Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

<Previous Article In Volume
Next Article In Volume>

Sematic Segmentation Of Land Cover Dataset

Authors
M. Nikhil Sai1, A. Rahul1, G. G. V. Praneeth Kumar1, P. Visalakshi1, *
1Department of NWC, SRM Institute of Science and Technology, Chennai, India
*Corresponding author. Email: visalakp@srmist.edu.in
Corresponding Author
P. Visalakshi
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_99How to use a DOI?
Keywords
Tools and Frameworks; Datasets and Evaluation; Optimization and Loss Functions; U-Net; and Image Processing
Abstract

A number of disciplines rely heavily on satellite images, including those dealing with land use analysis, urban planning, agricultural monitoring, and the detection of environmental change. Nevertheless, because of differences in scale, illumination, and weather, distinguishing land features from satellite photos is a difficult undertaking. Convolutional neural networks and other deep learning techniques have lately become powerful image segmentation tools. This research applies the U-Net architecture to satellite land segmentation, a subset of convolutional neural networks (CNNs) designed for use in biomedical image segmentation. The research delves deeply into the U-Net architecture, exploring its adaptations for satellite photography, evaluating its performance on various datasets, and discussing its advantages and limitations in land segmentation tasks.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_99How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - M. Nikhil Sai
AU  - A. Rahul
AU  - G. G. V. Praneeth Kumar
AU  - P. Visalakshi
PY  - 2025
DA  - 2025/11/04
TI  - Sematic Segmentation Of Land Cover Dataset
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1189
EP  - 1203
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_99
DO  - 10.2991/978-94-6463-858-5_99
ID  - Sai2025
ER  -