Semi-Supervised Learning for Spatio-Temporal Landcover Monitoring

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Semi-Supervised Learning for Spatio-Temporal Landcover Monitoring

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Semi-Supervised Learning for Spatio-Temporal Landcover Monitoring

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

IEEE Geoscience and Remote Sensing Letters

ISSN

1545-598X

e-ISSN

1558-0571

Svazek periodika

22

Číslo periodika v rámci svazku

August

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

5

Strana od-do

—

Kód UT WoS článku

001571280700004

EID výsledku v databázi Scopus

2-s2.0-105013877184