APPLICATION OF TEMPORAL CONVOLUTIONAL NEURAL NETWORK FOR THE CLASSIFICATION OF CROPS ON SENTINEL-2 TIME SERIES Article Swipe
Matej Račič
,
Krištof Oštir
,
Devis Peressutti
,
A. Zupanc
,
Luka Čehovin Zajc
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xliii-b2-2020-1337-2020
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xliii-b2-2020-1337-2020
The recent development of Earth observation systems – like the Copernicus Sentinels – has provided access to satellite data with high spatial and temporal resolution. This is a key component for the accurate monitoring of state and changes in land use and land cover. In this research, the crops classification was performed by implementing two deep neural networks based on structured data. Despite the wide availability of optical satellite imagery, such as Landsat and Sentinel-2, the limitations of high quality tagged data make the training of machine learning methods very difficult. For this purpose, we have created and labeled a dataset of the crops in Slovenia for the year 2017. With the selected methods we are able to correctly classify 87% of all cultures. Similar studies have already been carried out in the past, but are limited to smaller regions or a smaller number of crop types.
Related Topics
Concepts
Convolutional neural network
Earth observation
Land cover
Satellite
Remote sensing
Temporal resolution
Computer science
Deep learning
Artificial neural network
Satellite imagery
Temporal database
Time series
Key (lock)
Artificial intelligence
Machine learning
Data mining
Pattern recognition (psychology)
Land use
Geography
Quantum mechanics
Aerospace engineering
Physics
Computer security
Civil engineering
Engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/isprs-archives-xliii-b2-2020-1337-2020
- https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/1337/2020/isprs-archives-XLIII-B2-2020-1337-2020.pdf
- OA Status
- diamond
- Cited By
- 22
- References
- 8
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3049631588
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3049631588Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/isprs-archives-xliii-b2-2020-1337-2020Digital Object Identifier
- Title
-
APPLICATION OF TEMPORAL CONVOLUTIONAL NEURAL NETWORK FOR THE CLASSIFICATION OF CROPS ON SENTINEL-2 TIME SERIESWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-14Full publication date if available
- Authors
-
Matej Račič, Krištof Oštir, Devis Peressutti, A. Zupanc, Luka Čehovin ZajcList of authors in order
- Landing page
-
https://doi.org/10.5194/isprs-archives-xliii-b2-2020-1337-2020Publisher landing page
- PDF URL
-
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/1337/2020/isprs-archives-XLIII-B2-2020-1337-2020.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2020/1337/2020/isprs-archives-XLIII-B2-2020-1337-2020.pdfDirect OA link when available
- Concepts
-
Convolutional neural network, Earth observation, Land cover, Satellite, Remote sensing, Temporal resolution, Computer science, Deep learning, Artificial neural network, Satellite imagery, Temporal database, Time series, Key (lock), Artificial intelligence, Machine learning, Data mining, Pattern recognition (psychology), Land use, Geography, Quantum mechanics, Aerospace engineering, Physics, Computer security, Civil engineering, EngineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
22Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 8, 2023: 5, 2022: 5, 2021: 3Per-year citation counts (last 5 years)
- References (count)
-
8Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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