New desktop tool
Train archaeological site-sensitivity models from approved records and a LiDAR DEM on your desktop, then use the model offline in the Ology field app. Spatial validation and professional review help determine where the model is useful.
How the model works
The trainer compares known-site landscape settings with the wider terrain, tests the model on held-out data, and exports a model for offline use in the app. The diagram is illustrative and contains no project data.
What it does
The trainer builds a predictive site-sensitivity model. It learns, from the places where sites are already known, what the terrain tends to look like where people lived — then it scores every patch of a new area for how much that ground resembles known-site terrain. The output is a heat surface: brighter means more likely to reward survey.
The model never tries to “see” a house pit or a scatter in the imagery. It asks: is this spot the kind of place — near reachable water, on a flood-safe bench, gently sloped, sheltered — that people historically chose? That is a question terrain can answer, which is why it works even for sites that leave no visible trace on the surface.
A prioritisation aid, not a site locator: a high score means “worth looking here first,” not “a site is here.” Ground-truth everything.
Every terrain predictor comes straight from your DEM — nothing to download.
Two model types
Transparent arithmetic. Every number is auditable — you can say exactly why a spot scored high, like “sites are 3.4× more likely on flood-safe terraces.” Fast, and the right first model.
Learns interactions FR cannot: “south-facing matters, but only on gentle slopes near water.” On tabular terrain data, gradient boosting is the current state of the art — it consistently matches or beats neural networks in independent benchmarks — and the trainer still reports each factor’s importance.
Searches model configurations and keeps the one that best predicts ground the model didn’t train on — not the one that best memorises.
Trains several models and averages them for a steadier surface. Exports to the same file format, so the app runs it with no changes.
Ensemble disagreement becomes a genuine confidence measure: “high sensitivity and the models agree” is a stronger survey target.
Spatial validation
Tick “Held-out validation map” and the trainer runs an honest experiment: it splits your area into a training region and a spatially separate held-out region, trains only on the training region, then checks whether the real sites in the unseen region land in the model’s high-scoring zones.
The held-out map and capture rate show how the model performed on a separate part of the training landscape. Compare that result with a chance baseline, examine where the model misses, and test transfer to new terrain before relying on it for survey planning.
How to use it
Then press Run Training. AirDrop the finished .json to your iPhone → Ology → Terrain → Archaeological Site Sensitivity → Use model → import.
Documentation
The guide assumes no machine-learning background. It explains in plain language what the trainer is doing under the hood, why the method is sound, how to run it, and how to read the results — including the held-out validation map, the ethics and honest caveats, and troubleshooting for macOS and Windows.
Get the trainer
The Ology Model Trainer runs on macOS and Windows (Python 3.12/3.13). It is currently distributed directly to interested archaeologists and cultural-resource professionals while it is refined. Request access with your contact details and general area of interest.
Site locations are protected under NHPA/ARPA and state law. Your training data never leaves your machine — the trainer runs entirely locally, and models derived from confidential sites should be handled with the same care.
Downloads
Public downloads will appear here as they are released. For now, the user guide is available to everyone and the trainer kit is available by request above.
No trained model is included on this website. Any future public model needs a confidentiality and disclosure review before release.
Desktop to field
The trainer and the app are two halves of one workflow: heavy LiDAR processing and honest validation on your computer, then a single portable model file that predicts offline anywhere the app can open a map. Both are aids to professional judgement, not replacements for it.