How to Read LiDAR Data

LiDAR data is one of the most powerful tools available to modern treasure hunters, but interpreting it requires understanding what you are looking at. This guide covers the three main LiDAR products and how to spot the anomalies that matter.

A Digital Elevation Model (DEM) represents the bare earth with vegetation and structures removed. It is the most useful product for treasure hunting because it reveals the actual ground contour. Look for rectangular depressions in the 3–6 ft range (possible cache pits), linear features (old roads, trails, or wagon tracks), circular depressions with spoil rings (mine shafts or wells), and flat elevated platforms (building foundations).

A Digital Surface Model (DSM) includes everything — vegetation, buildings, and terrain. Comparing DSM to DEM reveals vegetation height and can indicate disturbed areas where growth patterns differ from surrounding cover. Areas with unusually low or dense vegetation over otherwise uniform terrain may indicate subsurface disturbance.

Hillshade visualization renders the terrain as if illuminated from a specific angle, creating shadows that emphasize subtle elevation changes. Multi-directional hillshade (illumination from several angles combined) reveals features that single-angle views miss and is the most intuitive way to visually scan for anomalies.

Wagon tracks appear in the DEM as parallel linear depressions 4–6 feet apart. Cache depressions are rectangular, 3–6 feet wide, and typically isolated from natural drainage patterns.

Foundation remnants show as rectangular raised edges with 90° corners. Mine shafts appear as circular depressions 4–10 feet across, often with a surrounding spoil ring of excavated material.

Historical trails appear as narrow (18–36 inch) linear features that follow terrain contours rather than cutting across them. Reading contour behavior is a fast way to tell historical human-made features from natural drainage.

PinPoint Treasure automates LiDAR interpretation using AI analysis of USGS 3DEP and OpenTopography data. The AI identifies and classifies terrain anomalies, cross-references them with historical records, and returns findings with evidence grades — no manual LiDAR interpretation required.