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Hawaiʻi Wildfire Home Risk Explorer Explore the components and story of the wildfire tool. Try the tool
Evacuation access + building intelligence

Hawaiʻi WildfireHome Risk Explorer

Explore the components and story of the wildfire tool.

One project · three chapters

From hand-drawn evacuation zones to a statewide home screening tool.

  1. Automate the evacuation work

    An internship labeling one-way-in, one-way-out roads became a repeatable statewide assessment of evacuation congestion, shared exits, and how difficult it may be for a home to reach a major road.

  2. Add the wildfire context

    Wind, temperature, humidity, vegetation, terrain, ignition conditions, and historical fires were brought together to show what is happening around each home—not just on its route out.

  3. Turn the evidence into a usable tool

    The evacuation and wildfire evidence became one interface. Getting there required reconciling statewide building sources, using AI to audit remaining gaps, and developing a directional fire-exposure method from the available weather and landscape data.

Start at the beginning

A manual mapping project became a statewide question about how people get out.

During my HWMO internship, I drew polygons around Kauaʻi neighborhoods with limited road access. Those labels showed where one road failure could leave many homes depending on the same exit.

I expanded that work into a repeatable analysis of road access, distance, nearby homes, escape options, and shared downstream bottlenecks. The map shows the original human-reviewed layer that started the project.

Explore the original Kauaʻi access labels
Street view: OpenStreetMap data under ODbL. Satellite view: Esri World Imagery, displayed under Esri’s service terms; it is not open-source imagery.

In addition to downsteam road bottlenecks, what other factors impact evacuation burden?

From zones to individual homes · 01

Homes sharing one exit

Haleilio Road contains 800 mapped buildings. The highest-burden home has 795 buildings ahead of it on the route to the zone’s one usable exit.

Factor 01 · Shared route burden 800 buildings depend on one mapped exit
Real Kauaʻi footprints + project road-network metrics · OSM basemap
From zones to individual homes · 02

Distance to a major road

In this remote Kōkeʻe-area example, the analyzed road route is 24.944 km from the zone to its primary highway connection.

Factor 02 · Distance to a major road A remote road connection changes the burden
Real Kauaʻi footprints + project road-network metrics · OSM basemap
Chapter two · Add the wildfire context

I brought wind, temperature, humidity, vegetation, terrain, ignition conditions, and past fires into the same view.

Wildfire conditions · 01

Today’s ignition conditions

The University of Hawaiʻi’s Hawaiʻi Climate Data Portal (HCDP) publishes an experimental statewide map estimating the daily probability that a large (8+ acre) wildfire will ignite. The latest map and its three daily projections are shown here directly from the official source.

How to explore Hover over the map to see the modeled ignition probability at any location.
University of Hawaiʻi HCDP ignition probability · Esri satellite basemap

HCDP’s experimental ignition-probability product combines climate, vegetation, and land-cover inputs to estimate where a large fire of at least eight acres may start. It is a planning indicator—not an active-fire detection and not a forecast of the direction an existing fire will spread. Values, dates, and projections above come from the University of Hawaiʻi HCDP service.

Wildfire conditions · 02

Fire weather

The temperature map and arrows use NWS (National Weather Service) hourly forecasts sampled across every island. Arrow direction and length show wind flow and speed; color and weight encode relative humidity.

How to explore Hover over the map to see live locational data for temperature, humidity, and wind speed.
Temperature map, wind and relative humidity from NWS hourly forecasts · OSM basemap

The National Weather Service, an agency within the National Oceanic and Atmospheric Administration, produces the nation’s official weather observations and forecasts. Hourly temperature, relative humidity, wind direction, and wind speed are useful here because they describe the near-term atmospheric conditions that can dry fuels and support fire movement; they remain forecasts and can change as new observations arrive.

Wildfire conditions · 03

Wildland Fire Interagency Geospatial Services + NASA Fire Information for Resource Management System

Reported Wildland Fire Interagency Geospatial Services (WFIGS) incidents and NASA Fire Information for Resource Management System (FIRMS) heat detections are separate evidence layers and must never be treated as the same thing.

How to explore Hover over a fire to see discovery date and containment | hover over a dot to see NASA thermal heat detection details
WFIGS / IRWIN incidents and perimeters · FIRMS is a separate credentialed layer

WFIGS publishes authoritative interagency incident locations and mapped perimeters assembled through federal wildfire reporting systems such as IRWIN. NASA FIRMS distributes near-real-time MODIS and VIIRS satellite thermal detections. WFIGS describes reported incidents; a FIRMS dot marks satellite-observed heat and is not, by itself, confirmation of a wildfire.

Wildfire conditions · 04

Historical fire exposure

Explore Pacific Fire Exchange’s mapped large-fire perimeters year by year from 1999 through 2022.

How to explore Hover over a fire perimeter to see its recorded name, year, and acreage | use the slider to isolate a year
Pacific Fire Exchange / UH NREM large-fire perimeters · 1999–2022 · OSM basemap

Pacific Fire Exchange’s Hawaiʻi Large Fire Perimeter dataset combines records from county fire departments, the Hawaiʻi Department of Land and Natural Resources, the National Park Service, and mapping led by the University of Hawaiʻi at Mānoa’s Department of Natural Resources and Environmental Management. It tracks mapped perimeters from 1999–2022, generally emphasizing fires of at least 50 acres, so it is a strong statewide history—not a record of every small ignition.

Wildfire conditions · 05

Surrounding fuels and terrain

Statewide LANDFIRE vegetation cover and 3DEP shaded relief show how the surrounding landscape changes across every island.

How to explore Hover over the map to inspect slope, elevation, and vegetation cover at any location
USGS 3DEP terrain + LANDFIRE Hawaii LF2024 EVC (30 m) · OSM basemap

The U.S. Geological Survey’s 3D Elevation Program (3DEP) supplies the national elevation surface used to derive terrain and slope. The interagency LANDFIRE program’s Existing Vegetation Cover product estimates live canopy cover by life form for each 30-meter cell. Together they provide consistent statewide landscape context; they describe mapped terrain and vegetation, not real-time fuel moisture or a fire-spread forecast.

  1. 3–1 Reconcile building data
  2. 3–2 Run the AI audit
  3. 3–3 Present a visual evidence tool
Step 01

One state. Several versions of every building.

Kauaʻi began with a trusted internship building layer. Going statewide meant reconciling county footprints with FEMA, Microsoft, and OpenStreetMap — then finding a routable road network and public address points that could connect buildings to exits and searches.

The audit exposed the hard part: no single building source was complete everywhere. Select any of the eight islands, compare its inventories, and toggle every available source directly on the map.

Source assembly / KauaʻiFour building inventories, no universal winner
Comparison layers Unavailable sources are disabled for that island.

Kauaʻi county-building sample. The internship reference is the benchmark for the first island audit.

Building AI · statewide review Compare the broad scan with mapped buildings.

Loading 1,325 sampled squares…

The purple and yellow outlines are the new 55,060-tile scan of 2022 imagery. “Without nearby footprint” means no control footprint within about 15 m—not a confirmed missing building. Zoom in to inspect outlines against imagery, controls, and addresses. Training, Validation, and Test only filter the earlier labeled experiment.
Building AI · update underway 2024 scan in progress

We’re replacing the earlier imagery run. The comparison map will return after the corrected scan is checked.

3–3 · Put it all together

Put it all together.

Build one playful version of the Haleilio Road case study, one evidence layer at a time.

  1. 01
    Road layerHighway and neighborhood connections
  2. 02
    Building layerMapped homes in the one-exit zone
  3. 03
    AI audit layerPossible footprints missing from the baseline
  4. 04
    Address layerSearch anchors matched to nearby homes
  5. 05
    TemperatureCurrent forecast context
  6. 06
    FoliageMapped vegetation around the property
  7. 07
    Altitude + slopeTerrain beneath and around the home
  8. 08
    WindSpeed and direction at this location
  9. 09
    HumidityMoisture in the current forecast

Start with the road network, then add each source used by the screening tool.