreibung: Abstract
    Beschreibung: Multi-resolution exposure model for seismic risk assessment in Uzbekistan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Uzbekistan (provided as a separate file). The model prior is based on empirical observations in Kyrgyzstan and Tajikistan as well as user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
    Schlagwort(e): Earthquake Risk ; taxonomy ; RRVS ; GEM ; EMCA ; Central Asia ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; geological process 〉 seismic activity ; risk 〉 natural risk ; safety 〉 risk assessment 〉 disaster preparedness ; safety 〉 risk assessment 〉 natural risk analysis ; safety 〉 risk assessment 〉 risk exposure
    Materialart: Dataset , Dataset
    Standort Signatur Erwartet Verfügbarkeit
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    DATEN GFZ
  • 5
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    EMCA Seismic exposure model for Kazakhstan (2019)
    GFZ Data Services
    Details
    Publikationsdatum: 2021-11-17
    Beschreibung: Abstract
    Beschreibung: Multi-resolution exposure model for seismic risk assessment in Kazakhstan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Kazakhstan (provided as a separate file). The model prior is based on user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models
    Schlagwort(e): Earthquake Risk ; taxonomy ; RRVS ; GEM ; EMCA ; Central Asia ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; geological process 〉 seismic activity ; risk 〉 natural risk ; safety 〉 risk assessment 〉 disaster preparedness ; safety 〉 risk assessment 〉 natural risk analysis ; safety 〉 risk assessment 〉 risk exposure
    Materialart: Dataset , Dataset
    Standort Signatur Erwartet Verfügbarkeit
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    DATEN GFZ
  • 6
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    Earthquake Model Central Asia: seismic exposure modelling (2019)
    GFZ Data Services
    Details
    Publikationsdatum: 2021-11-17
    Beschreibung: Abstract
    Beschreibung: The datasets in this collection include input and output components of the seismic exposure model developed within the framework of the Earthquake Model Central Asia and used for seismic risk assessment. In particular the collection includes: - A dataset of around 7’000 individual building observations in Kyrgyzstan and Tajikistan collected using the Remote Rapid Visual Survey (RRVS) methodology developed at GFZ, along with the class schema used to map the individual taxonomic observations into vulnerability-related building classes. These are used to develop suitable prior distribution and to constrain locally the resulting exposure models - The seismic exposure models for the following central Asian countries: Kazakhstan , Kyrgyz Republic, Tajikistan, Turkmenistan and Uzbekistan, aggregated over a set of heterogeneous tessellations (geo-cells) The methodology employed for the development of the exposure models is described in Pittore, M., Haas, M., and Silva, V. (2020) “Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications”, Earthquake Spectra. Two versions of the models obtained with two different parameter settings are included. The models are provided in .csv and in .xml (nrml 0.5) format, for compatiliby with the OpenQuake hazard and risk assessment engine.
    Schlagwort(e): Earthquake Risk ; taxonomy ; RRVS ; GEM ; EMCA ; Central Asia ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; geological process 〉 seismic activity ; risk 〉 natural risk ; safety 〉 risk assessment 〉 disaster preparedness ; safety 〉 risk assessment 〉 natural risk analysis ; safety 〉 risk assessment 〉 risk exposure
    Materialart: Dataset , Dataset
    Standort Signatur Erwartet Verfügbarkeit
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    DATEN GFZ
  • 7
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    Unbekannt
    Remote Rapid Visual survey (RRVS) for exposure modelling in Kyrgyzstan and Tajikistan (2019)
    GFZ Data Services
    Details
    Publikationsdatum: 2021-11-17
    Beschreibung: Abstract
    Beschreibung: The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS methodology in Kyrgyzstan and Tajikistan, within the framework of the projects EMCA (Earthquake Model Central Asia), funded by GEM, and "Assessing Seismic Risk in the Kyrgyz Republic", funded by the World Bank. The survey has been carried out between 2012 and 2016 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images and footprints from OpenStreetMap. The attributes are encoded according to the GEM taxonomy v2.0 (see https://taxonomy.openquake.org). The following attributes are defined (not all are observable in the RRVS survey): code, description: lon, longitude in fraction of degrees lat, latitude in fraction of degrees object_id, unique id of the building surveyed MAT_TYPE,Material Type MAT_TECH,Material Technology MAT_PROP,Material Property LLRS,Type of Lateral Load-Resisting System LLRS_DUCT,System Ductility HEIGHT,Height YR_BUILT,Date of Construction or Retrofit OCCUPY,Building Occupancy Class - General OCCUPY_DT,Building Occupancy Class - Detail POSITION,Building Position within a Block PLAN_SHAPE,Shape of the Building Plan STR_IRREG,Regular or Irregular STR_IRREG_DT,Plan Irregularity or Vertical Irregularity STR_IRREG_TYPE,Type of Irregularity NONSTRCEXW,Exterior walls ROOF_SHAPE,Roof Shape ROOFCOVMAT,Roof Covering ROOFSYSMAT,Roof System Material ROOFSYSTYP,Roof System Type ROOF_CONN,Roof Connections FLOOR_MAT,Floor Material FLOOR_TYPE,Floor System Type FLOOR_CONN,Floor Connections For each building an EMCA vulnerability class has been assigned following the fuzzy scoring methodology described in Pittore et al., 2018. The related class definition schema (as a .json document) is included in the data package.
    Schlagwort(e): Earthquake Risk ; taxonomy ; RRVS ; GEM ; EMCA ; Central Asia ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; geological process 〉 seismic activity ; risk 〉 natural risk ; safety 〉 risk assessment 〉 disaster preparedness ; safety 〉 risk assessment 〉 natural risk analysis ; safety 〉 risk assessment 〉 risk exposure
    Materialart: Dataset , Dataset
    Standort Signatur Erwartet Verfügbarkeit
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    DATEN GFZ
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