Details
| Original language | English |
|---|---|
| Title of host publication | Information Technology and Implementation 2024 |
| Subtitle of host publication | IT&I 2024 |
| Pages | 1-18 |
| Number of pages | 18 |
| Publication status | Published - 1 Feb 2025 |
| Event | 11th International Scientific Conference "Information Technology and Implementation", IT and I 2024 - Kyiv, Ukraine Duration: 20 Nov 2024 → 21 Nov 2024 |
Publication series
| Name | CEUR workshop proceedings |
|---|---|
| Publisher | CEUR-WS |
| Volume | Vol-3909 |
| ISSN (Print) | 1613-0073 |
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.
Keywords
- agricultural lands, deep learning, satellite images, Semantic segmentation, U-Net architecture
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
Sustainable Development Goals
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Information Technology and Implementation 2024: IT&I 2024. 2025. p. 1-18 (CEUR workshop proceedings; Vol. Vol-3909).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Land cover mapping with Sentinel-2 imagery using deep learning semantic segmentation models
AU - Honcharov, Oleksandr
AU - Hnatushenko, Viktoriia
N1 - Publisher Copyright: © 2024 Copyright for this paper by its authors.
PY - 2025/2/1
Y1 - 2025/2/1
N2 - 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.
AB - 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.
KW - agricultural lands
KW - deep learning
KW - satellite images
KW - Semantic segmentation
KW - U-Net architecture
UR - http://www.scopus.com/inward/record.url?scp=85217281561&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85217281561
T3 - CEUR workshop proceedings
SP - 1
EP - 18
BT - Information Technology and Implementation 2024
T2 - 11th International Scientific Conference "Information Technology and Implementation", IT and I 2024
Y2 - 20 November 2024 through 21 November 2024
ER -