Is photogrammetry enough? Explaining sites that require LiDAR from four perspectives

Photogrammetry and LiDAR are both powerful methods for capturing a site in three dimensions. However, in practical work, simply comparing which is superior does not lead to an appropriate decision. In fact, depending on the condition of the subject, the required deliverables, site constraints, and the work setup, there are cases where photogrammetry is sufficient, and cases where choosing LiDAR is necessary to avoid major problems in subsequent processes.

Many practitioners searching for information on "LiDAR vs photogrammetry" want to know not only the differences in the technologies themselves but also which one they should choose for their own site. Being able to create visually pleasing 3D models does not necessarily mean you can consistently obtain results that are usable in practice. If you make the wrong decision in the early stages, you may need to re-measure or reprocess, which will affect the schedule and quality.

In this article, we organize situations where photogrammetry is sufficient and situations where LiDAR tends to be necessary from four perspectives that are directly relevant to practical decision-making. Rather than a mere technical comparison, we concentrate our explanation on points that often cause uncertainty in the field, so please use this as material to help you decide on adoption and to inform measurement planning.

Table of Contents

• Basic concepts for determining when photogrammetry is sufficient

• Perspective 1: Assess based on the amount of vegetation and obstructions

• Perspective 2: Assess based on the complexity of the shape and surface conditions

• Perspective 3: Judge based on required accuracy and deliverables

• Perspective 4: Assess based on on-site conditions and work efficiency

• Concrete examples of sites where LiDAR is likely to be required

• Think of photogrammetry and LiDAR not as opposing technologies but as tools to be used appropriately.

• Summary

Basic considerations for when photogrammetry is sufficient

First and foremost, it should be emphasized that photogrammetry is by no means a lesser method than LiDAR. Photogrammetry is a technique that reconstructs shape from images taken of a subject from multiple directions, and under the right conditions it can produce very high-density, visually appealing three-dimensional data. Because color and pattern information remain easy to handle as-is, it has significant advantages for uses such as appearance documentation, progress sharing, and current-condition assessment.

Therefore, if the target surface is sufficiently visible, shooting conditions are stable, and the desired deliverables are primarily for capturing overall geometry, as‑built verification, or record‑keeping, photogrammetry is often more than adequate. Its ability to efficiently capture large areas is another advantage, and at some sites you can meet your objectives without using LiDAR.

On the other hand, photogrammetry, which strongly tends to "reconstruct what is visible from images," often struggles with areas that are hard to see, surfaces with few distinguishing features, shaded parts, intricate recessed areas, and ground surfaces beneath vegetation. In addition, depending on surface conditions and lighting, gaps in the point cloud, noise, and shape distortions can occur. Even if it looks plausible visually, it can be difficult to use when checking cross sections or calculating quantities.

By contrast, LiDAR emits light and directly measures distance, which makes it relatively more reliable for capturing geometry. It is comparatively less dependent on the color or pattern of the target surface, and it is effective even for complex shapes or in places that are difficult to reconstruct using visual information alone. In particular, when what is needed on site is not a "clean 3D model" but "positional information with minimal gaps" or "shapes that can be confirmed in cross section," LiDAR's advantages become clear.

Therefore, when considering whether photogrammetry is sufficient, you must determine under which conditions photographs alone can achieve professional-quality results, rather than relying on the novelty of the technology or general impressions. To avoid making the wrong judgment, from here we will organize the discussion around four perspectives.

Perspective 1: Assess based on the density of vegetation and obstructions

The difference between photogrammetry and LiDAR becomes most apparent on sites with vegetation or other obstructions. To conclude, on sites where plants are dense or the object of interest is hidden by other things, it is risky to judge that photogrammetry alone is sufficient. This is because photogrammetry is based on visible surface information; if the ground or structural reference surface you actually want to capture is not visible beneath leaves or branches, the reconstruction quality for those parts will decline significantly.

For example, when checking the terrain before land development, understanding the current condition of slopes, inspecting the ground surface around rivers, or assessing a site prior to maintenance management, you may intend to capture the ground itself, but grasses and shrubs are often widely distributed. In such sites, using photogrammetry tends to produce a shape close to the top of the vegetation, yielding data that differs from the actual ground surface required. While this can be useful as an appearance record, it is unsuitable for estimating earthwork volumes or for capturing cross-sections.

LiDAR tends to be needed precisely in situations where the "visible" and the "wanted to know" do not match. In cases where reflected returns passing through vegetation can be utilized, it becomes easier to grasp ground-surface trends that are difficult to capture with photographs alone. Of course, how much of the ground surface can be captured depends on vegetation density and measurement conditions, but at the very least it provides more useful information for understanding the ground surface than photogrammetry.

Similarly, sites with many occlusions present the same problem. In places where equipment is densely packed, where scaffolding and piping are intertwined, under bridges or behind structures, or deep inside narrow passages or openings, it can be difficult for a camera to secure sufficient parallax, and even if images are captured the reconstruction may be unstable. In photogrammetry, it is important not only whether the target is captured but whether sufficient correspondences can be established from different angles. Simply increasing the number of photos does not necessarily solve the problem.

Therefore, when checking how much vegetation or other obstructions are present on site, you should base your assessment not on whether something is visible from above, but on whether the required surfaces are reliably visible. The judgment will vary greatly depending on whether it is sufficient to see only the top of the object, or whether you also need the sides, the underside, parts with depth, or areas in shadow.

Conversely, photogrammetry is highly effective on sites where paved surfaces are clearly defined and obstacles are few, on surfaces that are easy to photograph such as exterior walls and roofs, and on developed land that can be uniformly observed. If vegetation is sparse, the target shape is uncomplicated, and the required faces are sufficiently visible, it is highly likely that the objective can be achieved without relying on LiDAR.

In practice, it can be too late to make a decision after arriving on site. At the measurement-method selection stage, it is important to anticipate in advance whether the surfaces you want to capture will be affected by vegetation or other obstructions. If subsequent processes require ground surface extraction or cross-section verification, it is safer to consider the need for LiDAR early rather than optimistically assuming photogrammetry will be sufficient.

Perspective 2: Assessing Shape Complexity and Surface Conditions

Another important consideration is how complex the object's shape is and whether the surface conditions are suitable for photogrammetry. Photogrammetry yields more stable results when the target surface has features that make it easy to find corresponding points across multiple images. Conversely, it struggles with featureless surfaces, highly reflective surfaces, transparent or translucent areas, and thin, intricate components.

For example, smooth, low-pattern walls and floors may at first appear easy to photograph, but because they lack distinctive features in the images, the stability of reconstruction can decrease. The more uniform the surface, the harder it becomes to determine which position corresponds to which part of which image. Also, glossy or wet surfaces are more susceptible to reflections, causing results to vary depending on the time of capture and the weather.

Furthermore, subjects that contain many thin elements—such as handrails, piping, steel members, support columns, mesh-like components, and equipment clusters with many level changes—are also disadvantageous for photogrammetry. Even if they are visible in the images, they can be missing in the 3D reconstruction, appear bloated or thicker than they are, or multiple components can become fused together. In practice, these kinds of localized artifacts affect the overall quality. Even when there is no visible issue, they can impede dimensional verification and interference checks.

On the other hand, because LiDAR directly acquires distance information, it is less dependent on surface texture and tends to offer greater stability in shape acquisition. Of course it is not perfect, but it is highly effective for capturing complex structures, fine surface irregularities, and confirming shapes around intricate equipment. In particular, at sites where reproducing positional relationships and shape outlines is more important than realistic appearance, the value of LiDAR increases.

When judging from this perspective, what you should pay attention to is not the whole object but where the "parts important to the deliverable" are. For example, if you are only creating an overall exterior model of a building, photogrammetry may be sufficient, but if some areas include complex shapes—such as around openings, at equipment connection points, or in backside detailing—then looking only at those critical parts may mean LiDAR is required. In other words, you need to consider which parts must not fail, rather than whether it is generally easy to capture.

Surface conditions also change with the time of day and the environment. Photogrammetry tends to become less stable during periods of strong direct sunlight, in areas with deep shadows, on wet concrete, in dark locations, or in environments where dust is airborne. As the site person in charge, it is essential to consider the working conditions on the day of measurement. Even if things appear fine under normal circumstances, shooting conditions during routine inspections or on active construction sites can be harsher than expected.

To determine whether photogrammetry is sufficient, it is more important to consider whether features are easy to capture, whether the shape appears continuous, and whether missing important parts is acceptable than to ask whether the object is large or small. Photogrammetry is well suited to objects with simple shapes, broad surfaces, and high visibility. Conversely, when an object is complex, thin, recessed, and has many critical parts, the need for LiDAR increases.

Perspective 3: Decide based on the required accuracy and deliverables

The third consideration is how much accuracy is required and what will be used as the deliverable. If you choose a measurement method while leaving this unclear, you may succeed in data acquisition but find the data unusable during the deliverable creation stage. The choice between photogrammetry and LiDAR should be made based on the final required outputs, not on which is easier to perform at the time of measurement.

Representative examples of cases where photogrammetry is sufficient include visualization of existing conditions, sharing progress, record keeping, obtaining a general overview, and creating 3D models for explanatory materials. These are applications where some omissions or local inaccuracies are unlikely to seriously impede overall understanding, and where the value of a colored, easy-to-read model is high. When you want to convey an object's appearance or spatial relationships intuitively, the characteristics of photogrammetry are particularly effective.

On the other hand, in situations where strong numerical reproducibility is required—such as cross-section verification, quantity calculation, dimensional comparison, condition assessment, construction management, and precise alignment—simply being able to view something in three dimensions is not sufficient. It is important to confirm that required positions have not been missed, that surface irregularities have been faithfully captured, and that shapes have not been distorted by shadows or occlusion. Forcing photogrammetry in these cases increases the corrective burden in subsequent processes.

What practitioners often overlook is that "required accuracy" and "required density" are not the same thing. Photogrammetry tends to produce high-density point clouds and a detailed appearance, so at first glance it can seem highly accurate. However, having many points is different from having reliable positions for the required parts. What matters is whether data exists, without gaps, at the necessary locations and with the required accuracy.

For example, when assessing the current conditions of earthworks or land development, surface continuity is important, while for structural inspections it is important to capture the shapes of edges and deformed areas. For preliminary checks prior to equipment replacement, it is meaningless unless locations prone to interference and the detailing of connections are clearly defined. The quality aspects that should be prioritized vary depending on the intended use of the deliverables.

In such cases, photogrammetry is sufficient when "some parts may be unseen but this does not hinder achieving the objective." Conversely, LiDAR tends to be required when "there are places that must not be missing," "you want to examine cross-sections or positional relationships precisely later," or "you prioritize shape reproducibility over appearance." In particular, data used in the next stage for design adjustments or construction decisions require reliability more than visual appearance.

Also, whether the deliverables will be reused is a factor to consider. If point clouds and 3D models created once are intended to be used in the future for maintenance, additional design, comparative evaluation, or periodic monitoring, the quality of the initial acquisition must be considered carefully. What seems sufficient at the time can reveal shortcomings when used for a different purpose. The initial decision is especially important at sites where re-surveying is difficult.

In other words, when discussing accuracy you should not simply compare higher or lower; you need to clarify what the accuracy is for. Whether the deliverable is for presentation/explanatory purposes, for management, or for design decision-making will change whether photogrammetry is sufficient. The closer the business objective is to quantitative use, the more worthwhile it is to proactively consider LiDAR.

Perspective 4: Assess based on site conditions and work efficiency

The fourth perspective is site conditions and work efficiency. When selecting technology, people tend to focus on accuracy and data acquisition performance, but in practice operational conditions such as safety, access restrictions, work time, the difficulty of revisits, and changes in the surrounding environment have a major impact. Whether photogrammetry is sufficient is also determined by whether the necessary shooting paths at the site can be secured.

Photogrammetry assumes that the subject is photographed from multiple angles with overlap.

Therefore, it performs well in sites where it is easy to move around the subject in a wide space, in locations where capturing from the air or at height is convenient, and where there is sufficient time for the work. If there are enough shooting positions around the subject, little occlusion, and stable capture conditions, high-quality data can be obtained efficiently.

However, at sites where access is restricted, in areas close to operating equipment, in locations affected by traffic restrictions, in confined or narrow sections, or on terrain with large elevation differences, it may not be possible to secure ideal imaging paths. In photogrammetry, bias in imaging angles directly leads to quality degradation, so when site conditions are poor things often do not go according to plan. Even if you think you have captured the images, gaps or distortions may be discovered during post-processing.

LiDAR is often needed at sites where you must reliably capture geometry in a short time. For example, locations with limited time before traffic can be reopened, areas around equipment with short downtime, post-disaster sites that are difficult to revisit, and sites where construction progresses and conditions change quickly—at such places on-site reliability is important. Methods that are less affected by visibility and that make geometry capture more stable will, as a result, reduce rework.

Also, work efficiency needs to be considered not only on-site but also including post-processing. Even if photogrammetry data capture itself goes smoothly, tasks such as organizing images, adjusting processing parameters, removing noise, and checking for missing data can take time. Of course LiDAR also requires setup and maintenance work, but depending on site conditions, reliability at the acquisition stage can reduce the overall man-hours. It is important to judge based on the total man-hours to the final deliverable, not on whether you can save an hour in the field.

Furthermore, when there are multiple personnel on site, reproducibility of the work is also important. Methods that are only stable when performed by particular skilled operators pose a risk for organizational operations. Photogrammetry is prone to having results affected by variations in shooting plans and capture quality, so if operational rules are vague, stability will differ. At some sites, methods that can consistently produce a certain level of quality even when equipment conditions change somewhat are easier to manage.

Therefore, from the perspectives of site conditions and work efficiency, the criterion should be not "Is it possible under ideal conditions?" but "Can it be carried out with high reproducibility under actual constraints?" Photogrammetry is effective at sites that are easy to photograph, but as conditions such as limited time, confined spaces, occlusions, and difficulty of revisiting overlap, the need for LiDAR increases.

Specific examples of sites where LiDAR is often required

Considering the four perspectives discussed so far, there are common characteristics to sites where LiDAR tends to be required. First, these are sites that are easily affected by vegetation or obstacles, where the surfaces you want to capture cannot be adequately seen from photographs alone. When understanding the ground surface is important—such as on slopes, around forested areas, in grasslands, along rivers, or near property boundaries—photogrammetry alone often lacks the reliability needed for deliverables.

Second, these are sites with complex structures and many recessed areas and slender members. Around equipment, clusters of piping, under bridges, structures with backfill, and facilities with complex ancillary items, photogrammetry can produce omissions greater than they appear. If the spots you need are missing when you later cut cross-sections, practical use becomes difficult.

Third, these are sites where deliverables are used for quantity calculations, cross‑section checks, design updates, clash checks, and similar purposes. For such uses, minimizing missing geometry and the reliability of positional information are prioritized over appearance. Even if a model works as a visual representation, it is meaningless if it cannot be used for design decisions. The more exacting the final use, the greater the justification for adopting LiDAR.

Fourth, these are sites with strict on-site constraints and little opportunity for re-measurement. In short-duration construction, nighttime work, under traffic restrictions, around operating equipment, immediately after a disaster, or in restricted-access areas, it is important to reliably capture the necessary information. The more a site is one where missed data on-site cannot be recovered later, the more emphasis should be placed on measurement stability.

Conversely, at sites that do not fall into those categories, photogrammetry may be sufficient. If the target surface is clearly visible and easy to photograph, if the primary purposes are visual assessment and recording, and if some omissions will not hinder practical work, photogrammetry is very reasonable. What matters is choosing based on the necessary requirements, not selecting LiDAR just because it seems new or favoring photogrammetry merely because it seems easier.

Consider Photogrammetry and LiDAR as Complementary Rather Than Opposing

In practice, it is also important not to think of photogrammetry versus LiDAR as a binary choice. Depending on the site, dividing their roles and using both can ultimately be more efficient than trying to complete everything with only one. For example, perform overall appearance assessment and wide-area documentation with photogrammetry, and use LiDAR to supplement only the parts where gaps are unacceptable or the complex areas.

This differentiation is effective because there is not just one desired outcome. On site, multiple objectives exist simultaneously, such as explanations to stakeholders, progress sharing, record preservation, quantity verification, and design adjustments. Sometimes a model that is easy to view is needed, while other times an accurate geometry that can be confirmed in cross-section is required. Therefore, technology selection should be organized by purpose rather than by a single evaluation.

Also, when deciding whether to adopt a solution, you’re less likely to fail if you assess not only whether it is sufficient for the current site but also whether it can be scaled to future operations. Even if photogrammetry is sufficient for the current task, if you plan to expand in the future into as-built management, maintenance management, setting out, or comparison with current conditions, you may need a system with stronger capabilities in positional information and on-site utilization. The goal should not be 3D modeling itself, but it is important to consider whether it will lead to subsequent operational improvements.

When making a selection, clarifying each of the target object, the desired deliverables, acceptable omissions, site constraints, whether a revisit is possible, and how it will be used in subsequent processes will make your judgments less likely to waver. Correctly identifying sites suited to photogrammetry does not mean dismissing LiDAR as unnecessary. Conversely, recognizing sites that require LiDAR is not the same as opting for excessive equipment. The essence is to choose a method that can consistently achieve the required quality without undue effort.

Summary

When deciding whether photogrammetry is sufficient or LiDAR is necessary, it is important to relate the decision to the actual conditions on site rather than simply comparing the technologies. The four axes for judgment are the amount of vegetation and obstructions, the complexity of shapes and surface conditions, the required accuracy and deliverables, and the site conditions and operational efficiency. By checking these four in sequence, it becomes quite clear which sites are fine for photogrammetry and which should choose LiDAR.

Photogrammetry is a very effective method when the subject is clearly visible, shooting conditions are stable, and the primary focus is on visual assessment or documentation. On the other hand, LiDAR becomes increasingly necessary at sites where you need to capture the ground surface, comprehensively capture complex structures, perform cross-section checks or quantity calculations, or where re-measurement is difficult. The important thing is not how good it looks, but whether you can consistently obtain results that are genuinely usable for your work.

If you want to connect the results of three-dimensional measurement to on-site position checks and positioning tasks in the future, considering post-measurement uses in advance will increase effectiveness. For example, when you want to streamline on-site positioning and verification work based on the acquired data, combining it with an iPhone-mounted GNSS high-precision positioning device such as LRTK makes it easier to link the utilization of three-dimensional data to field operations. Rather than stopping at choosing between photogrammetry and LiDAR, it will be increasingly important on future sites to consider the subsequent steps of measuring, checking, and applying the data.