Understanding Historical Cityscapes from Aerial Imagery Through Machine Learning

Maltezos, Evangelos; Protopapadakis, Eftychios; Doulamis, Nikolaos; Ioannidis, Charalabos

doi:10.1007/978-3-030-01762-0_17

“…Driven in part by the lack of success of what may be termed 'conventional computer vision' approaches on the one hand and the groundbreaking achievements of deep-learning-based methods on the other, much like other recent cultural-heritage-focused computer science work [34][35][36], more recent efforts in automatic ancient coin analysis have turned their attention to the use of neural networks. Thus, Schlag and Arandjelović [18] proposed a VGG16 deep-neural-network-based algorithm for issuing authority recognition, and demonstrated outstanding performance on three large corpora of data.…”

Section: Research Effort To Datementioning

confidence: 99%

Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field

Arandjelović

¹

,

Zachariou

²

2020

Sci

View full text Add to dashboard Cite

Automatic ancient Roman coin analysis only recently emerged as a topic of computer science research. Nevertheless, owing to its ever-increasing popularity, the field is already reaching a certain degree of maturity, as witnessed by a substantial publication output in the last decade. At the same time, it is becoming evident that research progress is being limited by a somewhat veering direction of effort and the lack of a coherent framework which facilitates the acquisition and dissemination of robust, repeatable, and rigorous evidence. Thus, in the present article, we seek to address several associated challenges. To start with, (i) we provide a first overview and discussion of different challenges in the field, some of which have been scarcely investigated to date, and others which have hitherto been unrecognized and unaddressed. Secondly, (ii) we introduce the first data set, carefully curated and collected for the purpose of facilitating methodological evaluation of algorithms and, specifically, the effects of coin preservation grades on the performance of automatic methods. Indeed, until now, only one published work at all recognized the need for this kind of analysis, which, to any numismatist, would be a trivially obvious fact. We also discuss a wide range of considerations which had to be taken into account in collecting this corpus, explain our decisions, and describe its content in detail. Briefly, the data set comprises 100 different coin issues, all with multiple examples in Fine, Very Fine, and Extremely Fine conditions, giving a total of over 650 different specimens. These correspond to 44 issuing authorities and span the time period of approximately 300 years (from 27 BC until 244 AD). In summary, the present article should be an invaluable resource to researchers in the field, and we encourage the community to adopt the collected corpus, freely available for research purposes, as a standard evaluation benchmark.

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“…Nevertheless, though significantly surpassing the performance of the existing method at the time, even this method failed to demonstrate practically useful matching rates. Driven in part by the lack of success of what may be termed 'conventional computer vision' approaches on the one hand and the groundbreaking achievements of deep-learning-based methods on the other, much like other recent cultural-heritage-focused computer science work [33][34][35], more recent efforts in automatic ancient coin analysis have turned their attention to the use of neural networks. Thus, Schlag and Arandjelović [17] proposed a VGG16 deep-neural-network-based algorithm for issuing authority recognition, and demonstrated outstanding performance on three large corpora of data.…”

Section: Research Effort To Datementioning

confidence: 99%

Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field

Arandjelović¹,

Zachariou²

2020

Sci

View full text Add to dashboard Cite

Automatic ancient Roman coin analysis only recently emerged as a topic of computer science research. Nevertheless, owing to its ever-increasing popularity, the field is already reaching a certain degree of maturity, as witnessed by a substantial publication output in the last decade. At the same time, it is becoming evident that research progress is being limited by a somewhat veering direction of effort and the lack of a coherent framework which facilitates the acquisition and dissemination of robust, repeatable, and rigorous evidence. Thus, in the present article, we seek to address several associated challenges. To start with, (i) we provide a first overview and discussion of different challenges in the field, some of which have been scarcely investigated to date, and others which have hitherto been unrecognized and unaddressed. Secondly, (ii) we introduce the first data set, carefully curated and collected for the purpose of facilitating methodological evaluation of algorithms and, specifically, the effects of coin preservation grades on the performance of automatic methods. Indeed, until now, only one published work at all recognized the need for this kind of analysis, which, to any numismatist, would be a trivially obvious fact. We also discuss a wide range of considerations which had to be taken into account in collecting this corpus, explain our decisions, and describe its content in detail. Briefly, the data set comprises 100 different coin issues, all with multiple examples in Fine, Very Fine, and Extremely Fine conditions, giving a total of over 650 different specimens. These correspond to 44 issuing authorities and span the time period of approximately 300 years (from 27 BC until 244 AD). In summary, the present article should be an invaluable resource to researchers in the field, and we encourage the community to adopt the collected corpus, freely available for research purposes, as a standard evaluation benchmark.

show abstract

“…Driven in part by the lack of success of what may be termed 'conventional computer vision' approaches on the one hand and the groundbreaking achievements of deep-learning-based methods on the other, much like other recent cultural-heritage-focused computer science work [32][33][34], more recent efforts in automatic ancient coin analysis have turned their attention to the use of neural networks. Thus, Schlag and Arandjelović [17] proposed a VGG16 deep-neural-network-based algorithm for issuing authority recognition, and demonstrated outstanding performance on three large corpora of data.…”

Section: Research Effort To Datementioning

confidence: 99%

Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field

Arandjelović¹,

Zachariou²

2020

Sci

2

0

View full text Add to dashboard Cite

Automatic ancient Roman coin analysis only recently emerged as a topic of computer science research. Nevertheless, owing to its ever-increasing popularity, the field is already reaching a certain degree of maturity, as witnessed by a substantial publication output in the last decade. At the same time, it is becoming evident that research progress is being limited by a somewhat veering direction of effort and the lack of a coherent framework which facilitates the acquisition and dissemination of robust, repeatable, and rigorous evidence. Thus, in the present article, we seek to address several associated challenges. To start with, (i) we provide a first overview and discussion of different challenges in the field, some of which have been scarcely investigated to date, and others which have hitherto been unrecognized and unaddressed. Secondly, (ii) we introduce the first data set, carefully curated and collected for the purpose of facilitating methodological evaluation of algorithms and, specifically, the effects of coin preservation grades on the performance of automatic methods. Indeed, until now, only one published work at all recognized the need for this kind of analysis, which, to any numismatist, would be a trivially obvious fact. We also discuss a wide range of considerations which had to be taken into account in collecting this corpus, explain our decisions, and describe its content in detail. Briefly, the data set comprises 100 different coin types, all with multiple examples in Fine, Very Fine, and Extremely Fine conditions, giving a total of over 650 different specimens. These correspond to 44 issuing authorities and span the time period of approximately 300 years (from 27 BC until 244 AD). In summary, the present article should be an invaluable resource to researchers in the field, and we encourage the community to adopt the collected corpus, freely available for research purposes, as a standard evaluation benchmark.

show abstract

Understanding Historical Cityscapes from Aerial Imagery Through Machine Learning

Cited by 13 publications

References 21 publications

Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field

Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field

Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field

Images of Roman Imperial Denarii: A Curated Data Set for the Evaluation of Computer Vision Algorithms Applied to Ancient Numismatics, and an Overview of Challenges in the Field

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