Machine Learning-Based Processing Proof-of-Concept Pipeline for
Semi-Automatic Sentinel-2 Imagery Download, Cloudiness
Filtering, Classifications, and Updates of Open Land Use/Land
Cover Datasets

J 2021

Machine Learning-Based Processing Proof-of-Concept Pipeline for Semi-Automatic Sentinel-2 Imagery Download, Cloudiness Filtering, Classifications, and Updates of Open Land Use/Land Cover Datasets

ŘEZNÍK, Tomáš, Jan CHYTRÝ a Kateřina TROJANOVÁ

Základní údaje

Originální název

Machine Learning-Based Processing Proof-of-Concept Pipeline for Semi-Automatic Sentinel-2 Imagery Download, Cloudiness Filtering, Classifications, and Updates of Open Land Use/Land Cover Datasets

Autoři

ŘEZNÍK, Tomáš (203 Česká republika, domácí), Jan CHYTRÝ (203 Česká republika, domácí) a Kateřina TROJANOVÁ (203 Česká republika, domácí)

Vydání

ISPRS International Journal of Geo-Information, Basel, MDPI, 2021, 2220-9964

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10508 Physical geography

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.099

Kód RIV

RIV/00216224:14310/21:00121282

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.3390/ijgi10020102

UT WoS

000622576200001

Klíčová slova anglicky

machine learning; land use; land cover; satellite imagery; Sentinel 2; image classification; cloud masking; LightGBM estimator

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 16. 5. 2022 11:06, Mgr. Marie Šípková, DiS.

Anotace

V originále

Land use and land cover are continuously changing in today's world. Both domains, therefore, have to rely on updates of external information sources from which the relevant land use/land cover (classification) is extracted. Satellite images are frequent candidates due to their temporal and spatial resolution. On the contrary, the extraction of relevant land use/land cover information is demanding in terms of knowledge base and time. The presented approach offers a proof-of-concept machine-learning pipeline that takes care of the entire complex process in the following manner. The relevant Sentinel-2 images are obtained through the pipeline. Later, cloud masking is performed, including the linear interpolation of merged-feature time frames. Subsequently, four-dimensional arrays are created with all potential training data to become a basis for estimators from the scikit-learn library; the LightGBM estimator is then used. Finally, the classified content is applied to the open land use and open land cover databases. The verification of the provided experiment was conducted against detailed cadastral data, to which Shannon's entropy was applied since the number of cadaster information classes was naturally consistent. The experiment showed a good overall accuracy (OA) of 85.9%. It yielded a classified land use/land cover map of the study area consisting of 7188 km2 in the southern part of the South Moravian Region in the Czech Republic. The developed proof-of-concept machine-learning pipeline is replicable to any other area of interest so far as the requirements for input data are met.

Návaznosti

MUNI/A/1356/2019, interní kód MU

Název: Výzkum proměn geografických procesů a vztahů v prostoru a čase (Akronym: Progeo)

Investor: Masarykova univerzita, Výzkum proměn geografických procesů a vztahů v prostoru a čase, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

818346, interní kód MU

Název: Si-EU-Soil (Akronym: SIEUSIOL)

Investor: Evropská unie, Si-EU-Soil, Food security, sustainable agriculture and forestry, marine and maritime and inland water research (Societal Challenges)

Citovat

ŘEZNÍK, Tomáš, Jan CHYTRÝ a Kateřina TROJANOVÁ. Machine Learning-Based Processing Proof-of-Concept Pipeline for Semi-Automatic Sentinel-2 Imagery Download, Cloudiness Filtering, Classifications, and Updates of Open Land Use/Land Cover Datasets. ISPRS International Journal of Geo-Information. Basel: MDPI, 2021, roč. 10, č. 2, s. 1-23. ISSN 2220-9964. Dostupné z: https://dx.doi.org/10.3390/ijgi10020102.

@article{1754456,
   author = {Řezník, Tomáš and Chytrý, Jan and Trojanová, Kateřina},
   article_location = {Basel},
   article_number = {2},
   doi = {http://dx.doi.org/10.3390/ijgi10020102},
   keywords = {machine learning; land use; land cover; satellite imagery; Sentinel 2; image classification; cloud masking; LightGBM estimator},
   language = {eng},
   issn = {2220-9964},
   journal = {ISPRS International Journal of Geo-Information},
   title = {Machine Learning-Based Processing Proof-of-Concept Pipeline for Semi-Automatic Sentinel-2 Imagery Download, Cloudiness Filtering, Classifications, and Updates of Open Land Use/Land Cover Datasets},
   url = {https://doi.org/10.3390/ijgi10020102},
   volume = {10},
   year = {2021}
}

TY  - JOUR
ID  - 1754456
AU  - Řezník, Tomáš - Chytrý, Jan - Trojanová, Kateřina
PY  - 2021
TI  - Machine Learning-Based Processing Proof-of-Concept Pipeline for Semi-Automatic Sentinel-2 Imagery Download, Cloudiness Filtering, Classifications, and Updates of Open Land Use/Land Cover Datasets
JF  - ISPRS International Journal of Geo-Information
VL  - 10
IS  - 2
SP  - 1-23
EP  - 1-23
PB  - MDPI
SN  - 22209964
KW  - machine learning
KW  - land use
KW  - land cover
KW  - satellite imagery
KW  - Sentinel 2
KW  - image classification
KW  - cloud masking
KW  - LightGBM estimator
UR  - https://doi.org/10.3390/ijgi10020102
N2  - Land use and land cover are continuously changing in today's world. Both domains, therefore, have to rely on updates of external information sources from which the relevant land use/land cover (classification) is extracted. Satellite images are frequent candidates due to their temporal and spatial resolution. On the contrary, the extraction of relevant land use/land cover information is demanding in terms of knowledge base and time. The presented approach offers a proof-of-concept machine-learning pipeline that takes care of the entire complex process in the following manner. The relevant Sentinel-2 images are obtained through the pipeline. Later, cloud masking is performed, including the linear interpolation of merged-feature time frames. Subsequently, four-dimensional arrays are created with all potential training data to become a basis for estimators from the scikit-learn library; the LightGBM estimator is then used. Finally, the classified content is applied to the open land use and open land cover databases. The verification of the provided experiment was conducted against detailed cadastral data, to which Shannon's entropy was applied since the number of cadaster information classes was naturally consistent. The experiment showed a good overall accuracy (OA) of 85.9%. It yielded a classified land use/land cover map of the study area consisting of 7188 km2 in the southern part of the South Moravian Region in the Czech Republic. The developed proof-of-concept machine-learning pipeline is replicable to any other area of interest so far as the requirements for input data are met.
ER  -

ŘEZNÍK, Tomáš, Jan CHYTRÝ a Kateřina TROJANOVÁ. Machine Learning-Based Processing Proof-of-Concept Pipeline for Semi-Automatic Sentinel-2 Imagery Download, Cloudiness Filtering, Classifications, and Updates of Open Land Use/Land Cover Datasets. \textit{ISPRS International Journal of Geo-Information}. Basel: MDPI, 2021, roč.~10, č.~2, s.~1-23. ISSN~2220-9964. Dostupné z: https://dx.doi.org/10.3390/ijgi10020102.

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