Utility Record Sources – The need for comprehensive investigation:

I recently penned an article about utility record data, more specifically the conversion of SUE CAD Data to GIS and how that conversion essentially removes the adjudged quality level assignments that were indicated in the CAD depiction. One astute commenter opined that the QLD data itself may have come from a GIS system housing utility information, so why wasn’t that data automatically QLD? This is a fair question, and it boils down to the oversight and judgement of a licensed professional. While that answer is true, it is an oversimplification of the reasons and process.

The answer, while seemingly straightforward, involves complexities that merit deeper examination. Indeed, it's true that QLD data often originates from GIS systems housing utility records. However, these data sets, even when sourced from GIS, should only be regarded as record information. The critical assessment and QLD assignment lie in the hands of a licensed SUE professional, who evaluates whether the data meets the criteria for QLD designation. Record data, regardless of its source or previously adjudged quality level, does not automatically translate to QLD data. During the conversion process from certified deliverable formats to GIS platforms, data often undergoes changes such as shifting, rotation, or scaling. These alterations, driven by local controls, coordinate bases, or the need for clearer visual representation, can degrade the accuracy and precision of the original data.

Utility record data can emerge from a plethora of sources. Comprehensive investigation into all potential sources is crucial for determining the data's relevance to a specific project. These sources extend beyond obvious ones like utility owners. They include systems like the National Pipeline Mapping System (NPMS), US Energy Information Administration (USEIA), regional entities like the Texas Railroad Commission (TxRRC), and local resources such as city or county GIS systems, state DOTs, and public works departments. Even anecdotal accounts, like those from local utility inspectors, can hold valuable information. It is important to document the source of the data and how the data was utilized within the overall project deliverable.

One call markings, also known as 811 maps and marks, are a potentially significant source of record data. The data from these markings, while valuable, must be analyzed alongside other project information for relevance and accuracy. It's noteworthy that data provided by utility owners to their one call locators can sometimes be more detailed than that released in standard record inquiries. However, the reliability of such data remains contingent on verification by a SUE professional, as the methodologies used by one call locators are typically limited. It is important to note that one call markings are performed by others, not under the supervision or control of the SUE professional, and therefore cannot achieve a quality level assignment without the SUE professional’s analysis.

The advent of Artificial Intelligence (AI) in SUE record research marks a promising development. AI systems compile and conflate available record data for a specific area, employing sophisticated algorithms to derive the software determined best possible solutions. These systems also analyze satellite data and historical imagery to detect changes in topography indicative of utility infrastructure changes. While AI's capabilities in processing vast data sets and generating robust outputs are remarkable, the final assessment and application of AI-derived data to a project still fall under the purview of the SUE professional. AI systems compile data from multiple sources and systems, many of which are GIS systems which can have a wide range of accuracy. AI, despite its advancements, cannot replace the critical judgment and experience required for QLD assignment.

To recap, the conversion of SUE CAD data to GIS formats, and the subsequent interpretation and application of this data, is a nuanced process that requires the oversight and judgment of skilled professionals. The role of AI in this domain is growing, but the human element remains indispensable, ensuring that data accuracy and applicability are maintained throughout the process. As the field continues to evolve, the importance of professional expertise in managing and interpreting utility record data cannot be overstated.