Convolutional neural networks for urban green areas semantic segmentation on Sentinel-2 data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11310%2F24%3A10486258" target="_blank" >RIV/00216208:11310/24:10486258 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21110/24:00376522

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=2KJ7sDz~3g" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=2KJ7sDz~3g</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rsase.2024.101238" target="_blank" >10.1016/j.rsase.2024.101238</a>

Result language

angličtina

Original language name

Convolutional neural networks for urban green areas semantic segmentation on Sentinel-2 data

Original language description

Urban green areas are essential components of any urban environment, providing a wide range of uses. However, there is currently a noticeable absence of an automated tool for their land use classification. The use of urban green areas depends on their size, shape, and relationship with their surroundings, all of which are fundamental features in convolutional neural networks. Various convolutional neural network architectures (FCN, U -Net, SegNet, DeepLabv3+) were evaluated on open and widely accessible Sentinel-2 data for semantic segmentation of land cover and land use in different levels of urban green areas nomenclature and band combinations. Moreover, we compared the CNNs with random forests model as a baseline to underline the CNNs&apos; strengths. The evaluation found that convolutional neural networks are capable of the land cover and land use semantic segmentation not only on the full-band Sentinel-2 scenes but also on limited subdatasets consisting only of RGB bands. U -Net is identified as the bestperforming architecture, achieving an overall accuracy of almost 95% for a simple binary vegetation detection, 90% for the land -use task, and almost 88% for the land -use task enhanced by a distinction between high and low vegetation, while random forests reached 93%, 84%, and 81%, respectively. CNNs&apos; misclassifications were primarily identified at the edges of two neighbouring competing classes where mixed pixels appear. Data augmentation improved the model&apos;s performance in 94% of cases. However, dropout layers led to an overall accuracy decrease in more than half of the cases. Additionally, we compared the segmented urban green area with a pan -European dataset, the Coordination of Information on the Environment Land Cover - and found that the latter omits 74% of the total urban vegetation. This is mainly due to its minimal mapping unit specification. It is concluded that the most suitable approach for automated urban green areas land cover and land use semantic segmentation is the use of convolutional neural networks, from the tested architectures particularly U -Net.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10508 - Physical geography

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

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

Name of the periodical

Remote Sensing Applications: Society and Environment

ISSN

2352-9385

e-ISSN

2352-9385

Volume of the periodical

36

Issue of the periodical within the volume

November

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

26

Pages from-to

101238

UT code for WoS article

001254375000001

EID of the result in the Scopus database

2-s2.0-85195846979