Semi-Supervised Learning for Spatio-Temporal Landcover Monitoring
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00385712" target="_blank" >RIV/68407700:21230/25:00385712 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/LGRS.2025.3600458" target="_blank" >https://doi.org/10.1109/LGRS.2025.3600458</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/LGRS.2025.3600458" target="_blank" >10.1109/LGRS.2025.3600458</a>
Alternative languages
Result language
angličtina
Original language name
Semi-Supervised Learning for Spatio-Temporal Landcover Monitoring
Original language description
We consider the task of spatio-temporal landcover monitoring and formulate it as follows. Given a time sequence of multispectral satellite images of an area of interest (AOI), we want to predict a corresponding sequence of semantic segmentations with segment labels representing landcover types. We propose to combine asymmetric UNets (achieving super-resolution segmentation) with Markov chain models to account for both spatial and temporal dependencies. Such models cannot be trained in a supervised manner, as obtaining dense spatio-temporal annotations for satellite image time sequences is infeasible. We therefore focus on the challenge of their semi-supervised training. The proposed approach is evaluated on the task of forest monitoring in a national park in the Czech Republic, which suffers from a severe forest dieback due to droughts and bark beetle outbreaks. We achieve 83 % (90 %) prediction accuracy in this challenging task.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Geoscience and Remote Sensing Letters
ISSN
1545-598X
e-ISSN
1558-0571
Volume of the periodical
22
Issue of the periodical within the volume
August
Country of publishing house
DE - GERMANY
Number of pages
5
Pages from-to
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UT code for WoS article
001571280700004
EID of the result in the Scopus database
2-s2.0-105013877184