LiDAR vs Photogrammetry: Differences & Use Cases in 3D Mapping Technologies

LiDAR vs photogrammetry defines how organizations generate and analyze 3D mapping technologies for modern geospatial data collection.  

Both are critical remote sensing methods used by surveying drones in industries like construction, agriculture, mining, and urban planning.  

The choice between the two hinges on required accuracy, surface characteristics, environmental conditions, and data-processing capacity.​ 

LiDAR vs Photogrammetry

LiDAR (Light Detection and Ranging) emits laser pulses—often 100,000 to 1,500,000 per second - to measure distances and generate dense 3D point clouds with 1–3 cm vertical precision.1  

Photogrammetry, by contrast, reconstructs geometry from overlapping images captured by drones or aircraft and processed through image triangulation. It produces color-rich 3D models but relies on visual correlation and lighting conditions. 

In short: 

• LiDAR excels at accuracy, vegetation penetration, and large-scale precision mapping. 

• Photogrammetry excels at affordability, textural realism, and visual analytics. 

Surveying drones increasingly integrate both payloads to produce data-rich digital terrain models (DTM) that balance texture and true elevation mapping.​ 

Why LiDAR & Photogrammetry matters in modern mapping

As industries shift toward automation and AI in mapping, understanding these two remote sensing methods becomes critical for workflow optimization.  

LiDAR enables direct terrain measurement - even under canopy - while photogrammetry supports fast, color-detailed reconstructions for client presentations and GIS visualization.​ 

In infrastructure, LiDAR reduces rework by generating precision 3D base maps for grading and cut-fill analysis. In agriculture, regular photogrammetric flights using UAV applications detect crop stress via RGB and multispectral imagery. 

Thus, LiDAR vs photogrammetry is not a competition but a matter of context. Combining them through surveying drones and mapping software ensures complete geospatial workflows, accurate to the centimeter and interpretable by non-specialists alike.​ 

What is LiDAR? 

LiDAR is an active 3D mapping technology that measures distance via light pulses reflected off ground or infrastructure. Sensors calculate return time, called time-of-flight, to generate spatial coordinates (x, y, z).  

Mounted on surveying drones, LiDAR systems typically operate on 905 nm or 1550 nm wavelengths for safe, high-efficiency scanning.2 

Each laser pulse forms a point cloud; data density ranges from 100–1000 points/m² depending on sensor power and flight altitude. Integration of IMU and GNSS modules ensures sub-decimeter georeferencing even on steep terrain.

Key LiDAR advantages include: 

• Superior LiDAR accuracy (1–3 cm vertical) 

• Canopy penetration for digital terrain models (DTM) 

• 24/7 operability, unaffected by lighting 

• Minimal reliance on ground control points 

• Seamless processing into elevation maps & 3D visualizations 

Heavy-lift UAVs carrying LiDAR payloads scan 100+ hectares in a single flight, making it the preferred method for large-scale projects like flood modeling, corridor mapping, and dense vegetation surveying.​ 

What is Photogrammetry? 

Photogrammetry is a passive optical technique based on overlapping aerial images. It uses triangulation to infer depth and build 3D mapping technologies from multiple views. The workflow involves: 

• Capturing thousands of geo-tagged photos with 70–90% overlap. 
• Aligning imagery through Structure from Motion (SfM) algorithms. 
• Generating a textured mesh, orthomosaic, and digital surface model (DSM). 

Photogrammetry’s major advantages are straightforward setup and photogrammetry cost & accessibility - standard mirrorless cameras or drone-integrated sensors can deliver sufficient precision for small-scale projects.​ 

Key strengths: 

• Cost-effective entry using consumer UAVs 

• True-color textures suitable for visualization 

• Easy compatibility with GIS integration workflows 

• Suitable for construction mapping and visual inspections 

The limitation is precision sensitivity: lighting, shadows, and vegetation gaps can reduce accuracy by 15–30% compared with LiDAR. Still, cloud-based automation and AI in mapping now cuts processing time dramatically and enhances image correlation.​ 

Use Cases 

Construction mapping: 

 LiDAR supports volumetric analysis, slope stability checks, and terrain flattening before excavation with <3 cm tolerance. Photogrammetry complements visual progress tracking, BIM documentation, and façade texture modeling. Combined, they deliver both precision and presentation.​ 

Agriculture drones: 

Multispectral photogrammetry enables NDVI health analysis. When terrain modeling is required, LiDAR provides accurate elevation data for advanced irrigation management and drainage mapping in precision agriculture.​ 

Forestry: 

LiDAR quantifies canopy height, biomass, and ground-level DTM through dense coverage. Photogrammetry aids species identification by analyzing RGB reflectance. Hybrid maps guide sustainable logging and fire prevention.​ 

Mining: 

LiDAR dominates for stockpile volume estimation and pit deformation detection under low visibility or dust. Photogrammetry adds true-color context for inspections and reclamation reporting.​ 

Urban planning:  

LiDAR captures street-level geometry, while photogrammetry textures building exteriors for photorealistic 3D visualizations. Together, these feed digital-twin city models in GIS integration platforms.​ 

Future Trends 

Automation and AI in mapping are reshaping both technologies. Machine learning now classifies LiDAR point clouds automatically for terrain segmentation, building extraction, or vegetation filtering and cloud computing accelerates photogrammetry, from raw photo import to finished orthomosaic in under two hours for moderate projects.​ 

Emerging hardware adds: 

• Smartphone-based LiDAR sensors achieving 2–3 cm scan accuracy 

• AI-driven flight planning for autonomous surveying drones 

• Onboard real-time SLAM for low-GNSS environments 

• Hybrid payloads that simultaneously record LiDAR returns and RGB imagery on one UAV flight​ 

As 3D mapping technologies converge, users can generate geospatial datasets that feed directly into mapping software pipelines for urban analysis, terrain correction, and infrastructure monitoring.​ 

Conclusion  

Both LiDAR and photogrammetry shape how modern surveying drones collect and convert geospatial data. LiDAR enhances precision mapping on complex or vegetated terrain, while photogrammetry ensures visual clarity and cost efficiency for accessible landscapes. Integrating both achieves complete geospatial workflows - accurate, scalable, and visually intuitive. 

Explore LiDAR payloads, photogrammetry cameras, and BeyondSky’s enterprise-ready drone software on the official hardware and mapping pages to build your next-generation surveying workflow today. 

FAQs

1. What is LiDAR and how does it work for 3D mapping?

LiDAR emits laser pulses to measure distances via time-of-flight, creating dense 3D point clouds with 1–3 cm accuracy, ideal for penetrating vegetation and generating DTMs.​

2. What is photogrammetry and what are its core strengths?

Photogrammetry reconstructs 3D models from overlapping images using Structure from Motion, delivering cost-effective, color-rich orthomosaics and DSMs for visual analysis.​

3. When should I choose LiDAR over photogrammetry?

Choose LiDAR for high-accuracy elevation under canopy, thin features, or low-light conditions; photogrammetry suits open terrain needing texture and affordability.​

4. What are the main use cases for each technology?

LiDAR excels in forestry, mining volumes, and corridor surveys; photogrammetry fits agriculture NDVI, construction visuals, and urban texture mapping.​

5. Can LiDAR and photogrammetry be used together?

Yes, hybrid workflows combine LiDAR's precise elevation with photogrammetry's rich imagery for complete DTMs, DSMs, and textured 3D models on surveying drones.​

6. What are the key limitations of each method?

LiDAR is costly and heavy; photogrammetry struggles with shadows, vegetation, and low contrast, often needing good lighting and ground control points.​

7. What future trends are emerging in LiDAR vs photogrammetry? 

AI automates point cloud classification, cloud processing speeds workflows, hybrid payloads integrate both, and real-time SLAM enables GNSS-denied mapping.​