Di Lorenzo Giovanni, Angelini Franco, Pierallini Michele, Tolomei Simone, De Benedittis Davide, Denaro Agnese, Rivieccio Giovanni, Caria Maria Carmela, Bonini Federica, Grassi Anna, de Simone Leopoldo, Fanfarillo Emanuele, Fiaschi Tiberio, Maccherini Simona, Valle Barbara, Borgatti Marina Serena, Bagella Simonetta, Gigante Daniela, Angiolini Claudia, Caccianiga Marco, Garabini Manolo
Centro di Ricerca 'Enrico Piaggio', and Dipartimento di Ingegneria dell'Informazione, University of Pisa, Largo Lucio Lazzarino 1, 56122, Pisa, Italy.
Department of Chemical, Physical, Mathematical and Natural Sciences, Via Piandanna 4, 07100, Sassari, Italy.
Sci Data. 2025 May 20;12(1):822. doi: 10.1038/s41597-025-05182-7.
The present data descriptor presents a dataset designed for the detection of plant species in various habitats of the European Union. This dataset is based on images captured using multiple different hardware including quadrupedal robot ANYmal C, referring to ecologically important species to assess the presence and conservation status in Annex I habitats 2110, 2120, 6210*, 8110, 8120, and 9210*. Plant scientists and robotic engineers gathered the data in key Italian protected areas and labeled it using YOLOtxt format. Researchers in vegetation science, habitat monitoring, robotics, machine learning, and biodiversity conservation can access the dataset through Zenodo. The ultimate goal of this collaborative effort was to create a dataset that can be used to train artificial intelligence models to assess parameters that enable robotic habitat monitoring. The availability of this dataset may enhance future studies and conservation initiatives for Annex I habitats inside and outside the Natura 2000 network. The dataset and the methods used to obtain it are fully described, highlighting the significance of interdisciplinary cooperation in habitat monitoring.
本数据描述符展示了一个旨在检测欧盟不同栖息地植物物种的数据集。该数据集基于使用包括四足机器人ANYmal C在内的多种不同硬件拍摄的图像，涉及生态重要物种，以评估附件I中2110、2120、6210*、8110、8120和9210*栖息地的物种存在情况和保护状况。植物科学家和机器人工程师在意大利的关键保护区收集了数据，并使用YOLOtxt格式进行标注。植被科学、栖息地监测、机器人技术、机器学习和生物多样性保护领域的研究人员可以通过Zenodo访问该数据集。这项合作努力的最终目标是创建一个可用于训练人工智能模型的数据集，以评估能够实现机器人栖息地监测的参数。该数据集的可用性可能会加强对自然2000网络内外附件I栖息地的未来研究和保护举措。文中对数据集及其获取方法进行了全面描述，突出了跨学科合作在栖息地监测中的重要性。
