A Critical Review of Remote Sensing Approaches and Deep Learning Techniques in Archaeology

Kadhim, Israa; Abed, Fanar M.

doi:10.3390/s23062918

Cited by 6 publications

(5 citation statements)

References 73 publications

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“…Two others (3%) present critical assessments of ML without the authors having undertaken it themselves (Casini et al, 2021;Casana, 2020). Two (of four) review articles included critiques, one of which recognised specific challenges but generally reflected the positive tone of the literature (Argyrou and Agapiou, 2022), while the other offered a more sustained discussion of challenges and possible solutions (Kadhim and Abed, 2023). The overwhelmingly positive tone of these papers likely indicates a certain degree of "publication bias", where positive results are more likely to be published than negative (Brown et al, 2017;Dickersin et al, 1987;Harrison et al, 2017;Ioannidis, 2005;K€ uhberger et al, 2014;Møller and Jennions, 2001), or at the very least a reflection of the rhetorical shift in scientific research towards less qualified or uncertain presentation of outcomes (Vinkers et al, 2015;Wheeler et al, 2021;Yao et al, 2023;Yuan and Yao, 2022).…”

Section: Automated Approaches To Remotely Sensed Datamentioning

confidence: 99%

“…Rather than training our own model from scratch, we used a pre-trained CNN, a technique known as transfer learning (Casini et al, 2022;Character et al, 2021;Gallwey et al, 2019; see also Kadhim and Abed, 2023;Pan and Yang, 2010;Sech et al, 2023; for an overview see Weiss et al, 2016;Xiong et al, 2020). Transfer learning assumes that large, complex models can be pre-trained using data from one domain, then fine-tuned for a specific task in another domain.…”

Section: Transfer Learningmentioning

confidence: 99%

“…During initial experiments, the model was found to overfit the training data, reporting close to 100% accuracy after only a few training epochs. Overfitting in such an extreme manner results in a model that does not generalise well to new data (Kadhim and Abed, 2023), meaning that such a classifier would not be useful. In order to counter this overfitting, we used proven data augmentation techniques to constrain model performance (Wang et al, 2017).…”

Section: Transfer Learningmentioning

confidence: 99%

“…Projects applying ML approaches to satellite imagery or LiDAR data likewise report a lack of high-quality, well-annotated training data, often due to cost and time constraints (Albrecht et al, 2019;Argyrou and Agapiou, 2022;Canedo et al, 2023;Casini et al, 2021;Gallwey et al, 2019;Kadhim and Abed, 2023;Karamitrou et al, 2022Karamitrou et al, , 2023Lambers et al, 2019;Ofli et al, 2016;Sech et al, 2023;Verschoof-van der Vaart and Landauer, 2021). Transfer learning based on pre-trained models is sometimes proposed as solution to the problem of limited training data, as well as related problems like small dataset size (Casini et al, 2021(Casini et al, , 2022Character et al, 2021;Gallwey et al, 2019;Sech et al, 2023;Xiong et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Validating predictions of burial mounds with field data: the promise and reality of machine learning

Sobotkova,

Kristensen-McLachlan,

Mallon

et al. 2024

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PurposeThis paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.Design/methodology/approachAutomated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.FindingsValidation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.Research limitations/implicationsOur attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.Practical implicationsImproving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.Social implicationsOur literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.Originality/valueUnlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

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Section: Automated Approaches To Remotely Sensed Datamentioning

confidence: 99%

Section: Transfer Learningmentioning

confidence: 99%

Section: Transfer Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validating predictions of burial mounds with field data: the promise and reality of machine learning

Sobotkova,

Kristensen-McLachlan,

Mallon

et al. 2024

View full text Add to dashboard Cite

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“…The advancement of hardware has dramatically contributed to the development of deep learning in 3D vision [7,8], leading to a shift from traditional hand-crafted features [9][10][11] to learning-based methods in place recognition. These learning-based architectures can be broadly categorized into point-based and voxel-based methods based on their data representations.…”

Section: Introductionmentioning

confidence: 99%

LWR-Net: Robust and Lightweight Place Recognition Network for Noisy and Low-Density Point Clouds

Zhang,

Chen,

Shu

et al. 2023

Sensors

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Point cloud-based retrieval for place recognition is essential in robotic applications like autonomous driving or simultaneous localization and mapping. However, this remains challenging in complex real-world scenes. Existing methods are sensitive to noisy, low-density point clouds and require extensive storage and computation, posing limitations for hardware-limited scenarios. To overcome these challenges, we propose LWR-Net, a lightweight place recognition network for efficient and robust point cloud retrieval in noisy, low-density conditions. Our approach incorporates a fast dilated sampling and grouping module with a residual MLP structure to learn geometric features from local neighborhoods. We also introduce a lightweight attentional weighting module to enhance global feature representation. By utilizing the Generalized Mean pooling structure, we aggregated the global descriptor for point cloud retrieval. We validated LWR-Net’s efficiency and robustness on the Oxford robotcar dataset and three in-house datasets. The results demonstrate that our method efficiently and accurately retrieves matching scenes while being more robust to variations in point density and noise intensity. LWR-Net achieves state-of-the-art accuracy and robustness with a lightweight model size of 0.4M parameters. These efficiency, robustness, and lightweight advantages make our network highly suitable for robotic applications relying on point cloud-based place recognition.

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Convolutional neural networks for accurate identification of mining remains from UAV-derived images

Fernández-Alonso,

Fernández-Lozano,

García-Ordás

2023

Appl Intell

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A new deep learning system is proposed for the rapid and accurate identification of anthropogenic elements of the Roman mining infrastructure in NW Iberia, providing a new approach for automatic recognition of different mining elements without the need for human intervention or implicit subjectivity. The recognition of archaeological and other abandoned mining elements provides an optimal test case for decision-making and management in a broad variety of research fields. A new image dataset was created by obtaining UAV images from different anthropic features. A convolutional neural network architecture was implemented, achieving recognition results of close to 95% accuracy. This methodological approach is suitable for the identification and accurate location of ancient mines and hydrologic infrastructure, providing new tools for accurate mapping of mining landforms. Additionally, this novel application of deep learning can be implemented to reduce potential risks caused by abandoned mines, which can cause significant annual human and economic losses worldwide.

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A Critical Review of Remote Sensing Approaches and Deep Learning Techniques in Archaeology

Cited by 6 publications

References 73 publications

Validating predictions of burial mounds with field data: the promise and reality of machine learning

Validating predictions of burial mounds with field data: the promise and reality of machine learning

LWR-Net: Robust and Lightweight Place Recognition Network for Noisy and Low-Density Point Clouds

Convolutional neural networks for accurate identification of mining remains from UAV-derived images

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