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One of the key potential benefits of MLS is that a single acquired dataset can be used for a variety of applications. The data can possibly also be “mined” for additional information that may not have been a focus of the original acquisition. Figure 1 summarizes a sampling of existing and potential applications of MLS data. Note that this list is not comprehensive as new applications of MLS are being developed on a regular basis.

3.1 Applicability

While MLS technology has many applications, it should be realized that it is “a tool in the toolbox” and that it may not always be the best solution.  In some cases, a transportation agency may be mostly concerned with an end product and not the actual technique that the service provider uses to obtain that information. A benefit/cost ratio analysis should be conducted to determine if MLS is the optimal technique for a specific project. This analysis should compare MLS to other potential technologies, and include:

  1. Accounting for all potential uses of the data during its lifespan,
  2. Estimating the workload needed to perform quality control and certification,
  3. Deciding how the data will integrate into existing workflows and determining what workflows would need to be improved and/or updated,
  4. Understanding the ability to share the data (and costs) both within the transportation agency and outside of the transportation agency,
  5. Incorporating both resolution and accuracy needs, and
  6. Considering additional data that can be acquired from the same platform.

Note that in some cases, MLS data may need to be supplemented by data from other 3D survey techniques (e.g., airborne or static scanning). Further, the necessary hardware for a MLS can be mounted to other platforms such as helicopters and boats to acquire data. A scan system that provides the best view of the object (most orthogonal acquisition), at a minimum range, will generally provide the best results.

Section 6 provides a flowchart of considerations to aid decision making.

Recommendation: Conduct a cost/benefit analysis to determine if MLS is the right approach.



Figure 1:  Transportation Applications of Mobile LIDAR (current and emerging).


3.2 Data Collection procurement categories

Not all applications of MLS require the same level of accuracy and density of data (resolution). There may be cases where the local (i.e., relative measurements within the dataset) accuracy of the final point cloud is more important than the network (i.e., absolute positioning in a coordinate system) accuracy requirements. An example of this would be MLS data acquired for bridge clearance calculations. In this case the local accuracy (to determine the bridge clearance) is very stringent, but the network accuracy of the point cloud (to determine bridge location) is not as critical. Therefore, it is difficult to establish data procurement categories with a “one size fits all” approach.

As a result, we are recommending a Data Collection Category (DCC) approach where the general accuracy and density requirements for the point cloud can be identified by the transportation agency for each application.  The nine categories of this DCC approach can be seen graphically in Table 1.  The numbers represent the varying orders of accuracy (1 = High, 2 = Medium, 3 = Low), which will have the greatest influence on project cost. The letters represent the levels of point density (A = Coarse, B = Intermediate, C = Fine) on the targets of interest, which is easier to achieve through driving slower or making multiple passes.  Note that these DCC categories (e.g., high) are relative to typical MLS capabilities and do not correspond to other acquisition technologies.  For example, the “Coarse” DCC for MLS contains higher point densities (<30 points/m2) than those commonly achieved with airborne LIDAR (1-8 points/m2).

This DCC approach is meant to aid in planning, coordination, and decision-making purposes.   Because these are generalized categories, Part II contains guidance for specifying accuracy and point density requirements explicitly on a continuous scale once the general data collection category has been decided and provided to the technical staff responsible for developing contract requirements. This approach allows managers to focus on the application and the technologists on the theory and details.

3.3 Suggested accuracy levels for transportation applications

This section provides recommended levels of detail for a variety of transportation applications. However, when determining appropriate requirements for a statement of work, specific project requirements and/or transportation agency practices will also need to be considered.  Ideally, an agency would coordinate needs between departments to determine the maximum benefit to cost ratio for a MLS project.  Obviously, datasets collected at higher accuracies and point densities will be useable for applications that do not require as high of accuracy or point densities, although this may not be cost-effective. In contrast, data collected at a lower DCC may still be useful for an application requiring a higher DCC.  For example, drainage analysis (1A) could benefit from 2B data compared to what is available; however, the analysis may be more difficult to perform and less-reliable than if 1A data were collected.

Recommendation: Agencies should take into consideration all potential uses when deciding on the level of accuracy and resolution for a specific project.


Table 1:  Matrix of application and suggested accuracy and resolution requirements. Network accuracies may be relaxed for applications identified in red italics.  Note that these are only suggestions and may change based on project needs and specific transportation agency requirements. 


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