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During most of my geomatics career I have been involved with the production side of the business. Prior to GeoCue, I was with Z/I Imaging and Intergraph. There I managed various activities that ranged from metric aerial camera development to stereo-based elevation extraction software. For the first 6 years of GeoCue Corporation, we were solely involved in the data production side of the geomatics business. In this sort of very tight Business to Business relationship, our customers always knew exactly what the software and/or hardware was designed to do, how it would be deployed and where it fit into their business model.
Several years ago GeoCue acquired a company, QCoherent Software, LLC, that builds standalone and ArcGIS-based LIDAR exploitation tools (the LP360 family). Unlike the GeoCue product family, these software tools are directed toward customers who exploit geomatics data rather than create those data.
The LP360 products have taken us into a space where we are not as familiar with the customer base. We recently attended a GIS conference hosted by a state GIS organization. This particular state has a state-wide LIDAR data set with a number of counties actively adding higher resolution collects to the repository. I asked a lot of questions of the participants of the conference regarding their use of the data. A clear majority were aware that they had access to data but were not currently using the data in their jobs and did not have a clear understanding of the value of intensity modulated point cloud data. In other words, these data are, for the most part, sitting on the shelf. Now certainly the original value of the data was extracted in major production operations such as the Digital Flood Insurance Rate Maps (DFIRM) program, but it is painful to see so much useful data gather dust.
So this state of affairs led me to this thought; our industry really needs to educate and evangelize the applications of point cloud data. The next thought was okay what does this class of user do with this type of data?
As I explored these ideas, I realized that I have to separate “receipt of data” from “use of data.” We train a lot of end-use customers in the techniques used to validate LIDAR data when it is received from a contractor. In fact, we did a 4 webinar series on this topic for the Association of State Floodplain Managers in the second half of 2012 with an average of 750 attendees per webinar so there is obviously a lot of interest in this topic. But this is not use of the data, it’s just acceptance of data. Hence the remainder of this column is devoted to ideas on how point cloud data can improve productivity for consumers of these types of data. Now I suspect that most readers of LIDAR News will be familiar with these ideas but I think we all have evangelization responsibility to help grow this industry.
Perhaps the best example is simply visualization. Point cloud data can dynamically add a third dimension to imagery in a non-intrusive way. Figure 1 depicts a map view in ArcGIS with dynamic generation of contours from a point cloud source. In this particular case, the contours (red and green) are being generated in real time from a layer of point cloud data (from a semi-global matching algorithm) hidden under the image. This workflow is part of the QC acceptance of ortho photos but can be used day to day to simply visualize the underlying terrain. Note in this example that the elevation data are clearly incorrect since they do not accurately model the slopes along the railroad tracks. This is significantly better than simply using a static layer of contour vectors since the user can dynamically adjust the level of detail to fit the operation.
Another example of “adding the 3rd dimension to imagery” is depicted in Figure 2. Here the user has drawn a profile line (using LP360 for ArcGIS) to inspect the shoreline near a home (perhaps for an insurance assessment). The LIDAR data are once again hidden behind the ortho view and only come in to play when activated by the user invoking a tool that used these vertical data. Note that the user can focus on the image data (something that is familiar to all observers) while still visualizing the built-up water barrier between the foundation and shoreline.
A third example of simple visualization is shown in Figure 3. This is a transmission line corridor showing a deer hunting stand that has been surreptitiously erected within the right of way. Today it is rather rare for transmission line operators to invest in the technology necessary for managing LIDAR data and providing viewing environments. However, these data are obviously rich in content beyond line rating and vegetation management.
A list of general “consumer” level uses of LIDAR data might include:
Adding the 3D element to imagery via various display techniques
Extracting 3D information in localized areas such as roof drip lines
Adding drainage, flat water bodies and other hydrological network features
“Conflating” 3D values on to 2D vectors such as road networks
Performing true 3D measurements on ortho or map surfaces (measuring in an ortho does not provide a true value when there is terrain relief)
Performing “line-of-sight” analysis
Measuring 3D areas
Creating 3D analysis surfaces such as contours and shaded relief
I think you can see that this list goes on and on. Of course, one inhibiting factor is that many local governments do not have the server infrastructure to provide point cloud data to each desktop. My answer to this is to simply replicate the data (it is nearly always being used “read only”) to 4 TB USB disk drives and hand them out! A 4 TB external disk drive can be purchased for less than $175 so there is no excuse for not having these data available to everyone in an organization.
I strongly encourage everyone involved in the production side of LIDAR to have conversations with consumers of data as to how they can improve their data use experience by making LIDAR as common a data layer as ortho photos. This ubiquitous use of data will not only improve the overall LIDAR market but, more importantly, will bring dynamic 3D data use to analytic users.