Lightweight multiscale information aggregation network for land cover land use semantic segmentation from remote sensing images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F25%3A10258603" target="_blank" >RIV/61989100:27730/25:10258603 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41598-025-07908-4" target="_blank" >https://www.nature.com/articles/s41598-025-07908-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-025-07908-4" target="_blank" >10.1038/s41598-025-07908-4</a>

Result language

angličtina

Original language name

Lightweight multiscale information aggregation network for land cover land use semantic segmentation from remote sensing images

Original language description

Land Cover and Land Use (LCLU) segmentation plays a fundamental role in various remote sensing applications, including environmental monitoring, urban planning, and disaster management. Traditional models often face limitations in real-time processing and deployment on resource-constrained devices due to their high computational requirements. This paper presents a lightweight neural network designed to address these challenges by integrating dense dilated convolutions with pyramid depthwise convolutions for multiscale feature extraction. The proposed encoder-decoder architecture utilizes dense connections to aggregate spatial and contextual information across different resolutions, enhancing segmentation accuracy while minimizing computational overhead. The model&apos;s performance was rigorously evaluated using the NITRDrone and UDD6 datasets, demonstrating a segmentation accuracy of 94.8%, with a significantly reduced parameter count compared to state-of-the-art methods. The compact design of the network facilitates its implementation on low-power devices, enabling real-time LCLU analysis across diverse environmental conditions. This work underscores the potential of lightweight neural networks to advance remote sensing image processing, offering scalable and efficient solutions for practical applications in geospatial analysis.

Czech name

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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

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OECD FORD branch

10700 - Other natural sciences

Project

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Continuities

O - Projekt operacniho programu

Publication year

2025

Confidentiality

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

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Volume of the periodical

15

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

12

Pages from-to

nestránkováno

UT code for WoS article

001553526200007

EID of the result in the Scopus database

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