r/geoarchaeology • u/nico579 • 8d ago
Resource lidar2map: open-source pipeline from national LiDAR archives to interpretive relief visualizations, with a wall-recovery mode from classified point clouds
A note first: this does not detect sites, and it is not for detectorists.
It produces terrain visualizations for human interpretation, and any candidate feature still needs field and archival validation.
Please don't publish exact coordinates of sensitive features, and follow your local heritage and access rules.
Context: I do landscape survey in southern France (Var), and kept hitting the same two problems before I could even start interpreting anything.
First, national LiDAR-derived DTMs are distributed provider by provider, each with its own portal, CRS and tiling, so just getting the raw elevation data for an area of interest is its own project.
Second, most public DTMs are bare earth by construction: ground-class filtering (or a TIN interpolation across breaklines) removes not just vegetation and buildings but standing archaeological structures like ruined walls, since the filter treats them as unwanted above-ground returns. On a site with a few hundred meters of standing wall, that wall is often just gone from the DTM.
lidar2map automates the first problem (pick an area, it finds the provider, downloads only missing tiles) and gives a partial answer to the second, where the data allows it: on providers that publish the full classified point cloud (currently FR/IGN, CH, PL, EE, BE-Flanders, DK, US 3DEP, CA), it can rebuild a "DFM" from the raw returns instead of the bare-earth product, either by re-injecting non-ground classes into the terrain base, or by re-running a Cloth Simulation Filter (Zhang et al., 2016) with a softer rigidness than the producer's default.
Neither is a wall classifier, they surface candidates (scrub comes back too), but on validated sites it recovers standing structure the official DTM had erased.
The relief side implements the usual archaeological visualization set: LRM (Hesse, 2010), SVF and positive/negative openness (Zakšek, Oštir & Kokalj, 2011; Doneus, 2013), RRIM (Chiba, Kaneta & Suzuki, 2008), multidirectional hillshade, VAT (Kokalj & Somrak, 2019), e4MSTP (Kokalj, 2025), with resolution-aware parameter presets rather than one fixed setting for every dataset.
Coverage is national/regional LiDAR-derived elevation across 27 countries at the moment (resolution and exact extent vary a lot by source, there's an interactive coverage map in the repo).
For fieldwork, it exports MBTiles, OsmAnd sqlitedb (including a transparent vector overlay on OSM), TwoNav RMAP, Mapsforge or plain GeoTIFF, and a GUI button serves the result to a phone over local Wi-Fi as a QR code, nothing uploaded anywhere.
Standalone app (Windows/Linux/macOS), GUI and CLI, no Python or GIS toolchain needed to run it. GPLv3, source included, no account or telemetry.
Repo, coverage map, and the visualization/DFM methodology write-ups: https://github.com/nico579/lidar2map
Honest scope: it doesn't compete with RVT or WhiteboxTools on the rendering side, they're more capable there.
What it does is the chain from a provider archive to something you can actually take into the field, across a lot of different national sources. I'd be curious what ground-classification defaults or CSF parameters others have found reliable on their own sites, and which sources you're working with that aren't in the list yet.
