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

Automation on steroids: an exploration of why deep learning is dominating automation
Automation on steroids: an exploration of why deep learning is dominating automation
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.
Kramer, Iris, Caroline
ba56efc0-ce81-4897-9d74-4bec3e9e1fca
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Kramer, Iris, Caroline
ba56efc0-ce81-4897-9d74-4bec3e9e1fca
Hare, Jonathon
65ba2cda-eaaf-4767-a325-cd845504e5a9
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e

Kramer, Iris, Caroline, Hare, Jonathon and Prugel-Bennett, Adam (2017) Automation on steroids: an exploration of why deep learning is dominating automation. Aerial Archaeology Research Group 2017 Annual Meeting, Croatia. 13 - 15 Sep 2017.

Record type: Conference or Workshop Item (Other)

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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More information

Published date: September 2017
Venue - Dates: Aerial Archaeology Research Group 2017 Annual Meeting, Croatia, 2017-09-13 - 2017-09-15

Identifiers

Local EPrints ID: 416398
URI: http://eprints.soton.ac.uk/id/eprint/416398
PURE UUID: 43149a1b-c007-4f6a-ae4d-173c6421832a
ORCID for Jonathon Hare: ORCID iD orcid.org/0000-0003-2921-4283

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Date deposited: 15 Dec 2017 17:30
Last modified: 30 Jan 2020 01:34

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