Surveying has always been taught to me as an art as much as a science, with its modern functions deeply reliant on the ways of the past. While this is a nice notion, I would say the current dearth of work for companies around the country indicates that age-old idea may need some tweaking.
Surveying has always followed a progression in its work, where points are created with lines and areas following in turn. Adding data to these bits of spatial information is the basis of GIS, an industry unto itself. This data-additive method has been the standard from the beginning of time, but I would venture to try another method instead. With the use of laser scanners and photo modeling, the entire environment is captured in one setup. Therefore, all the as-built data exists in one setup and observation. Further layout and analysis can be done with this 3D model with few corrections to the raw dataset. This paves the way for a data-subtractive work flow where the formerly symbolized features of survey plats can be extracted as they are or could be in the real world. To me, this would seem to be a much more sure way to create solid survey data than any total station or GPS.
Another impact of using this data-subtractive workflow is that you have more data than the client needs. While they may be intrigued enough to purchase this extra information, the greater profit more likely lies with finding additional buyers. Perhaps a GIS firm or government committee needs to know something about the area you have been working in. By capturing the whole environment, you have also captured whatever tidbit of data the decision makers are seeking. Often these groups will spend thousands of dollars commissioning studies to discover what you already conveniently have. By making this available to them, they can save money by giving half of their budgeted amount to you for data that has already been paid for! Governments may not actually work this way, but the private sector holds more and more promise as businesses and individuals begin to need spatial data to solve problems more efficiently and reliably as their own bottom lines continue to shrink.
These are just ideas for now, but the potential of exponential returns for data that is not hard to create is certainly tantalizing to someone in my position. The problem I have is no one in my area is considering working with this kind of spatial data. If you have thoughts, experience, advice or questions, please leave them in the comments section below!