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RRVS Building survey for building exposure modelling in Chía (Colombia)

The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS (Remote Rapid Visual Screening) methodology. It is composed by 6249 randomly distributed buildings in the urban area of Chía (Colombia). The survey has been carried out between May and July 2020 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (and footprints from OpenStreetMap (OSM), both with vintages of May 2020. The buildings were inspected by dozens of local students of civil engineering students from the Universidad de La Sabana (Chía, Colombia). Their attribute values in terms of the GEM v.2.0 taxonomy.

Database (DSM) of in situ POC and SPM and satellite Rrs (matchup) collected between 2002 and 2018

This data release includes in situ measurements collected between 2002 and 2012 during different study sites (Full description of the methods adopted for each measurements in the references). DSM measurements include 101 coincident in situ concentrations of Particulate Organic Carbon (POC) (μgL-1) and Suspended Particulate Matter (SPM) (mg/l) and remote-sensing reflectances (Rrs, sr-1) matchups for the sensor Meris. It addressed the objective: 1) Validate the POC/SPM ratio using satellite Rrs (matchup)

Database (DSM) of in situPOC, SPM and Rrs collected between 1997 and 2018

This data release includes in situ measurements collected between 2010 and 2014 during different cruise missions and study sites (Full description of the methods adopted for each measurements in the references). Measurements include concentrations of Particulate Organic Carbon (POC) (μgL-1) and Suspended Particulate Matter (SPM) (mg/l), remote-sensing reflectances (Rrs, sr-1), the particulate backscattering coefficient (bbp), and the particulate attenuation coefficient (cp) (m-1) at 650 nm. The first in situ database, named DS0, includes 300 coincident in situ POC, SPM, bbp, and cp measurements. DS1 includes 325 coincident in situ POC, SPM and Rrs. It addressed two main objectives: 1) Check the relationship between POC/SPM and bbp/cp 2) Develop a POC/SPM algorithm based on Rrs

RRVS Building survey for building exposure modelling in Valparaiso and Viña del Mar (Chile)

The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS (Remote Rapid Visual Screening) methodology. It is composed by 604 randomly distributed buildings in the urban area of Valparaiso and Viña del Mar (Chile). The survey has been carried out between November and December 2018 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (vintage: December 2018) and footprints from OpenStreetMap (OSM). The buildings were inspected by local structural engineers from the Chilean Research Centre for Integrated Disaster Risk Management (CIGIDEN) while collecting their attribute values in terms of the GEM v.2.0 taxonomy

A data set of collocated satellite remote sensing reflectance from GlobColour merged products, chlorophyll a concentration of phytoplankton functional types derived from in situ pigment data, and CMEMS sea surface temperature from 2002 to 2012

This data set provides the collocated data of remote sensing reflectance (Rrs) at 9 bands extracted from the merged ocean color products from GlobColour archive (https://www.globcolour.info/), satellite sea surface temperature from CMEMS (https://marine.copernicus.eu/), and chlorophyll a concentrations (Chl-a) derived from a global database of in situ HPLC pigment data collected from 2002 to 2012. The total Chl-a, Chl-a of six phytoplankton functional types (PFTs) that are diatoms, dinoflagellates, haptophytes, green algae, prokaryotes and Prochlorococcus, and two fractions of prokaryotes and Prochlorococcus are included in this data set. PFT Chl-a and fractions are derived using an updated diagnostic pigment analysis (DPA) method (Soppa et al., 2014; Losa et al., 2017), that was originally developed by Vidussi et al. (2001), adapted in Uitz et al. (2006) and further refined by Hirata et al. (2011) and Brewin et al. (2015). Matchups of satellite Rrs to in situ PFT data (which were also matchups to SST) were extracted from global 4-km daily merged products. Extraction and averaging protocol including quality control were described in detail in Xi et al. (2020).

Buildings data from Remote Rapid Visual survey (RRVS) for exposure modelling in Kyrgyzstan and Tajikistan

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.

EMCA Seismic exposure model for Turkmenistan

Multi-resolution exposure model for seismic risk assessment in Turkmenistan. 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 Turkmenistan (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

EMCA Seismic exposure model for the Kyrgyz Republic

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

EMCA Seismic exposure model for Kazakhstan

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

Remote Rapid Visual survey (RRVS) for exposure modelling in Kyrgyzstan and Tajikistan

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.

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