Large-scale automatic extraction of agricultural greenhouses based on high-resolution remote sensing and deep learning technologies.
Agricultural greenhouses
Automatic extraction
Deep learning
Facility agriculture
Remote sensing
Journal
Environmental science and pollution research international
ISSN: 1614-7499
Titre abrégé: Environ Sci Pollut Res Int
Pays: Germany
ID NLM: 9441769
Informations de publication
Date de publication:
Oct 2023
Oct 2023
Historique:
received:
31
03
2023
accepted:
06
09
2023
medline:
23
10
2023
pubmed:
21
9
2023
entrez:
21
9
2023
Statut:
ppublish
Résumé
Widely used agricultural greenhouses are critical in the development of facility agriculture because of not only their huge capacity in food and vegetable supplies, but also their environmental and climatic effects. Therefore, it is important to obtain the spatial distribution of agricultural greenhouses for agricultural production, policy making, and even environmental protection. Remote sensing technologies have been widely used in greenhouse extraction mainly in small or local regions, while large-scale and high-resolution (~ 1-m) greenhouse extraction is still lacking. In this study, agricultural greenhouses in an important agricultural province (Shandong, China) are extracted by the combination of high-resolution remote sensing images from Google Earth and deep learning algorithm with high accuracy (94.04% for mean intersection over union over test set). The results demonstrated that the agricultural greenhouses cover an area of 1755.3 km
Identifiants
pubmed: 37733202
doi: 10.1007/s11356-023-29802-0
pii: 10.1007/s11356-023-29802-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
106671-106686Subventions
Organisme : Key Technologies Research and Development Program
ID : 2021YFF0704400
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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