GIS Programming: Peer Review 1

Article Citation:

Cerrillo-Cuenca, E. (2017). An approach to the automatic surveying of prehistoric barrows through LiDAR. Quaternary International, 435, 135-145. Retrieved June 14, 2017, from http://www.sciencedirect.com/science/article/pii/S1040618216002159

The author of this paper proposes a methodology for automated identification of prehistoric barrows (burial mounds) in western Europe using Python code to systematically analyze publicly available LiDAR data. Essentially, he has invented a method of creating a predictive model to help identify unknown barrow sites. 

Cerillo-Cuenca argues for the significance of his methodology on the basis that understanding the spatial patterning of barrows, particularly their relationship to settlement sites, is essential for understanding other aspects of prehistoric societies. Remote sensing and systematic survey are an efficient way to gain a better understanding of large areas, especially understudied ones, but, according to Cerillo-Cuenca, other methods used to assess LiDAR data rely on visual interpretation, which has many limitations.  

In the method outlined in this paper, the imagery is first processed to create a bare-earth topography model, which is then analyzed by a series of algorithms to identify locations that match the characteristic topography and shape of barrows, and return coordinates of those locations so that they can be confirmed, either on the ground or from aerial imagery.

I appreciated that Cerillo-Cuenca tested his method in a controlled way by comparing predicted locations to previously recorded sites, and it does appear that the process is fairly successful in identifying well-preserved barrows. It does not do well with poorly-preserved barrows, which is to be expected, but it also seems to consistently miss barrows that are smaller than average. The author acknowledges this shortcoming and suggests the use of higher-resolution imagery, with the possible trade-off of getting more false identifications—of which there are already quite a few based on this article. I would like to read more about how both of these issues might be mitigated, although, to be fair, it sounds as if the methodology is still being refined at this time. I’m also curious as to how the author would make the case that this is a significant improvement over other LiDAR survey methods (it’s likely faster, although I don’t think he explicitly says so), given that it also has clear limitations and still requires review and confirmation of predicted locations.   

On a more immediately practical level, the paper could also benefit from a clearer (and dare I say simplified?) explanation of the process used to identify potential sites, which was a bit over my head as someone with only a rudimentary understanding of this kind of analysis.  

Overall, though, this is an innovative approach that reflects the trend in archaeology towards greater use of technologies like remote sensing and digital modeling, and any method—particularly one that can be automated—for identifying new sites has big implications not only for research but for risk assessment and preservation. No predictive model can be perfect, and this one does seem to need more work, but it sounds very promising.