Visualization products computed from a raster elevation model still form the basis of most archaeological and geomorphological enquiries of lidar data. We believe there is a need to improve the existing visualizations and create meaningful image combinations that preserve positive characteristics of individual techniques. In this paper, we list the criteria a good visualization should meet, present five different blend modes (normal, screen, multiply, overlay, luminosity), which combine various images into one, discuss their characteristics, and examine how they can be used to improve the visibility (recognition) of small topographical features. Blending different relief visualization techniques allows for a simultaneous display of distinct topographical features in a single (enhanced) image. We provide a “recipe” and a tool for a mix of visualization techniques and blend modes, including all the settings, to compute a visualization for archaeological topography that meets all of the criteria of a good visualization.

Archaeologists engaging with Airborne Laser Scanning (ALS) data rely heavily on manual inspection of various derived visualizations. However, manual inspection of ALS data is extremely time-consuming and as such presents a major bottleneck in the data analysis workflow. We have therefore set out to learn and test a deep neural network model for classifying from previously manually annotated ancient Maya structures of the Chactún archaeological site in Campeche, Mexico. We considered several variations of the VGG-19 Convolutional Neural Network (CNN) to solve the task of classifying visualized example structures from previously manually annotated ALS images of man-made aguadas, buildings and platforms, as well as images of surrounding terrain (four classes and over 12,000 anthropogenic structures). We investigated how various parameters impact model performance, using: (a) six different visualization blends, (b) two different edge buffer sizes, (c) additional data augmentation and (d) architectures with different numbers of untrainable, frozen layers at the beginning of the network. Many of the models learned under the different scenarios exceeded the overall classification accuracy of 95%. Using overall accuracy, terrain precision and recall (detection rate) per class of anthropogenic structure as criteria, we selected visualization with slope, sky-view factor and positive openness in separate bands; image samples with a two-pixels edge buffer; Keras data augmentation; and five frozen layers as the optimal combination of building blocks for learning our CNN model.