LA Fires - Eaton fire January 2025: Building Damage Assessment

  • This dataset updates: Never

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  • eaton_planet_1_11_25_damage_predictions.gpkgGeopackage (51.0M)
    Modified: 14 January 2025

    Results from 01/11 using Planet imagery.

    156,102 buildings in study area

    8,682 estimated damaged

    The result file contains the following fields for each building footprint:

    damage_pct_0m​ – the fraction of the building footprint's area that is classified as damaged by our model

    damage_pct_10m​ – total damaged area within a 10m buffer of the building footprint (including the footprint itself) / building footprint's area (this can be >1.0 but we clip to 1.0)

    damage_pct_20m​ – same as above but with a 20m buffer

    damaged​ – 1 if damage_pct_0m > 0 else 0

    unknown_pct – fraction of the pixels within the building footprint that we think are obstructed (clouds, smoke, haze, too dark to evaluate)

  • eaton_jan_10_rgb_damage_predictions.gpkgGeopackage (9.9M)
    Modified: 11 January 2025

    Results from 01/10 using Maxar imagery.

    31,356 buildings in this AOI:

    27,202 in the 0-20 bucket

    4,154 in the >20 bucket (4602 with damage>0)

    The result file contains the following fields for each building footprint:

    damage_pct_0m​ – the fraction of the building footprint's area that is classified as damaged by our model

    damage_pct_10m​ – total damaged area within a 10m buffer of the building footprint (including the footprint itself) / building footprint's area (this can be >1.0 but we clip to 1.0)

    damage_pct_20m​ – same as above but with a 20m buffer

    damaged​ – 1 if damage_pct_0m > 0 else 0

    unknown_pct – fraction of the pixels within the building footprint that we think are obstructed (clouds, smoke, haze, too dark to evaluate)

Export metadata for this dataset: JSON | CSV

Source Microsoft - AI4G Lab
Contributor
Time Period of the Dataset [?] January 10, 2025-January 11, 2025 ... More
Modified [?] 14 January 2025
Dataset Added on HDX [?] 11 January 2025 Less
Expected Update Frequency Never
Location
Visibility
Public
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Methodology

We ran our damage assessment AI models on images provided by Maxar and Planet and have mapped out the affected buildings.

Caveats / Comments

While the data provides a valuable first look, it should serve as a preliminary guide and will require on-the-ground verification for a complete understanding.

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