Automation on steroids: an exploration of why deep learning is dominating automation

Abstract

Traditionally, research initiatives into automated detection of archaeological objects were focussed on feature engineering to detect individual object types. These methods have been criticised for their lack in accuracy which is mostly caused by their inability to capture the variability within an object type and the objects’ appearance across different land cover types.

Recently, rather than further optimizing features, research has shifted towards feature learning which offers more flexibility. This shift was triggered by the overwhelming successes of deep learning (shown for e.g. self-driving cars and medical imagery). A deep convolutional neural network is build-up out of many layers and learns features from images of known objects which are fed to the network. In the early layers of a network only basic abstractions such as lines and edges are learned and as the deeper layers are reached the features get more refined and are able to extract the key characteristics of the object type. This process is very similar to how a human learns although there are some important advantages to the structure of deep networks. For example, they can be designed to incorporate different types of remote sensor data and can hence internally compare this variety of data. In his manner a network will quickly identify obvious false positives and adapt the weights of the layers accordingly. Another important point is that a network can fully appreciate the small variation of pixel values without any image enhancements. For LiDAR data this effect can be demonstrated with a network that identifies a slope in the first layers of the network and later on learns that the slope direction and local relief are important features for a specific object type.

The above listed approaches just scratch the surface of the wide range of possible methods to using deep learning for aerial archaeology. In the end, the shift in research is mainly driven by the far-future concept of a national model which automatically retrains with newly acquired remote sensing data to allow for new discoveries that can further improve the networks.

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Identifiers

ORCID for Jonathon Hare: orcid.org/0000-0003-2921-4283

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