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DepthScout Research · Report 01

The Uneven Map Beneath the Water

What 37,672 public waters on DepthScout reveal about where measured bathymetry is easy to find, and where modeled contours still fill the gaps.

37,672sitewide waters
14,343survey-backed waters
38.1%sitewide coverage
63regional catalogs

Open a fishing map and the contour lines all look equally certain. They are not.

Some contours begin with measurements taken on the water: sonar soundings, agency lake surveys, or hydrographic work. Others are estimates built from a real shoreline and a known maximum depth. Both can help an angler plan a day. Only one can claim that the shape beneath the surface was measured.

We wanted to know how often each kind appears across DepthScout, and whether the national average tells the whole story. So we froze the public catalog on August 13, 2026, counted every water carrying the Survey depth data label, and compared coverage across 63 states, provinces, territories, and countries.

The short answer: measured bathymetry is not spread evenly. It is concentrated in a small number of places, while several of the largest regional catalogs remain almost entirely modeled.

What we found

The report uses two totals on purpose. The homepage provides the sitewide catalog count. Regional pages describe catalog placements. A lake on a border can appear in more than one region, so adding the regional pages produces a slightly larger number. We use the sitewide total for the national headline and regional placements only when comparing regions.

Figure 1

Regional catalogs by survey-backed coverage band

Number of regional catalogs in each coverage range

The catalog is split at the extremes: 16 regions were below 10% and 18 were at or above 90%. The national average is not a description of most regions.
View figure data as a table
Regional catalog count by survey-backed coverage band
Coverage bandRegional catalogs
Below 10%16
10–24.9%6
25–49.9%7
50–74.9%11
75–89.9%5
90% or more18

The average hides two different maps

A national survey-backed rate of 38.1% sounds like a fairly ordinary data gap: more modeled waters than measured ones, but plenty of both. The regional results are less ordinary.

At one end, Québec had survey-backed contours on 1,864 of 1,868 placements. Finland had them on 1,543 of 1,551. Michigan, Maine, and New Hampshire were all above 96%.

At the other end, Sweden had no survey-backed placements among 6,433 waters in the DepthScout catalog. The United Kingdom and Nova Scotia were also at zero. Wisconsin had 25 among 4,318 placements, and British Columbia had 28 among 2,459.

Those zeroes require careful wording. They do not prove that a region has never surveyed a lake, or that no public bathymetric record exists. They mean that no water in that regional DepthScout catalog carried the survey-data label on the snapshot date. A survey may be unavailable, difficult to license, published in a form that has not been integrated, or simply outside the current scope of the product.

Regional catalogPlacementsSurvey-backedCoverage
Québec1,8681,86499.8%
Finland1,5511,54399.5%
New Hampshire48848399.0%
Maine54953397.1%
Michigan1,4041,35396.4%
British Columbia2,459281.1%
Wisconsin4,318250.6%
Saskatchewan34910.3%
Sweden6,43300.0%
United Kingdom85000.0%

Figure 2

Composition of the 12 largest regional catalogs

Share of regional placements that were survey-backed or approximated

Catalog size and measured coverage move independently. Large catalogs appear at both ends of the coverage range.
View figure data as a table
Survey-backed and approximated placements in the 12 largest regional catalogs
Regional catalogTotalSurvey-backedApproximatedCoverage
Ontario7,9292,9704,95937.46%
Sweden6,43306,4330.00%
Wisconsin4,318254,2930.58%
Minnesota3,7741,9171,85750.79%
British Columbia2,459282,4311.14%
Québec1,8681,864499.79%
Finland1,5511,543899.48%
Michigan1,4041,3535196.37%
United Kingdom85008500.00%
Norway66451514977.56%
Maine5495331697.09%
New Hampshire488483598.98%

This is the central finding: the map is not gradually thinning from one place to another. It has hard edges. Cross a jurisdictional line and the share of measured contours can change from nearly everything to almost nothing.

Five regions account for two-thirds of survey-backed placements

The concentration becomes clearer when the question changes from “What percentage of each region is surveyed?” to “Where do the measured maps in the catalog actually come from?”

Five regional catalogs contributed 9,647 survey-backed placements:

  1. Ontario: 2,970
  2. Minnesota: 1,917
  3. Québec: 1,864
  4. Finland: 1,543
  5. Michigan: 1,353

Together, they accounted for 66.6% of all survey-backed regional placements in the snapshot. That does not mean those five places have the best bathymetry, the newest surveys, or the most surveyed lakes in the world. It means they currently carry most of the measured coverage visible through DepthScout.

The distinction matters because catalog size and survey coverage are separate things. Ontario had the largest regional catalog and the largest count of survey-backed placements, yet its coverage rate was 37.5%. Québec and Finland were smaller catalogs but almost completely survey-backed. Sweden and Wisconsin were among the largest catalogs overall and among the least survey-backed.

Figure 3

Leading contributors of survey-backed placements

Regional placements labeled “Survey depth data”

The top five account for 66.6% of survey-backed regional placements. This measures contribution concentration, not map quality.
View figure data as a table
Survey-backed placement contributions by the five leading regional catalogs and all other regions
Catalog groupSurvey-backed placementsShare
Ontario2,97020.51%
Minnesota1,91713.24%
Québec1,86412.87%
Finland1,54310.65%
Michigan1,3539.34%
All other regions4,83533.39%

For an angler, this explains why the DepthScout experience can feel different from one region to another. In one place, nearly every water may show solid, measured contours. In another, the useful information may be the real shoreline, the maximum depth, and an honestly labeled approximation.

“Survey-backed” describes origin, not quality

For this analysis, survey-backed means a water labeled Survey depth data on DepthScout. Its contours are derived from measured bathymetry: government soundings, digitized agency survey maps, or hydrographic surveys.

Approximated contours start with a real waterbody, its shoreline geometry, and a sourced maximum depth, but the individual contour lines are modeled. They are drawn differently and labeled so they are not mistaken for measured bathymetry.

That binary distinction is useful, but it is not a quality score.

A survey-backed map might come from dense modern sonar. It might also come from a paper survey made several decades ago and later digitized. Minnesota’s Department of Natural Resources says the lake maps available through its LakeFinder collection were compiled from the 1930s through the 1990s and makes no guarantee about their accuracy. Those maps are still evidence-based in a way a modeled basin is not; they simply answer a different question than a recent high-density sonar chart.

Several details determine how much confidence a measured map deserves:

This is why DepthScout treats every chart as fishing guidance, never navigation. The survey label tells you that someone measured the bottom. It does not tell you that every shoal is current, every rock is marked, or the water level matches the day you launch.

Why coverage varies so much

This snapshot measures the outcome of a long data pipeline, not the cause of every regional gap. Still, the public sources show why “Has this lake been mapped?” is not a yes-or-no question.

In Québec, the province’s Géobase des bathymétries de lac du Québec is offered through the public data portal in reusable formats including GeoJSON, GeoPackage, CSV, and file geodatabase. Finland’s environmental institute publishes downloadable lake and river depth areas, contour lines, and sounding points. Those are the kinds of files that can be checked, transformed, and linked back to a named source.

Minnesota illustrates a different reality. Its DNR makes a large archive of lake maps available, but many are historical image files, and commercial reuse requires a license agreement. The information exists; using it responsibly involves conversion, metadata work, quality review, and permission.

Other programs publish PDFs, scanned paper maps, web viewers, point soundings, contour vectors, raster grids, or no central collection at all. Waterbody names and identifiers differ between agencies. Survey dates may be missing. Coordinate systems and depth units change. Licensing language ranges from explicit open-data terms to silence.

The result is an integration gap as much as a surveying gap. A depth survey becomes broadly reusable only after it can be found, understood, licensed, matched to the right waterbody, and converted without stripping away its provenance.

We cannot use this snapshot to assign a cause to an individual region, and we have not tried to. Low coverage in DepthScout should be read as a lead for further investigation, not a verdict on the work of that region’s fisheries or mapping agencies.

What better public bathymetry looks like

If agencies and data stewards want their lake surveys to travel farther, five publishing choices make an outsized difference.

  1. Release reusable geometry, not only a picture of the map. Sounding points, contour vectors, and documented rasters preserve more information than a scanned PDF.
  2. Attach a stable waterbody identifier. Names repeat. A durable ID makes it possible to join bathymetry to fisheries records, access points, regulations, and later surveys without relying on fuzzy name matching.
  3. Keep the survey metadata with the data. Year, method, units, datum, contour interval, and responsible agency should survive every download.
  4. State the reuse terms plainly. A clear license is more useful than a dataset whose legal status has to be guessed.
  5. Preserve the distinction between measured and modeled. An estimated contour can be useful. It becomes misleading only when its origin disappears.

For mapping platforms, the obligation runs the other direction: keep the source visible, retain important caveats, never merge modeled and measured contours under one label, and provide a correction path when the public record is wrong or outdated.

What this means when you are choosing a water

The practical lesson is simple: check the data label before reading the contour lines too closely.

On a survey-backed water, use the map to study the measured shape of the basin (points, breaks, holes, humps, channels) while remembering that survey age and water level still matter.

On a water with approximated contours, use the chart at a broader scale. It can help you understand the shoreline, depth range, and likely basin shape. Do not treat a specific underwater turn or isolated hump as a measured feature.

Neither chart belongs in the navigation category. Slow down, use current official navigation products where required, watch the water in front of you, and follow local rules.

Methodology

We captured the public DepthScout catalog on August 13, 2026.

The national figures came from the DepthScout homepage, which reported 37,672 lakes and rivers in the sitewide catalog and 14,343 waters with real survey depth data. We divided the survey-backed count by the sitewide count to calculate the national rate of 38.1%.

For regional analysis, we collected the total and survey-backed counts published on each of 63 regional fishing-map pages. For every region we calculated:

survey-backed coverage = survey-backed placements ÷ total catalog placements

The regional pages summed to 37,905 placements, including 14,482 survey-backed placements. These totals are slightly higher than the national figures because regional catalogs are not a deduplicated list of waters. Shared and border waters can appear in more than one regional catalog. We therefore use “placements” whenever regional rows are added together.

Coverage bands were defined before comparing regions:

All percentages in the article are rounded for readability. Calculations were made from the unrounded counts.

Limitations

This report is a census of the public DepthScout catalog at one point in time. It is not a census of every lake, every bathymetric survey, or every file held by a government agency.

The analysis does not measure survey age, sounding density, positional accuracy, contour interval, licensing difficulty, or whether an unintegrated public survey exists. It also does not rank fishing quality. A region with a high survey-backed rate is not necessarily a better fishing destination, and a survey-backed chart is not automatically more current than an approximation.

DepthScout’s catalog changes as waters and source data are added or corrected. The downloadable dataset is frozen so this report remains reproducible even when live totals move.

Frozen dataset

All 63 regional catalogs

Choose any column heading to sort. Regional totals are placements, not unique waters.

Survey-backed coverage in DepthScout regional catalogs on August 13, 2026
Source
Ontario7,9292,9704,95937.46%Regional page
Sweden6,43306,4330.00%Regional page
Wisconsin4,318254,2930.58%Regional page
Minnesota3,7741,9171,85750.79%Regional page
British Columbia2,459282,4311.14%Regional page
Québec1,8681,864499.79%Regional page
Finland1,5511,543899.48%Regional page
Michigan1,4041,3535196.37%Regional page
United Kingdom85008500.00%Regional page
Norway66451514977.56%Regional page
Maine5495331697.09%Regional page
New Hampshire488483598.98%Regional page
Montana3663501695.63%Regional page
Saskatchewan34913480.29%Regional page
Wyoming32053151.56%Regional page
Massachusetts262255797.33%Regional page
New Brunswick2611808168.97%Regional page
Washington2522331992.46%Regional page
Indiana2141555972.43%Regional page
North Dakota2122021095.28%Regional page
Iowa1921811194.27%Regional page
Texas1911058654.97%Regional page
Ohio1771581989.27%Regional page
Alberta171164795.91%Regional page
Florida1671541392.22%Regional page
New York1521391391.45%Regional page
Connecticut146139795.21%Regional page
Nebraska141134795.04%Regional page
Missouri11571086.09%Regional page
Vermont111248721.62%Regional page
California100168416.00%Regional page
Oklahoma9688891.67%Regional page
South Dakota945895.32%Regional page
Utah932912.15%Regional page
Arizona884844.55%Regional page
Colorado863833.49%Regional page
Alaska83562767.47%Regional page
Illinois83275632.53%Regional page
New Mexico774735.19%Regional page
Nova Scotia740740.00%Regional page
Kansas7366790.41%Regional page
West Virginia6356788.89%Regional page
Idaho626569.68%Regional page
Mississippi6295314.52%Regional page
Kentucky54272750.00%Regional page
New Jersey5446885.19%Regional page
Alabama50292158.00%Regional page
Oregon50311962.00%Regional page
Tennessee48103820.83%Regional page
Georgia43162737.21%Regional page
Delaware39281171.79%Regional page
Manitoba3932782.05%Regional page
Nevada393367.69%Regional page
Virginia38221657.89%Regional page
North Carolina37122532.43%Regional page
Louisiana35112431.43%Regional page
Rhode Island2982127.59%Regional page
Pennsylvania2762122.22%Regional page
South Carolina27131448.15%Regional page
Arkansas2561924.00%Regional page
Maryland22111150.00%Regional page
Hawaii170170.00%Regional page
Puerto Rico12120100.00%Regional page

Data, sources, and citation

Download the regional survey-coverage snapshot. It includes the counts, calculated coverage rate, and source URL for all 63 regional catalogs.

Primary references:

Suggested citation:

DepthScout Research. “The Uneven Map Beneath the Water: What 37,672 Public Waters Reveal About Survey-Backed Depth Coverage.” August 13, 2026. https://www.depthscout.com/research/public-lake-depth-mapping-2026

What we want to measure next

Coverage is only the first layer. The next useful questions are about quality and change: How old are the surveys anglers can actually access? Which public programs publish soundings or vector contours instead of static images? How quickly is measured coverage expanding, and where are the largest tractable gaps?

Those questions need their own datasets. They should not be guessed from this one.