Datasets:
license: cc-by-4.0
task_categories:
- object-detection
tags:
- ground-penetrating-radar
- gpr
- subsurface
- synthetic-data
- civil-engineering
- remote-sensing
size_categories:
- 10K<n<100K
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.
cavityandpipeare solid. Geometry and polarity match what a void and a pipe actually do.manhole,box,patchare 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.