What Kind of Data Do You Need?
Choosing between photogrammetry and LiDAR comes down to understanding what kind of data your project truly needs. Whether you’re after rich visual detail, precise terrain mapping, or a hybrid workflow, knowing the strengths of each method is key to turning drone data into real insights.
As drone mapping becomes more accessible and capable, many organizations are asking the same question: Should we use photogrammetry or LiDAR for our project?
Both methods generate highly accurate spatial data — but they do so in very different ways. Choosing the right one depends on what you need to measure, how precise the data must be and the environment you’re operating in.
Photogrammetry: Data from Imagery
Photogrammetry uses high-resolution photographs taken from multiple angles to reconstruct a three dimensional model. Specialized software detects common points across overlapping images, creating orthomosaics, digital surface models (DSMs), and 3D point clouds.
It’s ideal for projects where visual context matters — such as construction monitoring, asset documentation, or environmental mapping. Because it captures detailed texture and color, photogrammetry is excellent for visual analysis and progress reporting. However, its accuracy depends on several factors:
- Lighting and exposure — consistent, diffused light minimizes shadow distortion.
- Ground Sample Distance (GSD) — lower GSD (smaller pixel size) equals higher resolution.
- Overlap and flight altitude — at least 75–85% frontlap and 65–80% sidelap ensures complete coverage.
- Ground Control Points (GCPs) — surveyed targets that anchor the imagery to true coordinates.
When properly planned with GCPs and RTK/PPK corrections, photogrammetry can achieve centimeter-level accuracy.
LiDAR: Data from Measurement
LiDAR (Light Detection and Ranging) takes a different approach. Instead of relying on images, it emits laser pulses and measures the time it takes for each beam to return. The result is a dense, georeferenced point cloud that represents the terrain and structures below — even through vegetation.
Because LiDAR is an active sensor, it can operate in low-light or shadowed conditions and penetrate surfaces like canopy or brush where cameras cannot. This makes it particularly useful for:
- Forestry and vegetation analysis
- Corridor mapping and utilities
- Topographic surveys in uneven terrain
SLAM LiDAR: Mapping Without GPS
In some environments — such as tunnels, dense forests, or urban canyons — GPS signals are unreliable or unavailable. That’s where SLAM LiDAR (Simultaneous Localization and Mapping) comes in.
SLAM systems use onboard sensors and algorithms to track movement and orientation in real time, allowing drones or handheld scanners to map indoors or under canopy without GNSS.
Accuracy vs. Precision: Knowing the Difference
When evaluating drone data, accuracy and precision are often confused — yet they describe different qualities.
For example, a photogrammetric model may show consistent geometry (high precision) but still be offset from its true location if no GCPs were used (low accuracy). LiDAR, with proper calibration and RTK corrections, can achieve both — but understanding this distinction is critical when defining project requirements or compliance standards.
- Accuracy means how close your measurements are to the true position on the ground.
- Precision means how consistent your measurements are with one another.
For example, a photogrammetric model may show consistent geometry (high precision) but still be offset from its true location if no GCPs were used (low accuracy). LiDAR, with proper calibration and RTK corrections, can achieve both — but understanding this distinction is critical when defining project requirements or compliance standards.
Finding the Right Resolution for Your Project
Resolution is another key decision point. It’s defined by Ground Sample Distance (GSD) for imagery and point density for LiDAR.
Matching resolution to project objectives — rather than simply “collecting more data” — keeps workflows efficient and deliverables fit for purpose.
A common misconception is that more data automatically means better results. In practice, excessive resolution can be unnecessary, costly, and time-consuming if it exceeds the accuracy requirements of the end use. The right question isn’t “how much data can we collect?” but “what accuracy and level of detail does this project actually require?”
By aligning your data resolution to project goals — whether for volumetric analysis, asset documentation, or environmental monitoring — you reduce storage demands, shorten processing times, and keep operational costs predictable. Precision, in this sense, is not about excess; it’s about relevance.
- High-resolution photogrammetry (1–2 cm GSD) is suited for small sites or detailed inspections but generates large datasets and longer processing times.
- Moderate resolution (3–5 cm GSD) balances efficiency and accuracy for most construction, mining, and engineering applications.
- LiDAR point density is determined by factors such as pulse rate, altitude, and scan angle. Higher densities (200+ points/m²) are used for fine detail, while lower densities (20–50 points/m²) work for general terrain.
Matching resolution to project objectives — rather than simply “collecting more data” — keeps workflows efficient and deliverables fit for purpose.
A common misconception is that more data automatically means better results. In practice, excessive resolution can be unnecessary, costly, and time-consuming if it exceeds the accuracy requirements of the end use. The right question isn’t “how much data can we collect?” but “what accuracy and level of detail does this project actually require?”
By aligning your data resolution to project goals — whether for volumetric analysis, asset documentation, or environmental monitoring — you reduce storage demands, shorten processing times, and keep operational costs predictable. Precision, in this sense, is not about excess; it’s about relevance.
The Bottom Line
There’s no single “best” choice between photogrammetry and LiDAR. Each has a role depending on environment, resolution requirements, budget, and regulatory needs.
For many projects, the ideal solution is a hybrid workflow — using LiDAR for geometry and photogrammetry for colorized context. The key is understanding the type of data your business truly needs: visual realism, precise measurement, or both.
As drone technologies evolve, the ability to match the right sensor and workflow to the right application is what separates simple data collection from meaningful insight.