The present work proposes an innovative approach to surveys and demonstrates the effectiveness of bringing together traditional archaeological questions, such as the exploration and the analysis of settlement patterns, with the most innovative technologies related to Machine Learning. Namely, we applied Random Forest, an ensemble learning method based on decision trees, to perform archaeological predictive modeling (APM) for the Canton of Zurich, in Switzerland. This was done based on a dataset of known archaeological sites dating back to the Roman Age. The APM represents an automated decision-making and probabilistic reasoning tool that is relevant for archaeological risk assessment and cultural heritage management. Machine learning-based approaches can learn from data and make predictions, starting from the acquired knowledge, through the modeling of the hidden relationships between a set of observations, representing the dependent variable (i.e. the archeological sites), and the independent variables (i.e. the geo-environmental features prone to influence the site locations). The main objective of the present study is to assess the spatial probability of presence for Roman settlements within the study area. As results, we produced: 1) a probability map, expressing the likelihood of finding a Roman site at different locations; 2) the importance ranking of the geo-environmental features influencing the presence of the archeological sites. These outputs in our results are of paramount importance, not only in verifying the reliability of the data, but also in stimulating experts in different ways. Also, these results help evaluate the benefits and constraints of using such innovative techniques and, ultimately, help explore the performance of machine learning-based models in processing archaeological information.
{"title":"An Explorative Application of Random Forest Algorithm for Archaeological Predictive Modeling. A Swiss Case Study","authors":"M. Castiello, M. Tonini","doi":"10.5334/JCAA.71","DOIUrl":"https://doi.org/10.5334/JCAA.71","url":null,"abstract":"The present work proposes an innovative approach to surveys and demonstrates the effectiveness of bringing together traditional archaeological questions, such as the exploration and the analysis of settlement patterns, with the most innovative technologies related to Machine Learning. Namely, we applied Random Forest, an ensemble learning method based on decision trees, to perform archaeological predictive modeling (APM) for the Canton of Zurich, in Switzerland. This was done based on a dataset of known archaeological sites dating back to the Roman Age. The APM represents an automated decision-making and probabilistic reasoning tool that is relevant for archaeological risk assessment and cultural heritage management. Machine learning-based approaches can learn from data and make predictions, starting from the acquired knowledge, through the modeling of the hidden relationships between a set of observations, representing the dependent variable (i.e. the archeological sites), and the independent variables (i.e. the geo-environmental features prone to influence the site locations). The main objective of the present study is to assess the spatial probability of presence for Roman settlements within the study area. As results, we produced: 1) a probability map, expressing the likelihood of finding a Roman site at different locations; 2) the importance ranking of the geo-environmental features influencing the presence of the archeological sites. These outputs in our results are of paramount importance, not only in verifying the reliability of the data, but also in stimulating experts in different ways. Also, these results help evaluate the benefits and constraints of using such innovative techniques and, ultimately, help explore the performance of machine learning-based models in processing archaeological information.","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46211956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automated remote sensing has made substantial breakthroughs for archaeological investigation. Over the past 20 years, the reliability of these methods has vastly improved, and the total number of practitioners has been increasing. Nonetheless, much of the work conducted, to date, focuses almost exclusively on specific topographic features and monumental architecture, ignoring the potential of automation to readily assess more ephemeral components of the archaeological record. Likewise, the emphasis on specific feature types overlooks broader landscape patterns, thus delegating automated remote sensing analysis as a method in and of itself, mostly disconnected from larger archaeological and anthropological investigations. Here, I review recent attempts to rectify this shortcoming by using automated analysis methods to record and explain ephemeral archaeological material distributions. While such research is limited, I argue that the successes achieved in these recent studies offer a pathway forward for automated remote sensing to become more fully integrated with archaeological work beyond the detection of specific topographically distinct features.
{"title":"Theoretical Repositioning of Automated Remote Sensing Archaeology: Shifting from Features to Ephemeral Landscapes","authors":"Dylan S. Davis","doi":"10.5334/JCAA.72","DOIUrl":"https://doi.org/10.5334/JCAA.72","url":null,"abstract":"Automated remote sensing has made substantial breakthroughs for archaeological investigation. Over the past 20 years, the reliability of these methods has vastly improved, and the total number of practitioners has been increasing. Nonetheless, much of the work conducted, to date, focuses almost exclusively on specific topographic features and monumental architecture, ignoring the potential of automation to readily assess more ephemeral components of the archaeological record. Likewise, the emphasis on specific feature types overlooks broader landscape patterns, thus delegating automated remote sensing analysis as a method in and of itself, mostly disconnected from larger archaeological and anthropological investigations. Here, I review recent attempts to rectify this shortcoming by using automated analysis methods to record and explain ephemeral archaeological material distributions. While such research is limited, I argue that the successes achieved in these recent studies offer a pathway forward for automated remote sensing to become more fully integrated with archaeological work beyond the detection of specific topographically distinct features.","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44330508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The ever-increasing amount of remotely-sensed data pertaining to archaeology renders human-based analysis unfeasible, especially considering the expert knowledge required to correctly identify structures and objects in these type of data. Therefore, robust and reliable computer-based object detectors are needed, which can deal with the unique challenges of not only remotely-sensed data, but also of the archaeological detection task. In this research – across-domain collaboration between archaeology and computer science — the latest developments in object detection and Deep Learning — for both natural and satellite imagery — are used to develop an object detection approach, based on the YOLOv4 framework, and modified to the specific task of detecting archaeology in remotely-sensed LiDAR data from the Veluwe (the Netherlands). Experiments show that a general version of the YOLOv4 architecture outperforms current object detection workflows used in archaeology, while the modified version of YOLOv4, geared towards the archaeological task, reaches even higher performance. The research shows the potential and benefit of cross-domain collaboration, where expert knowledge from different research fields is used to create a more reliable detector. 275 Olivier and Verschoof-van der Vaart Journal of Computer Applications in Archaeology DOI: 10.5334/jcaa.78
与考古有关的遥感数据数量不断增加，使得以人为基础的分析变得不可行，特别是考虑到在这些类型的数据中正确识别结构和物体所需的专业知识。因此，需要一种鲁棒可靠的基于计算机的目标探测器，它不仅能应对遥感数据的独特挑战，而且能应对考古探测任务的独特挑战。在这项研究中——考古学和计算机科学之间的跨领域合作——物体检测和深度学习的最新发展——用于自然和卫星图像——用于开发一种基于YOLOv4框架的物体检测方法，并修改为在Veluwe(荷兰)的遥感激光雷达数据中检测考古的具体任务。实验表明，通用版本的YOLOv4架构优于当前考古学中使用的目标检测工作流，而针对考古任务的修改版本的YOLOv4达到了更高的性能。这项研究显示了跨领域合作的潜力和好处，来自不同研究领域的专家知识被用来创建更可靠的检测器。[5] Olivier and Verschoof-van der Vaart Journal in Archaeology . DOI: 10.5334/jcaa.78
{"title":"Implementing State-of-the-Art Deep Learning Approaches for Archaeological Object Detection in Remotely-Sensed Data: The Results of Cross-Domain Collaboration","authors":"Martin Olivier, Wouter B. Verschoof‐van der Vaart","doi":"10.5334/jcaa.78","DOIUrl":"https://doi.org/10.5334/jcaa.78","url":null,"abstract":"The ever-increasing amount of remotely-sensed data pertaining to archaeology renders human-based analysis unfeasible, especially considering the expert knowledge required to correctly identify structures and objects in these type of data. Therefore, robust and reliable computer-based object detectors are needed, which can deal with the unique challenges of not only remotely-sensed data, but also of the archaeological detection task. In this research – across-domain collaboration between archaeology and computer science — the latest developments in object detection and Deep Learning — for both natural and satellite imagery — are used to develop an object detection approach, based on the YOLOv4 framework, and modified to the specific task of detecting archaeology in remotely-sensed LiDAR data from the Veluwe (the Netherlands). Experiments show that a general version of the YOLOv4 architecture outperforms current object detection workflows used in archaeology, while the modified version of YOLOv4, geared towards the archaeological task, reaches even higher performance. The research shows the potential and benefit of cross-domain collaboration, where expert knowledge from different research fields is used to create a more reliable detector. 275 Olivier and Verschoof-van der Vaart Journal of Computer Applications in Archaeology DOI: 10.5334/jcaa.78","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70674256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Lithic Transport Patterns, Tool Curation Behavior, and Group Range Estimates: A Model-Based Exploration","authors":"A. White","doi":"10.5334/jcaa.82","DOIUrl":"https://doi.org/10.5334/jcaa.82","url":null,"abstract":"","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70674412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mapping Historical Archaeology and Industrial Heritage: The Historical Spatial Data Infrastructure","authors":"Daniel Trepal, Don Lafreniere, T. Stone","doi":"10.5334/jcaa.77","DOIUrl":"https://doi.org/10.5334/jcaa.77","url":null,"abstract":"","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70674523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper provides a summary of the percolation method of cluster analysis, and then describes two detailed archaeological case studies where the technique is applied at significantly different geographical scales. This demonstrates not only the potential for the technique within archaeology, but also how it can be applied at different spatial scales with different objectives appropriate to the specific problem in question. The technique, originally developed in physics and more recently adopted in geography, is a way of identifying groupings or clusters, purely based on spatial separation using Euclidian distance. The paper includes a discussion comparing percolation analysis with other clustering techniques already established within archaeology. The first case study uses percolation analysis as an exploratory tool for investigating the distribution of hillforts in Britain, identifying clusters and groupings at a national scale. The purpose is to identify possible socio-political entities for further investigation. In the second case study, the technique is applied to excavated features at a sub-regional level, in Saxony-Anhalt, central Germany, with the different objective of identifying settlement sites along a 13 km strip excavation. The aim here was to arrive at estimates of settlement sizes, which in turn can inform landscape archaeological surveys for deciding on the attribution of single finds to an already known site or registering a new site. Percolation analysis is shown to be an effective tool for analysing archaeological data sets at widely different geographical scales and should become a standard part of the archaeologist’s spatial analysis toolkit.
{"title":"Percolation Analysis – Archaeological Applications at Widely Different Spatial Scales","authors":"M. Maddison, Sophie C. Schmidt","doi":"10.5334/jcaa.54","DOIUrl":"https://doi.org/10.5334/jcaa.54","url":null,"abstract":"This paper provides a summary of the percolation method of cluster analysis, and then describes two detailed archaeological case studies where the technique is applied at significantly different geographical scales. This demonstrates not only the potential for the technique within archaeology, but also how it can be applied at different spatial scales with different objectives appropriate to the specific problem in question. The technique, originally developed in physics and more recently adopted in geography, is a way of identifying groupings or clusters, purely based on spatial separation using Euclidian distance. The paper includes a discussion comparing percolation analysis with other clustering techniques already established within archaeology. The first case study uses percolation analysis as an exploratory tool for investigating the distribution of hillforts in Britain, identifying clusters and groupings at a national scale. The purpose is to identify possible socio-political entities for further investigation. In the second case study, the technique is applied to excavated features at a sub-regional level, in Saxony-Anhalt, central Germany, with the different objective of identifying settlement sites along a 13 km strip excavation. The aim here was to arrive at estimates of settlement sizes, which in turn can inform landscape archaeological surveys for deciding on the attribution of single finds to an already known site or registering a new site. Percolation analysis is shown to be an effective tool for analysing archaeological data sets at widely different geographical scales and should become a standard part of the archaeologist’s spatial analysis toolkit.","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49632493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Digital Scholarship in Archaeology","authors":"","doi":"10.5334/jcaa.dsia","DOIUrl":"https://doi.org/10.5334/jcaa.dsia","url":null,"abstract":"","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47923438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
José Iriarte, M. Robinson, J. G. Souza, A. Damasceno, Franciele da Silva, Francisco Ruji Nakahara, A. Ranzi, L. Aragão
Recent research has shown that the entire southern rim of Amazonia was inhabited by earth-building societies involving landscape engineering, landscape domestication and likely low-density urbanism during the Late Holocene. However, the scale, timing, and intensity of human settlement in this region remain unknown due to the dearth of archaeological work and the logistical difficulties associated with research in tropical forest environments. A case in point are the newly discovered Mound Villages (AD ~1000–1650) in the SE portion of Acre State, Brazil. Much of recent pioneering work on this new archaeological tradition has mainly focused on the excavation of single mounds within sites with little concern for the architectural layout and regional settlement patterns, thus preventing us from understanding how these societies were organised at the regional level. To address these shortcomings, we carried out the first Lidar survey with a RIEGL VUX-1 UAV Lidar sensor integrated into an MD 500 helicopter. Our novel results documented distinctive architectural features of Circular Mound Villages such as the presence of ranked, paired, cardinally oriented, sunken roads interconnecting villages, the occurrence of a diversity of mound shapes within sites, as well as the exposure the superimposition of villages. Site size distribution analysis showed no apparent signs of settlement hierarchy. At the same time, it revealed that some small groups of villages positioned along streams exhibit regular distances of 2.5–3 km and 5–6 km between sites. Our data show that after the cessation of Geoglyph construction (~AD 950), this region of SW Amazonia was not abandoned, but occupied by a flourishing regional system of Mound Villages. The results continue to call into question traditional views that portray interfluvial areas and the western sector of Amazonia as sparsely inhabited. A brief discussion of our findings in the context with pre-Columbian settlement patterns across other regions of Amazonia is conducted.
{"title":"Geometry by Design: Contribution of Lidar to the Understanding of Settlement Patterns of the Mound Villages in SW Amazonia","authors":"José Iriarte, M. Robinson, J. G. Souza, A. Damasceno, Franciele da Silva, Francisco Ruji Nakahara, A. Ranzi, L. Aragão","doi":"10.5334/jcaa.45","DOIUrl":"https://doi.org/10.5334/jcaa.45","url":null,"abstract":"Recent research has shown that the entire southern rim of Amazonia was inhabited by earth-building societies involving landscape engineering, landscape domestication and likely low-density urbanism during the Late Holocene. However, the scale, timing, and intensity of human settlement in this region remain unknown due to the dearth of archaeological work and the logistical difficulties associated with research in tropical forest environments. A case in point are the newly discovered Mound Villages (AD ~1000–1650) in the SE portion of Acre State, Brazil. Much of recent pioneering work on this new archaeological tradition has mainly focused on the excavation of single mounds within sites with little concern for the architectural layout and regional settlement patterns, thus preventing us from understanding how these societies were organised at the regional level. To address these shortcomings, we carried out the first Lidar survey with a RIEGL VUX-1 UAV Lidar sensor integrated into an MD 500 helicopter. Our novel results documented distinctive architectural features of Circular Mound Villages such as the presence of ranked, paired, cardinally oriented, sunken roads interconnecting villages, the occurrence of a diversity of mound shapes within sites, as well as the exposure the superimposition of villages. Site size distribution analysis showed no apparent signs of settlement hierarchy. At the same time, it revealed that some small groups of villages positioned along streams exhibit regular distances of 2.5–3 km and 5–6 km between sites. Our data show that after the cessation of Geoglyph construction (~AD 950), this region of SW Amazonia was not abandoned, but occupied by a flourishing regional system of Mound Villages. The results continue to call into question traditional views that portray interfluvial areas and the western sector of Amazonia as sparsely inhabited. A brief discussion of our findings in the context with pre-Columbian settlement patterns across other regions of Amazonia is conducted.","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48746288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a response to Hurricane Mitch and the resulting widespread loss of life and destruction of Honduran infrastructure in 1998, the United States Geological Survey (USGS) conducted the first wide-area airborne lidar topographic mapping project in Central America. The survey was executed by the Bureau of Economic Geology at the University of Texas at Austin (BEG) in 2000, and it was intended to cover 240 square kilometers distributed among 15 flood-prone communities throughout Honduras. The original data processing produced basic digital elevation models at 1.5-meter grid spacing which were used as inputs for hydrological modeling. The USGS published the results in a series of technical reports in 2002. The authors became interested in this dataset in 2013 while searching for geospatial data that would provide additional context and comparative references for an archaeological lidar project conducted in 2012 in the Honduran Mosquitia. After multiple requests to representatives from the USGS and BEG, we found various types of processed data in personal and institutional archives, culminating in the identification of 8-mm magnetic tapes that contained the original point clouds. Point clouds for the 15 communities plus a test area centered on the Maya site of Copan were recovered from the tapes (16 areas totaling 700 km2). These point clouds have been reprocessed by the authors using contemporary software and methods into higher resolution and fidelity products. Within these new products, we have identified and mapped multiple archaeological sites in proximity to modern cities, many of which are not part of the official Honduran site registry. Besides improving our understanding of ancient Honduras, our experiences dealing with issues of data management and access, ethics, and international collaboration have been informative. This paper summarizes our experiences in the hope that they will contribute to the discussion and development of best practices for handling geospatial datasets of archaeological value.
{"title":"Whose Data Is It Anyway? Lessons in Data Management and Sharing from Resurrecting and Repurposing Lidar Data for Archaeology Research in Honduras","authors":"J. Fernandez-Diaz, Anna S. Cohen","doi":"10.5334/jcaa.51","DOIUrl":"https://doi.org/10.5334/jcaa.51","url":null,"abstract":"As a response to Hurricane Mitch and the resulting widespread loss of life and destruction of Honduran infrastructure in 1998, the United States Geological Survey (USGS) conducted the first wide-area airborne lidar topographic mapping project in Central America. The survey was executed by the Bureau of Economic Geology at the University of Texas at Austin (BEG) in 2000, and it was intended to cover 240 square kilometers distributed among 15 flood-prone communities throughout Honduras. The original data processing produced basic digital elevation models at 1.5-meter grid spacing which were used as inputs for hydrological modeling. The USGS published the results in a series of technical reports in 2002. The authors became interested in this dataset in 2013 while searching for geospatial data that would provide additional context and comparative references for an archaeological lidar project conducted in 2012 in the Honduran Mosquitia. After multiple requests to representatives from the USGS and BEG, we found various types of processed data in personal and institutional archives, culminating in the identification of 8-mm magnetic tapes that contained the original point clouds. Point clouds for the 15 communities plus a test area centered on the Maya site of Copan were recovered from the tapes (16 areas totaling 700 km2). These point clouds have been reprocessed by the authors using contemporary software and methods into higher resolution and fidelity products. Within these new products, we have identified and mapped multiple archaeological sites in proximity to modern cities, many of which are not part of the official Honduran site registry. Besides improving our understanding of ancient Honduras, our experiences dealing with issues of data management and access, ethics, and international collaboration have been informative. This paper summarizes our experiences in the hope that they will contribute to the discussion and development of best practices for handling geospatial datasets of archaeological value.","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45685632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digital terrain models (DTM) based on airborne laser scanning (ALS) are an important source for identifying and monitoring archaeological sites and landscapes. However, a DTM is only one of many representations of a given surface. Its accuracy and quality must conform to its purpose and are a result of several considerations and decisions along the processing chain. One of the most important factors of ALS-based DTM generation is ground point filtering, i.e., the classification of the acquired point-cloud into terrain and off-terrain points. Filtering is not straightforward. The resulting DTM is usually a compromise that might show the surface below very dense vegetation while losing detail in other areas. In this paper, we show that in very complex situations (e.g., strongly varying vegetation cover), an optimal compromise is difficult to achieve, and more than one filter with different settings adapted to the varying degree of vegetation cover is necessary. For practical reasons, the results need to be combined into a single DTM. This is demonstrated using the case study of a Mediterranean landscape in Croatia, which consists of open areas (agricultural and grassland), olive plantations, as well as extremely dense and evergreen macchia vegetation. The results are the first step toward an adaptive ground point filtering strategy that might be useful far beyond the field of archaeology.
{"title":"Archaeological Ground Point Filtering of Airborne Laser Scan Derived Point-Clouds in a Difficult Mediterranean Environment","authors":"M. Doneus, G. Mandlburger, Nives Doneus","doi":"10.5334/jcaa.44","DOIUrl":"https://doi.org/10.5334/jcaa.44","url":null,"abstract":"Digital terrain models (DTM) based on airborne laser scanning (ALS) are an important source for identifying and monitoring archaeological sites and landscapes. However, a DTM is only one of many representations of a given surface. Its accuracy and quality must conform to its purpose and are a result of several considerations and decisions along the processing chain. One of the most important factors of ALS-based DTM generation is ground point filtering, i.e., the classification of the acquired point-cloud into terrain and off-terrain points. Filtering is not straightforward. The resulting DTM is usually a compromise that might show the surface below very dense vegetation while losing detail in other areas. In this paper, we show that in very complex situations (e.g., strongly varying vegetation cover), an optimal compromise is difficult to achieve, and more than one filter with different settings adapted to the varying degree of vegetation cover is necessary. For practical reasons, the results need to be combined into a single DTM. This is demonstrated using the case study of a Mediterranean landscape in Croatia, which consists of open areas (agricultural and grassland), olive plantations, as well as extremely dense and evergreen macchia vegetation. The results are the first step toward an adaptive ground point filtering strategy that might be useful far beyond the field of archaeology.","PeriodicalId":32632,"journal":{"name":"Journal of Computer Applications in Archaeology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42833980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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