Both produce a 3D model from drone capture, and both have legitimate places. The interesting question isn't which is 'better' — it's which actually solves the problem in front of you. Here's the honest version.
If you ask a drone LiDAR provider whether you should use LiDAR or photogrammetry for your project, the answer is — surprise — LiDAR. Ask a photogrammetry shop and the answer is — surprise — the other one. The truth is more useful than either pitch: each method wins on specific projects, and the criteria are knowable in advance.
This article works through what actually separates the two technologies, when each genuinely outperforms, and a five-question decision framework you can run against your own project without needing to call anyone.
Strip away the marketing and you're left with this: LiDAR is active sensing. Photogrammetry is passive sensing.
A LiDAR sensor fires laser pulses at the ground and measures how long each one takes to return. Range derives directly from time-of-flight and the speed of light. The sensor generates its own illumination, so it works in shadow, at night, in low light and in dust.
A photogrammetry workflow captures hundreds of overlapping aerial photographs from a normal camera, then uses computer vision algorithms (structure-from-motion, multi-view stereo) to reconstruct 3D coordinates by triangulating matched features across photos. There's no measurement of distance — only inference of geometry from correlated pixels.
Almost every other practical difference between the two methods cascades from this single distinction.
Three real advantages worth being honest about:
Photogrammetry produces a textured 3D mesh and a high-resolution orthomosaic as natural outputs of the same workflow. The model looks like the site. For visual context, planning approvals, marketing, stakeholder briefings and progress documentation, this is genuinely valuable — and LiDAR doesn't provide it natively (you can add a camera pass, but that's a different sensor working alongside).
A consumer-grade drone with a 20-megapixel camera is an order of magnitude cheaper than a survey-grade LiDAR sensor. The barrier to entry is lower, the market is more competitive, and on small, simple sites the per-hectare cost is meaningfully less.
Photogrammetry struggles where pixel matching fails — uniform colour, specular reflection, dense shadow, water. On the inverse — bare ground with texture variation, quarry walls, building facades, finished earthworks — it works beautifully. A photogrammetric capture of an empty stockpile compound or a completed civil site is more than enough for most purposes.
Five real advantages — most of them traceable directly back to active sensing:
A single laser pulse can return multiple times from different surfaces — first from a leaf at the top of the canopy, intermediate returns from branches, a final return from the ground beneath. Modern sensors record 5–8 returns per pulse. Photogrammetry has no equivalent because it can't see through anything its cameras can't image. We'll come back to this in detail below — it's the dominant filter on method selection.
Each LiDAR return is a measured distance. Each photogrammetry point is an inferred position from triangulation. Where photogrammetry has nothing to triangulate from (low-texture surfaces, repeating patterns, shadow), the inference gets noisy or fails outright. LiDAR doesn't have this failure mode.
LiDAR works at night, in heavy shadow, in haze and smoke, and under overcast cloud — the kinds of conditions that defeat photogrammetric matching. For corridor capture that has to fit inside narrow weather windows, this matters a lot more than spec sheets suggest.
Powerlines, conductors, wires, rails, antennae, slim structures — LiDAR captures them reliably as individual returns. Photogrammetry typically reconstructs thin features as noisy artefacts because the features don't appear consistently across enough photos for the matching algorithm to converge.
Engineering-grade drone LiDAR achieves ±15–30 mm vertical accuracy on bare surfaces. Drone photogrammetry tops out around ±50 mm in favourable conditions and ±100–150 mm typically. For design surfaces, volumetrics, hydrology and engineering-driven workflows, the gap matters. For planning visualisation, it usually doesn't.
Here are the realistic, defensible-to-an-auditor accuracy bands for each method on a properly-run drone capture in Australian conditions in 2026:
| Method | Vertical (typical) | Vertical (best case) | Horizontal | | ----------------------- | ------------------ | -------------------- | ---------------- | | Drone photogrammetry | ±50–150 mm | ±30 mm | ±30–100 mm | | Drone LiDAR | ±15–30 mm | ±10 mm | ±20–40 mm | | Traditional ground | ±5–15 mm | ±3 mm | ±5–15 mm | | Helicopter LiDAR | ±50–150 mm | ±30 mm | ±100–200 mm |
Two things worth noting. First, drone LiDAR genuinely outperforms helicopter LiDAR for vertical accuracy at typical project scales — the lower altitude buys higher pulse density and tighter geometry. Second, the photogrammetry numbers assume the surface is well-textured and well-lit. In the wrong conditions, the realistic numbers degrade significantly — sometimes by a factor of 2–3 in the same single project (one area of the site captures well, another doesn't).
This is the killer issue, and it deserves its own section.
Light cannot pass through an opaque leaf. A camera has no return path from anything obscured by foliage. The only thing photogrammetry can reconstruct over vegetation is the surface of the canopy — and even that suffers because foliage moves between photographs and breaks the matching algorithm's assumption of static features.
LiDAR's behaviour over vegetation is fundamentally different. A single laser pulse encounters a leaf at the top of the canopy, but the pulse has cross-sectional area larger than the leaf, so part of the pulse passes through gaps. That portion encounters another leaf lower down, and so on. Eventually some of the pulse reaches the ground. The sensor records returns at each surface, so a single pulse can produce a leaf-top point, an intermediate point, and a ground point.
Across an entire flight, even modest canopy gets enough ground returns that classification algorithms can isolate a continuous bare-earth surface beneath the trees. This is the single biggest operational difference between the two methods, and it's the deciding factor on any project that involves vegetation at all.
There is no clever photogrammetric workaround for this. Vegetation either kills the option or it doesn't.
On purely bare sites, photogrammetry typically runs at 40–60% of the cost of LiDAR for the same project — lower hardware cost, faster processing, simpler workflow. This is real and worth acknowledging.
Where the crossover happens is when vegetation enters the picture. Once the site has any meaningful canopy and the project needs a bare-earth surface:
You also see crossover when accuracy gets tight (LiDAR's ±15–30 mm vs photogrammetry's ±50–150 mm matters once the deliverable feeds engineering design rather than visualisation) and when thin features must be captured (powerlines, rails, edges of formation).
The honest summary: photogrammetry is the cheaper method for the projects where it works, and a useless method for the projects where it doesn't. Method choice is a binary decision before it's a commercial decision.
Many modern survey drones carry both a LiDAR sensor and a high- resolution camera, capturing both in a single mission. The deliverable combines:
For projects that need engineering geometry and visual context (most civil and property work), hybrid is genuinely the right answer — faster than two separate flights, cheaper than two separate mobilisations, and the two datasets are automatically registered to the same control.
The downside is hardware cost, which is reflected in pricing. A hybrid capture typically runs 15–25% above LiDAR-only for the extra camera pass.
Run these questions against your project — you'll have an answer before you finish reading them:
Is there meaningful vegetation on the site? If yes, LiDAR or hybrid. Photogrammetry is out for any deliverable that needs bare-earth surface beneath canopy.
Do you need a bare-earth surface anywhere on the site? If yes, LiDAR or hybrid. The DTM doesn't come from cameras.
What vertical accuracy do you need? ±150 mm planning grade: photogrammetry is fine. ±50 mm design grade: LiDAR or photogrammetry depending on conditions. ±20 mm engineering grade: LiDAR or traditional ground survey.
Are thin features (powerlines, rails, wires, signs) part of the capture target? If yes, LiDAR. Photogrammetry reconstructs them unreliably.
Is photoreal texture or visual output part of the deliverable? If yes (and #1–4 don't already rule it out), photogrammetry or hybrid. LiDAR captures geometry only without a paired camera pass.
For projects where the answers conflict (you need both bare-earth and photoreal, or you have vegetation but also want visual texture), hybrid is the resolution. The interactive decision tool walks through this with weighted scoring across six questions in 90 seconds.
A few things we hear regularly that aren't quite right:
"LiDAR is always more accurate." Not for textured, bare, well-lit surfaces. Good photogrammetry on a quarry wall in afternoon light will outperform poorly-controlled LiDAR on the same site. Method matters, but rigour matters more.
"Photogrammetry is always cheaper." True for the projects it can do. For projects involving vegetation or engineering accuracy, the cheaper method costs more in re-fly, re-capture or fundamentally inadequate deliverables.
"Sensor specs determine accuracy." Sensor matters, but it's typically third on the list behind point density (set by capture parameters) and control rigour (set by ground control + PPK + QA). A premium sensor on a sloppy capture loses to a mid-range sensor on a rigorous one.
"Both methods produce the same point cloud." Surface-feature only. A LiDAR cloud has classified multi-return points from canopy, ground and structures alike. A photogrammetric cloud has only surface-visible points and no classification. The file format may be the same (LAS); the contents are not.
| Situation | Recommended | | -------------------------------------------------- | ----------------- | | Bare quarry, mine pit, civil site, paving | Photogrammetry | | Open stockpile yards with good texture | Photogrammetry | | Visual context, planning approvals, marketing | Photogrammetry | | Any site with significant canopy | LiDAR | | Engineering surface for design or earthworks | LiDAR | | Corridor work (road, rail, powerline) | LiDAR | | Mining reconciliation programme | LiDAR | | Property development with vegetation + design need | Hybrid | | Asset inspection of structures | LiDAR or hybrid | | Set-out, cadastral, signed legal survey | Ground survey |
If your project is on the line between two options — or hits items in multiple rows — the decision tool gives a weighted recommendation in 90 seconds. The aim of this article is to make you confident that whichever the tool recommends, it's not a sales pitch — it's the answer that survives an honest engineering conversation.
Run the decision tool first — it's quicker than reading another article. If your project is genuinely on the fence between methods, that's the conversation we have on a scoping call.
Six questions about vegetation, accuracy, site size and deliverables → ranked method recommendation with confidence band.
Indicative pricing bands by tier, the four levers that move the number, and what isn't in the headline price.