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.

Robotic monitoring of European habitats: a labeled dataset for plant detection in Annex I habitats of Italy / G. Di Lorenzo, F. Angelini, M. Pierallini, S. Tolomei, D. De Benedittis, A. Denaro, G. Rivieccio, M.C. Caria, F. Bonini, A. Grassi, L. De Simone, E. Fanfarillo, T. Fiaschi, S. Maccherini, B. Valle, M.S. Borgatti, S. Bagella, D. Gigante, C. Angiolini, M. Caccianiga, M. Garabini. - In: SCIENTIFIC DATA. - ISSN 2052-4463. - 12:1(2025), pp. 822.1-822.24. [10.1038/s41597-025-05182-7]

Robotic monitoring of European habitats: a labeled dataset for plant detection in Annex I habitats of Italy

B. Valle;M. Caccianiga;
2025

Abstract

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.
No
English
Settore BIOS-01/B - Botanica sistematica
Settore BIOS-01/C - Botanica ambientale e applicata
Articolo
Esperti anonimi
Pubblicazione scientifica
Goal 14: Life below water
Goal 15: Life on land
   Natural Intelligence for Robotic Monitoring of Habitats (NI)
   NI
   EUROPEAN COMMISSION
   H2020
   101016970
2025
20-mag-2025
Nature Research
12
1
822
1
24
24
Pubblicato
Periodico con rilevanza internazionale
crossref
Aderisco
info:eu-repo/semantics/article
Robotic monitoring of European habitats: a labeled dataset for plant detection in Annex I habitats of Italy / G. Di Lorenzo, F. Angelini, M. Pierallini, S. Tolomei, D. De Benedittis, A. Denaro, G. Rivieccio, M.C. Caria, F. Bonini, A. Grassi, L. De Simone, E. Fanfarillo, T. Fiaschi, S. Maccherini, B. Valle, M.S. Borgatti, S. Bagella, D. Gigante, C. Angiolini, M. Caccianiga, M. Garabini. - In: SCIENTIFIC DATA. - ISSN 2052-4463. - 12:1(2025), pp. 822.1-822.24. [10.1038/s41597-025-05182-7]
open
Prodotti della ricerca::01 - Articolo su periodico
21
262
Article (author)
Periodico con Impact Factor
G. Di Lorenzo, F. Angelini, M. Pierallini, S. Tolomei, D. De Benedittis, A. Denaro, G. Rivieccio, M.C. Caria, F. Bonini, A. Grassi, L. De Simone, E. F...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1177216
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