Detection of Hunting Pits using Airborne Laser Scanning and Deep Learning

被引:1
|
作者
Lidberg, William [1 ]
Westphal, Florian [2 ]
Brax, Christoffer [3 ]
Sandstrom, Camilla [4 ]
Ostlund, Lars [1 ]
机构
[1] Swedish Univ Agr Sci, Umea, Sweden
[2] Jonkoping Univ, Jonkoping, Sweden
[3] Swedish Forest Agcy, Jonkoping, Sweden
[4] Umea Univ, Umea, Sweden
关键词
Archaeology; forest history; hunting pits; airborne laser scanning; artificial intelligence; deep learning; machine learning; TOPOGRAPHIC POSITION; LIGHT DETECTION; LIDAR; IMPACT;
D O I
10.1080/00934690.2024.2364428
中图分类号
K85 [文物考古];
学科分类号
0601 ;
摘要
Forests worldwide contain unique cultural traces of past human land use. Increased pressure on forest ecosystems and intensive modern forest management methods threaten these ancient monuments and cultural remains. In northern Europe, older forests often contain very old traces, such as millennia-old hunting pits and indigenous Sami hearths. Investigations have repeatedly found that forest owners often fail to protect these cultural remains and that many are damaged by forestry operations. Current maps of hunting pits are incomplete, and the locations of known pits have poor spatial accuracy. This study investigated whether hunting pits can be automatically mapped using national airborne laser data and deep learning. The best model correctly mapped 70% of all the hunting pits in the test data with an F1 score of 0.76. This model can be implemented across northern Scandinavia and could have an immediate effect on the protection of cultural remains.
引用
收藏
页码:395 / 405
页数:11
相关论文
共 50 条
  • [41] Using deep neural networks on airborne laser scanning data: Results from a case study of semi-automatic mapping of archaeological topography on Arran, Scotland
    Trier, Oivind Due
    Cowley, David C.
    Waldeland, Anders Ueland
    ARCHAEOLOGICAL PROSPECTION, 2019, 26 (02) : 165 - 175
  • [42] Machine Learning Algorithms to Predict Tree-Related Microhabitats using Airborne Laser Scanning
    Santopuoli, Giovanni
    Di Febbraro, Mirko
    Maesano, Mauro
    Balsi, Marco
    Marchetti, Marco
    Lasserre, Bruno
    REMOTE SENSING, 2020, 12 (13)
  • [43] A New Method of Building Footprints Detection Using Airborne Laser Scanning Data and Multispectral Image
    Luo, Yiping
    Jiang, Ting
    Gao, Shengli
    Wang, Xin
    5TH INTERNATIONAL SYMPOSIUM ON ADVANCED OPTICAL MANUFACTURING AND TESTING TECHNOLOGIES: OPTOELECTRONIC MATERIALS AND DEVICES FOR DETECTOR, IMAGER, DISPLAY, AND ENERGY CONVERSION TECHNOLOGY, 2010, 7658
  • [44] Detection of fallen trees in forested areas using small footprint airborne laser scanning data
    Muecke, Werner
    Deak, Balazs
    Schroiff, Anke
    Hollaus, Markus
    Pfeifer, Norbert
    CANADIAN JOURNAL OF REMOTE SENSING, 2013, 39 : S32 - S40
  • [45] Estimating and mapping forest structural diversity using airborne laser scanning data
    Mura, Matteo
    McRoberts, Ronald E.
    Chirici, Gherardo
    Marchetti, Marco
    REMOTE SENSING OF ENVIRONMENT, 2015, 170 : 133 - 142
  • [46] Evaluating Volumetric Glacier Change Methods Using Airborne Laser Scanning Data
    Joerg, Philip Claudio
    Zemp, Michael
    GEOGRAFISKA ANNALER SERIES A-PHYSICAL GEOGRAPHY, 2014, 96 (02) : 135 - 145
  • [47] Site index determination using a time series of airborne laser scanning data
    Moan, Maria asnes
    Bollandsas, Ole Martin
    Saarela, Svetlana
    Gobakken, Terje
    Naesset, Erik
    Orka, Hans Ole
    Noordermeer, Lennart
    FOREST ECOSYSTEMS, 2025, 12
  • [48] Using the full potential of Airborne Laser Scanning (aerial LiDAR) in wildlife research
    Cosgrove, Cameron F.
    Coops, Nicholas C.
    Martin, Tara G.
    WILDLIFE SOCIETY BULLETIN, 2024, 48 (03):
  • [49] SINGLE STRATA CANOPY COVER ESTIMATION USING AIRBORNE LASER SCANNING DATA
    Ferraz, Antonio
    Mallet, Clement
    Goncalves, Gil
    Tome, Margarida
    Soares, Paula
    Pereira, Luisa
    Jacquemoud, Stephane
    2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2013, : 184 - 187
  • [50] Detection of Pneumonia using Deep learning
    Zararia, Atharva
    Gangbhoj, Riddhi
    Kumar, Prashant
    Bhaiyya, Vaishnavi
    Borkar, Nishant M.
    INTERNATIONAL JOURNAL OF NEXT-GENERATION COMPUTING, 2023, 14 (01): : 150 - 153