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<p>Forest conservation and management are increasingly challenged by evolving societal expectations, biodiversity decline, and the impacts of climate change, requiring accurate, spatially detailed data for effective decision-making. Remote sensing and photogrammetry have become critical tools allowing detailed mapping and measurement of forests worldwide. While traditional satellite and airborne remote sensing remains important, ground-based data is becoming increasingly important for monitoring biodiversity and optimising management. Despite advances in deep learning for tree segmentation and species classification, the lack of extensive, high-quality labelled datasets is hampering development. To address this issue, TreeScanPL10K is being introduced — a dataset that surpasses previous resources in scope, comprising 10,417 individual trees, with species identified for approximately 72%. The dataset, which represents most of the forest-forming species in Central Europe, was collected in various Polish forest stands of different ages, composition and development stages. TreeScanPL10K aims to support researchers and forestry professionals by enabling the training and testing of advanced analytical tools, promoting transparency and accelerating progress in precision forestry and ecological studies.</p>

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A Central European tree species dataset of annotated terrestrial laser scanning point clouds - TreeScanPL10K

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月4日

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  • Krzysztof Stereńczak,
  • Maksymilian Kulicki,
  • Bartłomiej Kraszewski,
  • Torana Arya Gasica,
  • Krzysztof Mitelsztedt,
  • Miłosz Mielcarek,
  • Elvis Tangwa,
  • Yousef Erfanifard,
  • Michał Brach,
  • Hepi Hapsari Handayani,
  • Anisa Nabila Rizki Ramadhani,
  • Paweł Hawryło,
  • Grzegorz Krok,
  • Aura Jovita Gandari Armazeta,
  • Dhea Rizky Arisandy,
  • Franciszek Błaś,
  • Rena Anggita Damayanti,
  • Raul Javier De Yong,
  • Deden,
  • Karolina Fabjan,
  • Farida Nuraini Fathimah,
  • Julia Górska,
  • Qarina Putri Amelia Nuri Ila,
  • Reyhan Dhihan Irawan,
  • Filip Iwaniec,
  • Andrzej Jabłoński,
  • Wojciech Krawczyk,
  • Bartosz Kwaśniewski,
  • Karol Kwartnik,
  • Raihanata Mardhatillah,
  • Rahmadiana Aulya Massayudevi,
  • Jakub Miszczyszyn,
  • Shafira Btari Ari Murti,
  • Dafina Ainand Nabila,
  • Riskyanirmala Novatiana,
  • Widodo Eko Prasetyo,
  • Febryanto Pratama,
  • Alita Tri Utami Ramadhani,
  • Natalia Rębisz,
  • Haikal Fikri Firdausi Rizannyansyah,
  • Zuzanna Seremak,
  • Bartosz Skomorucha,
  • Daniel Skóra,
  • Naufal Sukmawan,
  • Chelsea Alfarelia Putri Taslyanto,
  • Gabriela Turemka,
  • Efsa Valika,
  • Dira Muvianti Warman,
  • Aleksandra Wilczyńska,
  • Kamila Winkowska,
  • Kacper Witkowski,
  • Syauqi Arka Yudisti,
  • Nikodem Zdunek,
  • Damian Zieliński,
  • Adam Ziółkowski

摘要

Forest conservation and management are increasingly challenged by evolving societal expectations, biodiversity decline, and the impacts of climate change, requiring accurate, spatially detailed data for effective decision-making. Remote sensing and photogrammetry have become critical tools allowing detailed mapping and measurement of forests worldwide. While traditional satellite and airborne remote sensing remains important, ground-based data is becoming increasingly important for monitoring biodiversity and optimising management. Despite advances in deep learning for tree segmentation and species classification, the lack of extensive, high-quality labelled datasets is hampering development. To address this issue, TreeScanPL10K is being introduced — a dataset that surpasses previous resources in scope, comprising 10,417 individual trees, with species identified for approximately 72%. The dataset, which represents most of the forest-forming species in Central Europe, was collected in various Polish forest stands of different ages, composition and development stages. TreeScanPL10K aims to support researchers and forestry professionals by enabling the training and testing of advanced analytical tools, promoting transparency and accelerating progress in precision forestry and ecological studies.