# GPR Data Interpretation Approaches in Archaeological Prospection
## 7 Discussion  
In this paper the drawbacks of the conventional GPR data interpretation process were discussed, and studies aiming to overcome these pitfalls were reviewed. What becomes clear is that GPR data from archaeological prospection are quite complex and site-dependent. No straightforward process exists in both processing and interpreting the data, which requires a deeper understanding of the GPR method. Therefore, it is recommended to follow a target-directed analysis while taking into account both B-scans and C-scans to get the most out of GPR recorded information. Attribute analysis used in seismic data seems to be beneficial in enhancing archaeological remains not very well described by the traditional instantaneous envelope. Further, developments in hardware and computational systems allow using multiple sensors to collect information from the area of interest. This combined information can improve interpretation significantly leading to a complete understanding of the buried past. At the same time, it allows generating more intuitive visualizations through highly detailed and immersive 3D models. This can be expanded in fusion approaches that aim to merge the information acquired from the different sensors, reducing, in this way, interpretation uncertainties. However, the approaches mentioned above come at the price of significantly increasing time spent in data collection, processing time, and interpretation. On this matter, the automatic interpretation using DL algorithms may have a potential to aid data interpretation and be embedded in the process by using trained models to detect and identify complex patterns. Even though DL algorithms are widely used in computer vision tasks such as image classification, object detection, and image segmentation for image analysis, they have not yet been explored with GPR data derived from archaeological prospection. The main reason is the limitation in data availability. For this reason, an example to classify C-scans produced by the traditional interpretation approach was performed. For the classification task, CNNs with AlexNet architecture were used. The obtained results showed great potential to classify patterns attributed to ancient structures, noise and geophysical anomalies not identified as a structure. This choice of classes produced interesting results and showed that if enough data exists, a complete analysis of the subsurface conditions mapped in GPR C-scans can be achieved with CNNs. Some practical insights were obtained during the training dataset construction, which was proven challenging due to the limited GPR images featuring structures which are rarer to find. At the same time, they are more often noisy and exhibit other geophysical anomalies of the subsurface that are not a structure. For this reason, an overlapping and sliding window crop approach was mandatory to effectively increase the images to a number that allowed learning. Further, two different splitting approaches were tested to examine which one can lead to better performance and generalization. These were tested on an evaluation set that was made using 100 new GPR examples from the sites of Ancient Halos in Magnesia, Thessaly, Greece, and Sissi in Heraklion, Crete, Greece. The results showed how great an impact this has had on the learning process, with the random approach performing better. However, considering the small number of examples in the evaluation, more tests are required to reach a safer conclusion. Overall, the results of this application suggest that CNNs and deep architectures are a promising lead to improve GPR data interpretation processes, and could be combined with the different approaches reviewed in this paper. The presented results are part of the first author's Ph.D. dissertation which was partially funded by POLITEIA, Action KRIPIS research project no.