Land cover mapping with Sentinel-2 imagery using deep learning semantic segmentation models

verfasst von

Oleksandr Honcharov, Viktoriia Hnatushenko

Abstract

Land cover mapping is essential for environmental monitoring and evaluating the effects of human activities. Recent studies have demonstrated the effective application of particular deep learning models for tasks such as wetland mapping. Nonetheless, it is still ambiguous which advanced models developed for natural images are most appropriate for remote sensing data. This study focuses on the segmentation of agricultural fields using satellite imagery to distinguish between cultivated and non-cultivated areas. We employed Sentinel-2 imagery obtained during the summer of 2023 in Ukraine, illustrating the nation's varied land cover. The models were trained to differentiate among three principal categories: water, fields, and background. We chose and optimised five advanced semantic segmentation models, each embodying distinct methodological methods derived from U-Net. Upon examination, all models exhibited robust performance, with total accuracy spanning from 80% to 89.2%. The highest-performing models were U-Net with Residual Blocks and U-Net with Residual Blocks and Batch Normalisation, whereas U-Net with LeakyReLU Activation exhibited much quicker inference times. The findings suggest that semantic segmentation algorithms are highly effective for efficient land cover mapping utilising multispectral satellite images and establish a dependable benchmark for assessing future advancements in this domain.

Organisationseinheit(en)

Institut für Photogrammetrie und Geoinformation

Externe Organisation(en)

Ukrainian State University of Science and Technologies

Typ

Aufsatz in Konferenzband

Seiten

1-18

Anzahl der Seiten

18

Publikationsdatum

01.02.2025

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Allgemeine Computerwissenschaft

Ziele für nachhaltige Entwicklung

SDG 15 – Lebensraum Land

Elektronische Version(en)

https://ceur-ws.org/Vol-3909/Paper_1.pdf (Zugang: Offen)