A comparison of automated object extraction methods for mound and shell-ring identification in coastal South Carolina

Davis, Dylan S.; Lipo, Carl P.; Sanger, Matthew C.

doi:10.1016/j.jasrep.2018.10.035

Cited by 27 publications

(50 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Freeland et al () demonstrate the first use of hydrological depression algorithms for archaeological mound detection. In this instance, an inversed DEM was created and processed through an algorithm that looks for topographic depressions, effectively identifying and mapping mound features (also see Davis, Lipo, & Sanger, in press). The work of Guyot et al () is a strong case for the use of automated object extraction methods within archaeology, as their multiscalar algorithm successfully identified over 2000 Neolithic burial mounds, while false positives ( n = 41) and false negatives ( n = 46) were minimal.…”

Section: Obia and Machine Learning In Archaeologymentioning

confidence: 99%

“…Crumley, ; Millican, ; Robinson, ). Third, future work with OBIA should seek to compare different methods of automated feature detection (e.g. Davis et al, in review). By comparing different methods, researchers can best determine which methods are most appropriate for specific purposes and thereby adopt the successes and avoid the failures and setbacks of prior studies. Fourth, to improve the ability of OBIA to detect archaeological features, researchers must share their datasets – this includes new algorithms, computer code, processing steps, and training data.…”

Section: Future Directionsmentioning

confidence: 99%

See 1 more Smart Citation

Object‐based image analysis: a review of developments and future directions of automated feature detection in landscape archaeology

Davis

2018

Archaeological Prospection

Self Cite

View full text Add to dashboard Cite

Object-based image analysis (OBIA) is a method of assessing remote sensing data that uses morphometric and spectral parameters simultaneously to identify features in remote sensing imagery. Over the past 10-15 years, OBIA methods have been introduced to detect archaeological features. Improvements in accuracy have been attained by using a greater number of morphometric variables and multiple scales of analysis. This article highlights the developments that have occurred in the application of OBIA within archaeology and argues that OBIA is both a useful and necessary tool for archaeological research. Additionally, I discuss future research paths using this method. Some of the suggestions put forth here include: pushing for multifaceted research designs utilizing OBIA and manual interpretation, using OBIA methods for directly studying landscape settlement patterns, and increasing data sharing of methods between researchers. KEYWORDS automated feature extraction, landscape analysis, machine learning, object-based image analysis, pattern recognition, remote sensing

show abstract

Section: Obia and Machine Learning In Archaeologymentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

Object‐based image analysis: a review of developments and future directions of automated feature detection in landscape archaeology

Davis

2018

Archaeological Prospection

Self Cite

View full text Add to dashboard Cite

show abstract

“…The main interest however, has been focused mainly on the analysis of archaeological features from airborne laser scanning (ALS) and satellite data. Such applications have been summarized by Trier, Cowley, and Waldeland (), Opitz and Herrmann (), Davis (), and Davis, Lipo, and Sanger (); and the recent studies of Verschoof‐van, der Vaart, and Lambers (), Meyer, Pfeffer, and Jürgens (), and Lambers, Verschoof‐van der Vaart, and Bourgeois (). Automated analysis is also considered to be an important approach for ground‐based geophysical surveys.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based automated analysis of archaeo‐geophysical images

Küçükdemirci

Sarris

2020

Archaeological Prospection

View full text Add to dashboard Cite

Thanks to recent advances in deep learning (DL) and the increasing availability of large labeled/annotated datasets and trained network models, there has been impressive progress in the automated analysis of images from different scientific domains such as medicine, microbiology, astronomy and remote sensing. The automated analysis of archaeo-geophysical data is also considered important due to the large spatial extent of areas covered by landscape surveys using multi-sensor arrays driven by motorized carts and subsequently the large volume of collected data. In this work, a convolutional neural network (CNN) is built by Python 3.6 programming language using the Deep Learning Library of Keras with Tensorflow backends, a library that implements the building blocks for CNN. The network is trained from scratch adopting U-Net architecture to accomplish an automatic analysis of the archaeo-geophysical features with emphasis on ground-penetrating radar (GPR) anomalies. K E Y W O R D S archaeo-geophysics, convolutional neural networks (CNNs), deep learning, feature extraction, GPR (ground-penetrating radar), U-Net

show abstract

“…With so much information at our disposal, the challenge lies in efficient and reproducible analysis [10][11][12][13]. It is within this set of challenges where MI research has made great strides, especially within remote sensing applications of cultural heritage and archaeological research [5,6,12,[14][15][16][17][18][19][20][21][22][23][24]. MI encompasses statistical classifiers, semi-automated analysis, deep learning, machine learning, and other methods of systematically parsing through image data to extract information [14,16,25].…”

Section: Introductionmentioning

confidence: 99%

Geographic Disparity in Machine Intelligence Approaches for Archaeological Remote Sensing Research

Davis

2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

A vast majority of the archaeological record, globally, is understudied and increasingly threatened by climate change, economic and political instability, and violent conflict. Archaeological data are crucial for understanding the past, and as such, documentation of this information is imperative. The development of machine intelligence approaches (including machine learning, artificial intelligence, and other automated processes) has resulted in massive gains in archaeological knowledge, as such computational methods have expedited the rate of archaeological survey and discovery via remote sensing instruments. Nevertheless, the progression of automated computational approaches is limited by distinct geographic imbalances in where these techniques are developed and applied. Here, I investigate the degree of this disparity and some potential reasons for this imbalance. Analyses from Web of Science and Microsoft Academic searches reveal that there is a substantial difference between the Global North and South in the output of machine intelligence remote sensing archaeology literature. There are also regional imbalances. I argue that one solution is to increase collaborations between research institutions in addition to data sharing efforts.

show abstract

A comparison of automated object extraction methods for mound and shell-ring identification in coastal South Carolina

Cited by 27 publications

References 47 publications

Object‐based image analysis: a review of developments and future directions of automated feature detection in landscape archaeology

Object‐based image analysis: a review of developments and future directions of automated feature detection in landscape archaeology

Deep learning based automated analysis of archaeo‐geophysical images

Geographic Disparity in Machine Intelligence Approaches for Archaeological Remote Sensing Research

Contact Info

Product

Resources

About