{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T05:58:18Z","timestamp":1768888698254,"version":"3.49.0"},"reference-count":50,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T00:00:00Z","timestamp":1631664000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Over the past several centuries, the iron industry played a central role in the economy of Sweden and much of northern Europe. A crucial component of iron manufacturing was the production of charcoal, which was often created in charcoal piles. These features are visible in LiDAR (light detection and ranging) datasets. These charcoal piles vary in their morphology by region, and training data for some feature types are severely lacking. Here, we investigate the potential for machine automation to aid archaeologists in recording charcoal piles with limited training data availability in a forested region of J\u00f6nk\u00f6ping County, Sweden. We first use hydrological depression algorithms to conduct a preliminary assessment of the study region and compile suitable training data for charcoal production sites. Then, we use these datasets to train a series of RetinaNet deep learning models, which are less computationally expensive than many popular deep learning architectures (e.g., R-CNNs), allowing for greater usability. Together, our results demonstrate how charcoal piles can be automatically extracted from LiDAR datasets, which has great implications for improving our understanding of the long-term environmental impact of the iron industry across Northern Europe. Furthermore, our workflow for developing and implementing deep learning models for archaeological research can expand the use of such methods to regions that lack suitable training data.<\/jats:p>","DOI":"10.3390\/rs13183680","type":"journal-article","created":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T12:00:44Z","timestamp":1631707244000},"page":"3680","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms, and Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5783-3578","authenticated-orcid":false,"given":"Dylan S.","family":"Davis","sequence":"first","affiliation":[{"name":"Department of Anthropology, The Pennsylvania State University, University Park, PA 16802, USA"}]},{"given":"Julius","family":"Lundin","sequence":"additional","affiliation":[{"name":"Arkeologerna, Statens Historiska Museer, 226 60 Lund, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,15]]},"reference":[{"key":"ref_1","unstructured":"\u00c5gren, M. (1998). Iron-Making Societies: Early Industrial Development in Sweden and Russia, 1600\u20131900, Berghahn Books."},{"key":"ref_2","unstructured":"Petterssen, J.E. (1997). Svenskt J\u00e4rn Under 2500 \u00e5r: Fr\u00e5n Gruvpigor och Smeddr\u00e4ngar till Operat\u00f6rer, Bokb\u00f6rsen AB."},{"key":"ref_3","unstructured":"Svensson, E. (1998). M\u00e4nniskor i Utmark. [Lund Studies in Medieval Archaeology 21], Lund University."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.quaint.2020.10.020","article-title":"2500 Years of Charcoal Production in the Low Countries: The Chronology and Typology of Charcoal Kilns and Their Relation with Early Iron Production","volume":"593\u2013594","author":"Deforce","year":"2021","journal-title":"Quat. Int."},{"key":"ref_5","unstructured":"Hennius, A. (2019). Sp\u00e5r Av Kolning: Arkeologiskt Kunskapsunderlag Och Forsknings\u00f6versikt, Riksantikvarie\u00e4mbetet."},{"key":"ref_6","first-page":"11","article-title":"The Supply with Charcoal of the Swedish Iron Industry from 1830 to 1950","volume":"35","author":"Arpi","year":"1953","journal-title":"Geogr. Ann."},{"key":"ref_7","unstructured":"(2021, August 03). Jernkontoret Svenska J\u00e4rn- och St\u00e5lindustrins Historia. Available online: https:\/\/www.jernkontoret.se\/sv\/stalindustrin\/stalindustrins-historia\/."},{"key":"ref_8","unstructured":"Anglert, M., and Lager\u00e5s, P. (2009). M\u00e4nniskorna Och Skogen: Arkeologiska Platser i \u00d6rkelljungatrakten, Riksantikvarie\u00e4mbetet."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Stere\u0144czak, K., Zap\u0142ata, R., W\u00f3jcik, J., Kraszewski, B., Mielcarek, M., Mitelsztedt, K., Bia\u0142czak, M., Krok, G., Kuberski, \u0141., and Markiewicz, A. (2020). ALS-Based Detection of Past Human Activities in the Bia\u0142owie\u017ca Forest\u2014New Evidence of Unknown Remains of Past Agricultural Systems. Remote Sens., 12.","DOI":"10.3390\/rs12162657"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zap\u0142ata, R., Baku\u0142a, K., and Ostrowski, W. (2014, January 2\u20137). Transformation Methods and ALS-Data Visualization in the Studies of Historical Charcoal Piles. Proceedings of the International Multidisciplinary Scientific Conferences on Social Sciences and Arts SGEM2014, Albena, Bulgaria.","DOI":"10.5593\/sgemsocial2014\/B31\/S9.053"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1073\/pnas.1015876108","article-title":"High-Precision Radiocarbon Dating Shows Recent and Rapid Initial Human Colonization of East Polynesia","volume":"108","author":"Wilmshurst","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1002\/arp.1806","article-title":"A Modified Mask Region-Based Convolutional Neural Network Approach for the Automated Detection of Archaeological Sites on High-Resolution Light Detection and Ranging-Derived Digital Elevation Models in the North German Lowland","volume":"28","author":"Bonhage","year":"2021","journal-title":"Archaeol. Prospect."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1038\/s43247-021-00137-3","article-title":"Legacies of Indigenous Land Use Shaped Past Wildfire Regimes in the Basin-Plateau Region, USA","volume":"2","author":"Carter","year":"2021","journal-title":"Commun. Earth Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1002\/arp.1497","article-title":"A Template-Matching Approach Combining Morphometric Variables for Automated Mapping of Charcoal Kiln Sites: Automated Mapping of Charcoal Kiln Sites","volume":"22","author":"Schneider","year":"2015","journal-title":"Archaeol. Prospect."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.quaint.2014.09.041","article-title":"Pre-Industrial Charcoal Production in Lower Lusatia (Brandenburg, Germany): Detection and Evaluation of a Large Charcoal-Burning Field by Combining Archaeological Studies, GIS-Based Analyses of Shaded-Relief Maps and Dendrochronological Age Determination","volume":"367","author":"Raab","year":"2015","journal-title":"Quat. Int."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.2136\/sssaj2017.02.0067","article-title":"Soils on Historic Charcoal Hearths: Terminology and Chemical Properties","volume":"81","author":"Hirsch","year":"2017","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104991","DOI":"10.1016\/j.catena.2020.104991","article-title":"Gradients of Geochemical Change in Relic Charcoal Hearth Soils, Northwestern Connecticut, USA","volume":"197","author":"Donovan","year":"2021","journal-title":"Catena"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.quaint.2019.04.015","article-title":"High Concentration of Charcoal Hearth Remains as Legacy of Historical Ferrous Metallurgy in Southern Poland","volume":"512","author":"Rutkiewicz","year":"2019","journal-title":"Quat. Int."},{"key":"ref_19","unstructured":"Boheman, E. (1921). Svenska Turistf\u00f6reningens \u00c5rsskrift 1921, Wahlstr\u00f6m & Widstrand."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1002\/arp.1730","article-title":"Object-Based Image Analysis: A Review of Developments and Future Directions of Automated Feature Detection in Landscape Archaeology","volume":"26","author":"Davis","year":"2019","journal-title":"Archaeol. Prospect."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.jas.2019.04.005","article-title":"Theory and Practice for an Object-Based Approach in Archaeological Remote Sensing","volume":"107","author":"Magnini","year":"2019","journal-title":"J. Archaeol. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Traviglia, A., and Torsello, A. (2017). Landscape Pattern Detection in Archaeological Remote Sensing. Geosciences, 7.","DOI":"10.3390\/geosciences7040128"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Agapiou, A. (2020). Optimal Spatial Resolution for the Detection and Discrimination of Archaeological Proxies in Areas with Spectral Heterogeneity. Remote Sens., 12.","DOI":"10.3390\/rs12010136"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104998","DOI":"10.1016\/j.jas.2019.104998","article-title":"Convolutional Neural Networks for Archaeological Site Detection\u2014Finding \u201cPrincely\u201d Tombs","volume":"110","author":"Caspari","year":"2019","journal-title":"J. Archaeol. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.quaint.2015.12.099","article-title":"An Approach to the Automatic Surveying of Prehistoric Barrows through LiDAR","volume":"435","year":"2017","journal-title":"Quat. Int."},{"key":"ref_26","first-page":"166","article-title":"A Comparison of Automated Object Extraction Methods for Mound and Shell-Ring Identification in Coastal South Carolina","volume":"23","author":"Davis","year":"2019","journal-title":"J. Archaeol. Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.jas.2016.04.011","article-title":"Automated Feature Extraction for Prospection and Analysis of Monumental Earthworks from Aerial LiDAR in the Kingdom of Tonga","volume":"69","author":"Freeland","year":"2016","journal-title":"J. Archaeol. Sci."},{"key":"ref_28","first-page":"143","article-title":"Using CarcassonNet to Automatically Detect and Trace Hollow Roads in LiDAR Data from the Netherlands","volume":"47","author":"Landauer","year":"2020","journal-title":"J. Cult. Herit."},{"key":"ref_29","first-page":"31","article-title":"Learning to Look at LiDAR: The Use of R-CNN in the Automated Detection of Archaeological Objects in LiDAR Data from the Netherlands","volume":"2","author":"Lambers","year":"2019","journal-title":"J. Comput. Appl. Archaeol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1002\/arp.1731","article-title":"Using Deep Neural Networks on Airborne Laser Scanning Data: Results from a Case Study of Semi-Automatic Mapping of Archaeological Topography on Arran, Scotland","volume":"26","author":"Trier","year":"2019","journal-title":"Archaeol. Prospect."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105433","DOI":"10.1016\/j.jas.2021.105433","article-title":"Deep Learning Reveals Extent of Archaic Native American Shell-Ring Building Practices","volume":"132","author":"Davis","year":"2021","journal-title":"J. Archaeol. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lambers, K., Verschoof-van der Vaart, W., and Bourgeois, Q. (2019). Integrating Remote Sensing, Machine Learning, and Citizen Science in Dutch Archaeological Prospection. Remote Sens., 11.","DOI":"10.3390\/rs11070794"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Somrak, M., D\u017eeroski, S., and Kokalj, \u017d. (2020). Learning to Classify Structures in ALS-Derived Visualizations of Ancient Maya Settlements with CNN. Remote Sens., 12.","DOI":"10.3390\/rs12142215"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Davis, D.S. (2020). Geographic Disparity in Machine Intelligence Approaches for Archaeological Remote Sensing Research. Remote Sens., 12.","DOI":"10.3390\/rs12060921"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. (2018). A Survey on Deep Transfer Learning. Artificial Neural Networks and Machine Learning, Proceedings of the International Conference on Artificial Neural Networks (ICANN 2018), Rhodes, Greece, 4\u20137 October 2018, Springer.","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref_36","first-page":"102241","article-title":"Automated Mapping of Cultural Heritage in Norway from Airborne Lidar Data Using Faster R-CNN","volume":"95","author":"Trier","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 22\u201329). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Venice, Italy.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_39","unstructured":"ESRI (2020). ArcGIS Pro, Environmental Systems Research Institute, Inc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/0734578X.2018.1482186","article-title":"Automated Mound Detection Using Lidar and Object-Based Image Analysis in Beaufort County, South Carolina","volume":"38","author":"Davis","year":"2019","journal-title":"Southeast. Archaeol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"364","DOI":"10.3390\/heritage3020022","article-title":"Assessing the Utility of Open-Access Bathymetric Data for Shipwreck Detection in the United States","volume":"3","author":"Davis","year":"2020","journal-title":"Heritage"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Dolej\u0161, M., Pacina, J., Vesel\u00fd, M., and Br\u00e9tt, D. (2020). Aerial Bombing Crater Identification: Exploitation of Precise Digital Terrain Models. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9120713"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1515\/opar-2020-0113","article-title":"Between Land and Sea: An Airborne LiDAR Field Survey to Detect Ancient Sites in the Chekka Region\/Lebanon Using Spatial Analyses","volume":"6","author":"Rom","year":"2020","journal-title":"Open Archaeol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2041","DOI":"10.1080\/13658816.2015.1038719","article-title":"A Localized Contour Tree Method for Deriving Geometric and Topological Properties of Complex Surface Depressions Based on High-Resolution Topographical Data","volume":"29","author":"Wu","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1016\/j.cageo.2005.11.002","article-title":"Distinguishing Actual and Artefact Depressions in Digital Elevation Data","volume":"32","author":"Lindsay","year":"2006","journal-title":"Comput. Geosci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.geomorph.2016.05.006","article-title":"Automated Delineation of Karst Sinkholes from LiDAR-Derived Digital Elevation Models","volume":"266","author":"Wu","year":"2016","journal-title":"Geomorphology"},{"key":"ref_47","unstructured":"ESRI (2020). ArcGIS, Environmental Systems Research Institute, Inc."},{"key":"ref_48","first-page":"23","article-title":"Index That Quantifies Topographic Heterogeneity","volume":"5","author":"Riley","year":"1999","journal-title":"Intermt. J. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1017\/aap.2020.18","article-title":"The Integration of Lidar and Legacy Datasets Provides Improved Explanations for the Spatial Patterning of Shell Rings in the American Southeast","volume":"8","author":"Davis","year":"2020","journal-title":"Adv. Archaeol. Pract."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1002\/arp.1738","article-title":"Counting with the Invisible Record? The Role of LiDAR in the Interpretation of Megalithic Landscapes in South-western Iberia (Extremadura, Alentejo and Beira Baixa)","volume":"26","year":"2019","journal-title":"Archaeol. Prospect."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3680\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:59:57Z","timestamp":1760165997000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3680"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,15]]},"references-count":50,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183680"],"URL":"https:\/\/doi.org\/10.3390\/rs13183680","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,15]]}}}