Field Crops Classiffcation Using Sentinel-2 Satellite Image Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F21%3A00353105" target="_blank" >RIV/68407700:21340/21:00353105 - isvavai.cz</a>

Výsledek na webu

<a href="https://gams.fjfi.cvut.cz" target="_blank" >https://gams.fjfi.cvut.cz</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Field Crops Classiffcation Using Sentinel-2 Satellite Image Data

Popis výsledku v původním jazyce

Sentinel-2 mission, developed and operated by European Space Agency (ESA), is designed to provide high-resolution image data over land and coastal waters, which are further used for a multitude of applications, such as agricultural monitoring. Acquired data by the Sentinel-2 satellite are publicly available under the Copernicus Programme and can be accessed straightforwardly. In addition, we are provided with annotated maps of agricultural fields, which can be used as ground truth data. These maps include the location and additional speciffcation of fields and crops grown on the field. In this paper, we are interested in field crop classiffcation within the specified region. Aforementioned Sentinel-2 satellite image data and field labels are therefore combined to provide a dataset. This dataset can be then utilized by the classifier. For this cause, convolutional neural networks are used, as they have shown outstanding results of image classiffcation over the past years.

Název v anglickém jazyce

Field Crops Classiffcation Using Sentinel-2 Satellite Image Data

Popis výsledku anglicky

Sentinel-2 mission, developed and operated by European Space Agency (ESA), is designed to provide high-resolution image data over land and coastal waters, which are further used for a multitude of applications, such as agricultural monitoring. Acquired data by the Sentinel-2 satellite are publicly available under the Copernicus Programme and can be accessed straightforwardly. In addition, we are provided with annotated maps of agricultural fields, which can be used as ground truth data. These maps include the location and additional speciffcation of fields and crops grown on the field. In this paper, we are interested in field crop classiffcation within the specified region. Aforementioned Sentinel-2 satellite image data and field labels are therefore combined to provide a dataset. This dataset can be then utilized by the classifier. For this cause, convolutional neural networks are used, as they have shown outstanding results of image classiffcation over the past years.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

SPMS 2020/21 Stochastic and Physical Monitoring Systems, Proceedings of the international conferences

ISBN

978-80-01-06922-6

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

61-64

Název nakladatele

České vysoké učení technické v Praze

Místo vydání

Praha

Místo konání akce

Malá Skála

Datum konání akce

24. 6. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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