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Title: Automated image localization to support rapid building reconnaissance in a large‐scale area
Collecting massive amounts of image data is a common way to record the post-event condition of buildings, to be used by engineers and researchers to learn from that event. Key information needed to interpret the image data collected during these reconnaissance missions is the location within the building where each image was taken. However, image localization is difficult in an indoor environment, as GPS is not generally available because of weak or broken signals. To support rapid, seamless data collection during a reconnaissance mission, we develop and validate a fully automated technique to provide robust indoor localization while requiring no prior information about the condition or spatial layout of an indoor environment. The technique is meant for large-scale data collection across multiple floors within multiple buildings. A systematic method is designed to separate the reconnaissance data into individual buildings and individual floors. Then, for data within each floor, an optimization problem is formulated to automatically overlay the path onto the structural drawings providing robust results, and subsequently, yielding the image locations. The end-to end technique only requires the data collector to wear an additional inexpensive motion camera, thus, it does not add time or effort to the current rapid reconnaissance protocol. As no prior information about the condition or spatial layout of the indoor environment is needed, this technique can be adapted to a large variety of building environments and does not require any type of preparation in the postevent settings. This technique is validated using data collected from several real buildings.  more » « less
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Computer-Aided Civil and Infrastructure Engineering
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Medium: X
Sponsoring Org:
National Science Foundation
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    File descriptions:

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    Source data:

    HISDAC-ES is derived from cadastral building footprint data, available from different authorities in Spain:

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    • Other regions:
    • Data source of municipality polygons: Centro Nacional de Información Geográfica (

    Technical notes:

    Gridded data

    File nomenclature:



    • all: complete territory of Spain
    • can: Canarian Islands only
    • ibe: Iberic peninsula + Céuta + Melilla


    • laea: Lambert azimuthal equal area (EPSG:3035)
    • regcan: REGCAN95 / UTM zone 28N (EPSG:4083)
    • utm: ETRS89 / UTM zone 30N (EPSG:25830)


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    • physical / phys: physical building characteristics in 2020
    • temporal / temp: temporal characteristics (construction year statistics)

    Variables: evolution

    • budens: building density (count per grid cell area)
    • bufa: building footprint area
    • deva: developed area (any grid cell containing at least one building)
    • resbufa: residential building footprint area
    • resbia: residential building indoor area

    Variables: physical

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    • bufa: building footprint area
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    Variables: temporal

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    • maxcoy: minimum construction year per grid cell
    • meancoy: mean construction year per grid cell
    • medcoy: median construction year per grid cell
    • modecoy: mode (most frequent) construction year per grid cell
    • varcoy: variety of construction years per grid cell

    Variable: landuse

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    • hisdac_es_municipality_stats_multitemporal_longform_v1.csv: This CSV file contains the zonal sums of the gridded surfaces (e.g., number of buildings per year and municipality) in long form. Note that a value of 0 for the year attribute denotes the statistics for records without construction year information.
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    • NATCODE: 34 01 04 04001
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