Detection of Archaeological Surface Ceramics Using Deep Learning Image-Based Methods and Very High-Resolution UAV Imageries

Αγαπίου, Άθως; Vionis, Athanasios K.; Papantoniou, Giorgos

doi:10.3390/land10121365

Cited by 11 publications

(7 citation statements)

References 40 publications

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“…ArcGIS Pro contains built‐in libraries for deep learning, which have been used successfully for archaeological applications (e.g., Agapiou et al, 2021; Bickler & Jones, 2021; Davis et al, 2021; Davis & Lundin, 2021). To implement deep learning within the ArcGIS Pro environment, input data must be a multiband raster.…”

Section: Methodsmentioning

confidence: 99%

“…Machine learning and, specifically, deep learning hold the potential to increase the efficiency and success of lidar analysis across Oceania. Deep learning through convolutional neural networks (CNNs) has been applied with varying levels of success in other regions around the world (e.g., Agapiou et al, 2021; Bonhage et al, 2021; Davis et al, 2021; Guyot et al, 2021; Somrak et al, 2020; Soroush et al, 2020; Trier et al, 2019; Verschoof‐van der Vaart et al, 2020), and some approaches have successfully identified features even when training datasets are minimal (e.g., Davis et al, 2021). CNNs work by taking inputs from tensors (multidimensional matrices) to quantify multidirectional patterns, which allow neighbouring pixels to influence identifications (Caspari & Crespo, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Mask R‐CNN models to extract terracing across oceanic high islands: A case study from Sāmoa

Quintus

Davis

Cochrane

2023

Archaeological Prospection

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Lidar datasets have been crucial for documenting the scale and nature of human ecosystem engineering and land use. Automated analysis methods, which have been rising in popularity and efficiency, allow for systematic evaluations of vast landscapes. Here, we use a Mask R‐CNN deep learning model to evaluate terracing—artificially flattened areas surrounded by steeper slopes—on islands in American Sāmoa. Mask R‐CNN is notable for its ability to simultaneously perform detection and segmentation tasks related to object recognition, thereby providing robust datasets of both geographic locations of terracing features and their spatial morphometry. Using training datasets from across American Sāmoa, we train this model to recognize terracing features and then apply it to the island of Tutuila to undertake an island‐wide survey for terrace locations, distributions and morphologies. We demonstrate that this model is effective (F1 = 0.718), but limitations are also documented that relate to the quality of the lidar data and the size of terracing features. Our data show that the islands of American Sāmoa display shared patterns of terracing, but the nature of these patterns are distinct on Tutuila compared with the Manu'a island group. These patterns speak to the different interior configurations of the islands. This study demonstrates how deep learning provides a better understanding of landscape construction and behavioural patterning on Tutuila and has the capacity to expand our understanding of these processes on other islands beyond our case study.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating Mask R‐CNN models to extract terracing across oceanic high islands: A case study from Sāmoa

Quintus

Davis

Cochrane

2023

Archaeological Prospection

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“…This is essential in places where archaeological sites are difficult to access [127] . Two artificial intelligence approaches are introduced [128] over two areas of interest in the image processing field. They implemented a random forest classifier in their paper using the cloud platform of the Google Earth Engine data and a Single Shot Detector neural network is developed too.…”

Section: Archaeology Applicationsmentioning

confidence: 99%

Deep Learning Methods Used in Remote Sensing Images: A Review

Rewehel¹,

Li²,

Hamed³

et al. 2023

j. of environ. & earth. sci.

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Undeniably, Deep Learning (DL) has rapidly eroded traditional machine learning in Remote Sensing (RS) and geoscience domains with applications such as scene understanding, material identification, extreme weather detection, oil spill identification, among many others. Traditional machine learning algorithms are given less and less attention in the era of big data. Recently, a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer vision. The number of relevant articles has nearly doubled every year since 2015. Advances in remote sensing technology, as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale, have opened up the possibilities for a wide range of modern applications. However, there are some challenges related to the availability of annotated data, the complex nature of data, and model parameterization, which strongly impact performance. In this article, a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures (vintage Convolutional Neural Network, CNN; Fully Convolutional Networks, FCN; encoder-decoder, recurrent networks; attention models, and generative adversarial models). The characteristics, capabilities, and limitations of current DL models were examined, and potential research directions were discussed.

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“…It has experimented with geospatial data/images (satellite, aerial, lidar), texts, categorical tableau data, point clouds, and other datasets. For instance, one can consider some indicative examples such as the work that has been done on bone classification [1], remote sensing archaeology [2][3][4][5][6][7][8][9][10][11][12], geophysical prospection [13][14][15][16][17], detection of objects in paintings [18], classification of pottery [19], and the 3D reconstruction of heritage buildings [20]. The main reason behind this growing trend, which has been noticed in the last five years in all scientific domains, underlies the nuisance generated when dealing with multivariate analysis of high-volume datasets, which are challenging to process and interpret.…”

Section: Introductionmentioning

confidence: 99%

GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches: Future Perspectives for Archaeological Prospection

Küçükdemirci

Sarris

2022

Remote Sensing

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Ground penetrating radar (GPR) is a well-established technique used in archaeological prospection and it requires a number of specialized routines for signal and image processing to enhance the data acquired and lead towards a better interpretation of them. Computer-aided techniques have advanced the interpretation of GPR data, dealing with a wide range of operations aiming towards locating, imaging, and diagnosis/interpretation. This article will discuss the novel and recent applications of machine learning (ML) and deep learning (DL) techniques, under the artificial intelligence umbrella, for processing GPR measurements within archaeological contexts, and their potential, limitations, and possible future prospects.

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Detection of Archaeological Surface Ceramics Using Deep Learning Image-Based Methods and Very High-Resolution UAV Imageries

Cited by 11 publications

References 40 publications

Evaluating Mask R‐CNN models to extract terracing across oceanic high islands: A case study from Sāmoa

Evaluating Mask R‐CNN models to extract terracing across oceanic high islands: A case study from Sāmoa

Deep Learning Methods Used in Remote Sensing Images: A Review

GPR Data Processing and Interpretation Based on Artificial Intelligence Approaches: Future Perspectives for Archaeological Prospection

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