bitztedder/gpr-synth-v1

Synthetic GPR Radargrams with Object Labels

Labelled ground-penetrating-radar sections for training subsurface object detectors. Every label is exact, because every object was placed by the generator rather than drawn by a person.

One tile per class, boxes drawn straight from the label files — nothing tidied for the picture. Bottom right is empty ground, a third of the dataset.

Generated with gprsynth (MIT). No real survey data is included — the background is synthesised, so this dataset carries no third-party rights.

What's in it

	surveys	tiles	empty tiles
train	240	21,006	6,902 (33%)
val	60	5,528	1,565 (28%)

40,558 boxes over 26,534 tiles, near-even across the five classes (19–22% each). Tiles are 512 traces wide, 256 samples tall.

A third of the tiles carry no object at all, and that is deliberate. Most real road is empty; a detector trained only on ground that contains something learns to always find something.

Why this exists

Detecting voids under a road from GPR means finding hyperbolas in thousands of traces. Training a detector needs labelled radargrams, and there are almost none: labelling takes an expert, and the expert is the bottleneck you were trying to remove. This dataset sidesteps the bottleneck instead of paying it.

Classes

id	class	polarity	note
0	cavity	−1	air is slower than soil, so a void inverts the reflection
1	pipe	+1	line source — one scatterer, no cross-track extent
2	manhole	+1	shallow, wide, strong; rim rings louder than the middle
3	box	+1	buried structure
4	patch	+1	resurfaced cut — very shallow, flat, no thickness echo

Acquisition model

A generic 24-channel road array: 0.150 ns sampling, 0.080 m channel pitch, 0.050 m trace pitch, 2 m depth window, 256 samples per trace. These are round defaults for a system of this kind, not the calibration of a particular cart — nothing in the model depends on the exact values, and gprsynth/spec.py is where you put your own.

Each channel sees a target at its true slant range, so the hyperbola sits deeper the further that channel is from the object — that arrival-time shift, not amplitude, is what recovers cross-track position.

Format

YOLO. images/{train,val} + labels/{train,val} + data.yaml. Point ultralytics at data.yaml and train — no conversion step.

images/train/0000/s0007_c11_512-1024.png
labels/train/0000/s0007_c11_512-1024.txt

Filenames are s{survey}_c{channel}_{t0}-{t1}, so any tile traces back to the survey, the array channel, and the trace range it came from.

Tiles sit in subfolders of 50 surveys each rather than one flat directory — 26k files in a single folder is past what most git hosts allow. Ultralytics globs recursively and mirrors the subpath from images/ to labels/, so the nesting costs you nothing.

The split is by survey, never by tile. Tiles from one survey overlap and share ground; splitting by tile leaks and reports a score the field will not reproduce.

What the box covers

The box is the apex plus a fixed slice of the limbs — not the full extent of the visible hyperbola. You can see this in the figure above: the limbs run outside the box, clearest on box and pipe.

That is deliberate, and it is the convention hyperbola detectors are normally trained against. A limb asymptotes and stays faintly visible for as far as the section is wide, so "everything you can see" is not a box anyone can draw — for a loud shallow target it would be the whole tile. But it does mean a detector trained here learns the apex signature rather than the whole V, and you should know that before you compare its output against boxes somebody drew by hand to a different rule.

The slice is a constant (18 samples), chosen ahead of time. The separate decision of whether a target is labelled at all in a given channel is measured off the rendered section instead. If you want box extent measured the same way, _label_box in synth.py is the one function to change.

Honest limits — read before using

• The surveys are interchangeable, and that is measurable. Train a detector with a deliberately leaky tile-level split and its reported score comes out 0.002 mAP50 above what it scores on surveys it has never seen — that is, the leak buys nothing. Leakage pays only when there is something recording-specific to memorise, and this generator draws every survey from one distribution: same clutter statistics, same layering, same wave, only the seed changes. Real surveys differ in soil, moisture, pavement and backfill; this data has none of that between-survey variation, so a model trained here has never had to cope with it. Worked through in this notebook.
• cavity and pipe are solid. Geometry and polarity match what a void and a pipe actually do.
• manhole, box, patch are plausible approximations that have NOT been validated against labelled real examples.
• The background is not a soil simulation. It reproduces correlated clutter, layering, and the direct wave — the three things a detector keys on — and nothing else.
• No real survey has been compared against this data. If you have field data, measure the domain gap before trusting a model trained here.
• Synthetic data is a starting point, not a substitute for field validation.

Licence

CC BY 4.0. Generator is MIT.