The present study proposes a new software program to help researchers identify rock paintings fro... more The present study proposes a new software program to help researchers identify rock paintings from digital images, rapidly producing high-quality documentation, in a user-friendly way. The three RGB colour channels of the digital image are first decorrelated and then stretched, a well-known technique used by remote-sensing specialists for over thirty years. In contrast with the approaches previously developed specifically for rock art, several data-whitening algorithms are used at this step: (regular) principal component analysis, zero-phase component analysis, Cholesky decomposition, and independent component analysis. These transformations produce different arrangements of the colour information, which nevertheless share some important properties (e.g. the covariance matrix of the new channels equals the identity matrix). The decorrelated data, previously stretched and scaled to fit the RGB space, are then converted into various colour spaces (selected from among the most popular): XYZ, HLS, HSV, LAB (CIELAB), Luv, CMY(K), YCrCb, and YUV. The most subtle colour variations will be better perceived in some of these newly produced, contrasted, false-coloured images. The researcher can then take advantage of supervised machine learning algorithms to isolate painted figures. At this step, binary pixel classification is performed either by logistic regression, support vector machine, or k-nearest neighbours, possibly including confident learning. There is no need for complex tuning at any point during the procedure, which lasts a few minutes at most, while a posteriori cleaning of the produced document is minimal. The software, written in Python, is provided as a stand-alone executable program for Windows, for broader diffusion, and as open-source code, which can therefore be adapted to the evolving needs of the community.
The use of digital elevation models (DEMs) has become much more widespread in recent years, thank... more The use of digital elevation models (DEMs) has become much more widespread in recent years, thanks to technological developments that facilitate their creation and availability. To exploit these data, a set of processing techniques has been developed to reveal the characteristic structures of the relief. This paper presents a new method based on the volumetric approach, and two derivatives. These methods are evaluated on three DEMs at different resolutions and scales: a freely accessible DEM from JAXA DEM covering part of North-East Tanzania, a DEM corresponding to rock art in Siberia, and a DEM of an archaeological Bronze Age funeral structure. Our results show that with the volumetric approach, concave and convex areas are clearly visible, with contrast marking slope breaks, while the overall relief is attenuated. Furthermore, the use of volume reduces the impact of noise, which can occur when processing is based on sky visibility (e.g., sky-view factor or positive openness) or se...
Revealing carved parts in rock art is of primary importance and remains a major challenge for arc... more Revealing carved parts in rock art is of primary importance and remains a major challenge for archaeological documentation. Computational geometry applied to 3D imaging provides a unique opportunity to document rock art. This study evaluates five algorithms and derivatives used to compute ambient occlusion and sky visibility on 3D models of Mongolian stelae, also known as deer stones. By contrast with the previous companion work, models are processed directly in 3D, without preliminary projection. Volumetric obscurance gives the best results for the identification of carved figures. The effects of model resolution and parameters specific to ambient occlusion are then evaluated. Keeping tridimensional information intact allows accurate measurement of distance, volume, and depth. Objects augmented by ambient occlusion can easily be manipulated in 3D viewers, for seamless and effortless access to the overall organization of the figures, at the scale of the entire object. Qualitatively speaking, the 2D projected outputs are equivalent to, or even better than, existing archaeological documentation. The proposed workflow should be easily applicable in many situations, particularly as the functions provided for the free R programming software are fully configurable.
In Sumba Island –Indonesia, the implantation of vernacular houses, inside and outside traditional... more In Sumba Island –Indonesia, the implantation of vernacular houses, inside and outside traditional vil- lages, is considered to be an efficient proxy for the on-going complex cultural transformations resulting from globalization. This study presents an easily reproducible workflow allowing buildings to be auto- matically detected from satellite imagery, demonstrating how modern computer vision methods based on deep learning can help in this task, which would be far too time-consuming when undertaken by hand. Eight deep learning architectures based on convolutional neural networks were compared in terms of ability to identify and locate precisely traditional houses from satellite images. By combining a Faster R-CNN ResNet 101 architecture with artificial data augmentation, the model was taught properly using 1033 instances of vernacular houses ([email protected]:.95 = 71.9). Once 14 952 traditional houses had been de- tected, the Histogram of Orientated Gradients (HOG) was computed and processed by several machine learning algorithms to assess their surface area, as this parameter conveys pertinent information about the economic and political position of the householder. The best classifier was found to be a support vec- tor regressor (SVR, R 2 = 0.88), although the other classifiers tested also provided good results (R 2 > 0.76). Spatial analysis was used to draw conclusions from an anthropological / cultural identity point of view. More generally, these techniques not only offer a simple increase in recording capabilities for tangible cultural heritage, they open up new research perspectives, at greater scales.
The present study proposes a workflow to extract from orthomosaics the enormous amount of dry sto... more The present study proposes a workflow to extract from orthomosaics the enormous amount of dry stones used by past societies to construct funeral complexes in the Mongolian steppes. Several different machine learning algorithms for binary pixel classification (i.e. stone vs non-stone) were evaluated. Input features were extracted from high-resolution orthomosaics and digital elevation models (both derived from aerial imaging). Comparative analysis used two colour spaces (RGB and HSV), texture features (contrast, homogeneity and entropy raster maps), and the topographic position index, combined with nine supervised learning algorithms (nearest centroid, naive Bayes, k-nearest neighbours, logistic regression, linear and quadratic discriminant analyses, support vector machine, random forest, and artificial neural network). When features are processed together, excellent output maps, very close to or outperforming current standards in archaeology, are observed for almost all classifiers. The size of the training set can be drastically reduced (to ca. 300 samples) by majority voting, while maintaining performance at the highest level (about 99.5% for all performance scores). Note, however, that if the training set is inadequate or not fully representative, the classification results are poor. That said, the methods applied and tested here are extremely rapid. Extensive mapping, which would have been difficult with traditional, manual, or semiautomatic delineation of stones using a vector graphics editor, now becomes possible. This workflow generally surpasses pedestrian surveys using differential GPS or a total station.
The Anza site shows large ichnological surfaces indicating the coexistence in the same area of di... more The Anza site shows large ichnological surfaces indicating the coexistence in the same area of different vertebrate footprints (dinosaur and pterosaur) and of different types (tridactyl and tetradactyl, semiplantigrade and rounded without digit marks) and the footprint variability of long trackways. This area may become a world reference in ichnology because it contains the second undebatable African site with Cretaceous pterosaur footprints-described in part I-and the first African site with Macropodosaurus footprints. In this work, problems related to long trackways are also analyzed, such as their sinuosity, the order-disorder of the variability (long-short) of the pace length and the difficulty of morphological classification of the theropod footprints due to their morphological variability.
Photospheres, or 360° photos, offer valuable opportunities for perceiving space, especially when ... more Photospheres, or 360° photos, offer valuable opportunities for perceiving space, especially when viewed through head-mounted displays designed for virtual reality. Here, we propose to take advantage of this potential for archaeology and cultural heritage, and to extend it by augmenting the images with existing documentation, such as 2D maps or 3D models, resulting from research studies. Photospheres are generally produced in the form of distorted equirectangular projections, neither georeferenced nor oriented, so that any registration of external documentation is far from straightforward. The present paper seeks to fill this gap by providing simple practical solutions, based on rigid and non-rigid transformations. Immersive virtual environments augmented by research materials can be very useful to contextualize archaeological discoveries, and to test research hypotheses, especially when the team is back at the laboratory. Colleagues and the general public can also be transported to ...
The present study proposes a new software program to help researchers identify rock paintings fro... more The present study proposes a new software program to help researchers identify rock paintings from digital images, rapidly producing high-quality documentation, in a user-friendly way. The three RGB colour channels of the digital image are first decorrelated and then stretched, a well-known technique used by remote-sensing specialists for over thirty years. In contrast with the approaches previously developed specifically for rock art, several data-whitening algorithms are used at this step: (regular) principal component analysis, zero-phase component analysis, Cholesky decomposition, and independent component analysis. These transformations produce different arrangements of the colour information, which nevertheless share some important properties (e.g. the covariance matrix of the new channels equals the identity matrix). The decorrelated data, previously stretched and scaled to fit the RGB space, are then converted into various colour spaces (selected from among the most popular): XYZ, HLS, HSV, LAB (CIELAB), Luv, CMY(K), YCrCb, and YUV. The most subtle colour variations will be better perceived in some of these newly produced, contrasted, false-coloured images. The researcher can then take advantage of supervised machine learning algorithms to isolate painted figures. At this step, binary pixel classification is performed either by logistic regression, support vector machine, or k-nearest neighbours, possibly including confident learning. There is no need for complex tuning at any point during the procedure, which lasts a few minutes at most, while a posteriori cleaning of the produced document is minimal. The software, written in Python, is provided as a stand-alone executable program for Windows, for broader diffusion, and as open-source code, which can therefore be adapted to the evolving needs of the community.
The use of digital elevation models (DEMs) has become much more widespread in recent years, thank... more The use of digital elevation models (DEMs) has become much more widespread in recent years, thanks to technological developments that facilitate their creation and availability. To exploit these data, a set of processing techniques has been developed to reveal the characteristic structures of the relief. This paper presents a new method based on the volumetric approach, and two derivatives. These methods are evaluated on three DEMs at different resolutions and scales: a freely accessible DEM from JAXA DEM covering part of North-East Tanzania, a DEM corresponding to rock art in Siberia, and a DEM of an archaeological Bronze Age funeral structure. Our results show that with the volumetric approach, concave and convex areas are clearly visible, with contrast marking slope breaks, while the overall relief is attenuated. Furthermore, the use of volume reduces the impact of noise, which can occur when processing is based on sky visibility (e.g., sky-view factor or positive openness) or se...
Revealing carved parts in rock art is of primary importance and remains a major challenge for arc... more Revealing carved parts in rock art is of primary importance and remains a major challenge for archaeological documentation. Computational geometry applied to 3D imaging provides a unique opportunity to document rock art. This study evaluates five algorithms and derivatives used to compute ambient occlusion and sky visibility on 3D models of Mongolian stelae, also known as deer stones. By contrast with the previous companion work, models are processed directly in 3D, without preliminary projection. Volumetric obscurance gives the best results for the identification of carved figures. The effects of model resolution and parameters specific to ambient occlusion are then evaluated. Keeping tridimensional information intact allows accurate measurement of distance, volume, and depth. Objects augmented by ambient occlusion can easily be manipulated in 3D viewers, for seamless and effortless access to the overall organization of the figures, at the scale of the entire object. Qualitatively speaking, the 2D projected outputs are equivalent to, or even better than, existing archaeological documentation. The proposed workflow should be easily applicable in many situations, particularly as the functions provided for the free R programming software are fully configurable.
In Sumba Island –Indonesia, the implantation of vernacular houses, inside and outside traditional... more In Sumba Island –Indonesia, the implantation of vernacular houses, inside and outside traditional vil- lages, is considered to be an efficient proxy for the on-going complex cultural transformations resulting from globalization. This study presents an easily reproducible workflow allowing buildings to be auto- matically detected from satellite imagery, demonstrating how modern computer vision methods based on deep learning can help in this task, which would be far too time-consuming when undertaken by hand. Eight deep learning architectures based on convolutional neural networks were compared in terms of ability to identify and locate precisely traditional houses from satellite images. By combining a Faster R-CNN ResNet 101 architecture with artificial data augmentation, the model was taught properly using 1033 instances of vernacular houses ([email protected]:.95 = 71.9). Once 14 952 traditional houses had been de- tected, the Histogram of Orientated Gradients (HOG) was computed and processed by several machine learning algorithms to assess their surface area, as this parameter conveys pertinent information about the economic and political position of the householder. The best classifier was found to be a support vec- tor regressor (SVR, R 2 = 0.88), although the other classifiers tested also provided good results (R 2 > 0.76). Spatial analysis was used to draw conclusions from an anthropological / cultural identity point of view. More generally, these techniques not only offer a simple increase in recording capabilities for tangible cultural heritage, they open up new research perspectives, at greater scales.
The present study proposes a workflow to extract from orthomosaics the enormous amount of dry sto... more The present study proposes a workflow to extract from orthomosaics the enormous amount of dry stones used by past societies to construct funeral complexes in the Mongolian steppes. Several different machine learning algorithms for binary pixel classification (i.e. stone vs non-stone) were evaluated. Input features were extracted from high-resolution orthomosaics and digital elevation models (both derived from aerial imaging). Comparative analysis used two colour spaces (RGB and HSV), texture features (contrast, homogeneity and entropy raster maps), and the topographic position index, combined with nine supervised learning algorithms (nearest centroid, naive Bayes, k-nearest neighbours, logistic regression, linear and quadratic discriminant analyses, support vector machine, random forest, and artificial neural network). When features are processed together, excellent output maps, very close to or outperforming current standards in archaeology, are observed for almost all classifiers. The size of the training set can be drastically reduced (to ca. 300 samples) by majority voting, while maintaining performance at the highest level (about 99.5% for all performance scores). Note, however, that if the training set is inadequate or not fully representative, the classification results are poor. That said, the methods applied and tested here are extremely rapid. Extensive mapping, which would have been difficult with traditional, manual, or semiautomatic delineation of stones using a vector graphics editor, now becomes possible. This workflow generally surpasses pedestrian surveys using differential GPS or a total station.
The Anza site shows large ichnological surfaces indicating the coexistence in the same area of di... more The Anza site shows large ichnological surfaces indicating the coexistence in the same area of different vertebrate footprints (dinosaur and pterosaur) and of different types (tridactyl and tetradactyl, semiplantigrade and rounded without digit marks) and the footprint variability of long trackways. This area may become a world reference in ichnology because it contains the second undebatable African site with Cretaceous pterosaur footprints-described in part I-and the first African site with Macropodosaurus footprints. In this work, problems related to long trackways are also analyzed, such as their sinuosity, the order-disorder of the variability (long-short) of the pace length and the difficulty of morphological classification of the theropod footprints due to their morphological variability.
Photospheres, or 360° photos, offer valuable opportunities for perceiving space, especially when ... more Photospheres, or 360° photos, offer valuable opportunities for perceiving space, especially when viewed through head-mounted displays designed for virtual reality. Here, we propose to take advantage of this potential for archaeology and cultural heritage, and to extend it by augmenting the images with existing documentation, such as 2D maps or 3D models, resulting from research studies. Photospheres are generally produced in the form of distorted equirectangular projections, neither georeferenced nor oriented, so that any registration of external documentation is far from straightforward. The present paper seeks to fill this gap by providing simple practical solutions, based on rigid and non-rigid transformations. Immersive virtual environments augmented by research materials can be very useful to contextualize archaeological discoveries, and to test research hypotheses, especially when the team is back at the laboratory. Colleagues and the general public can also be transported to ...
Uploads
Talks by tanguy rolland
Papers by tanguy rolland