METHODS
What 10Point measures,
and what it doesn't.
10Point is a ranked scouting signal built from terrain, cover, and access-related landscape structure. This page explains the inputs, the ranking logic, and the limits — so you know exactly what signal you're using in the field.
THE SYSTEM
Landscape inputs
terrain, cover, access
↓
Species model
per-region training
↓
Percentile ranking
state + national
↓
Hunt decision
your verification
The signal contract
Four things worth being precise about before you read a map: what we measure, what we rank, what the map shows, and what you still have to do.
What goes in
Terrain and landscape structure: vegetation type, canopy cover, vegetation height, elevation, slope, aspect, and road proximity. All aligned to a 30m grid across CONUS from LANDFIRE, USGS, and NLCD public datasets.
What gets ranked
Relative habitat quality for the selected species — how each 30m cell compares to the rest of the landscape. Not raw scores, and not a yes/no call. A ranking.
What you see on the map
Every cell's rank shown as a state percentile and a national percentile. Top 1%, top 5%, and top 10% tiers are called out so you can scan fast and prioritize where to spend scouting time.
What you still verify
Sign. Access. Recent hunting pressure. Crop changes, logging, burns, or other disturbance. Stand-specific microfeatures inside a 30m cell. The map narrows the question — it does not answer it.
How to read the percentile ranking
Habitat quality is shown as percentile ranks within the selected state. If a cell is in the top 5%, it means 95% of habitat in that state scores lower. The tier swatches below are how that ranking shows up on the map.
Elite — top 1%
Rare ground. Worth a real scout.
Prime — top 5%
High-confidence habitat for the species.
Strong — top 10%
Quality habitat. Good secondary option.
Use top-percentile ground to decide where to scout — not to decide whether to scout. A 30m cell can still hide the pinch that actually holds the deer.
Coverage and update cadence
Species coverage
Whitetail covers CONUS (lower 48). Turkey, elk, and mule deer cover their established ranges. Coverage expands with each data refresh.
Habitat refreshes
Habitat layers are rebuilt on each LANDFIRE, NLCD, and USGS data cycle — typically annual.
Wind refreshes
NOAA's HRRR model updates hourly. Free gets current conditions. Pro extends that to a longer planning horizon at higher density.
Where coverage is thinner
Public data density varies by region, and model confidence varies with it. The map surfaces where things are well-mapped and where they're not.
Honest limits
Not live animal location
A ranked landscape is a probability field, not a tracker. Good ground raises your odds; it does not spawn deer.
Not stand placement
Microfeatures inside a 30m cell — a pinch, a rub line, a blowdown — matter, and they're smaller than we can see.
Not current intel
Yesterday's pressure, this morning's logging crew, last week's crop change — invisible to us. Boots still run that loop.
Not uniform coverage
Some regions have denser public data than others. Confidence varies. The map says so.
Under the hood
Species models are ResUNet-34 convolutional neural networks, trained per USGS region against GAP species habitat maps and aligned to a 30m grid across CONUS. Predictions are converted to state and national percentiles so the map reads as a ranking, not a raw score.
Data sources
• LANDFIRE Existing Vegetation Type, Cover, and Height (EVT / EVC / EVH)
• LANDFIRE 30m Digital Elevation Model (elevation, with derived slope and aspect)
• NLCD Impervious Surface Descriptor (road proximity)
• USGS GAP Species Habitat Maps (training labels)
• PADUS (public-land boundaries, map overlay)
