A practically feasible transfer learning method for deep-temporal urban change monitoring
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F23%3A10252827" target="_blank" >RIV/61989100:27740/23:10252827 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1080/01431161.2023.2243021" target="_blank" >https://doi.org/10.1080/01431161.2023.2243021</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/01431161.2023.2243021" target="_blank" >10.1080/01431161.2023.2243021</a>
Alternative languages
Result language
angličtina
Original language name
A practically feasible transfer learning method for deep-temporal urban change monitoring
Original language description
Neural networks have shown their potential to monitor urban changes with deep-temporal remote sensing data, which simultaneously considers a large number of observations within a given window. However, training these networks with supervision is a challenge due to the low availability of third-party sources with sufficient spatio-temporal resolution to label each window individually. To remedy this problem, we developed a novel approach utilizing transfer learning (TL) on a set of deep-temporal windows. We demonstrate that labelling of multiple windows simultaneously can be practically viable, even with a low amount of high spatial resolution third-party data. The overall process provides a trade-off between labour resources and the ability to train a network on existing systems, despite its intensive memory requirements. As a demonstration, an existing previously trained (pre-trained) network was used to transfer knowledge to a new target location. We demonstrate our method with combined Sentinel 1 and 2 observations for the area of Liège (Belgium) for the time period spanning 2017-2020. This is underpinned by our use of common metrics in machine learning and remote sensing, and in our discussion of selected examples. Three independent transfers of the same pre-trained model and their combination were carried out, all of which showed an improvement in terms of these metrics.
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
10200 - Computer and information sciences
Result continuities
Project
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Continuities
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Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Remote Sensing
ISSN
0143-1161
e-ISSN
1366-5901
Volume of the periodical
44
Issue of the periodical within the volume
17
Country of publishing house
US - UNITED STATES
Number of pages
35
Pages from-to
5172-5206
UT code for WoS article
001054398700001
EID of the result in the Scopus database
2-s2.0-85169299359