Deep Residual UNet with Transfer Learning for Fine Grained Agricultural Land Segmentation
Journal of Smart and Sustainable Farming
Received On : 16 January 2026
Revised On : 26 February 2026
Accepted On : 03 March 2026
Published On : 18 March 2026
Volume 02, 2026
Pages : 033-042
Abstract
The accurate mapping of agricultural landscapes based on the high-resolution remote sensing images is a basic requirement of successful application of the policies of the precision farming and land management. This paper presents TL- ResUNet, which is an adapted U-Net network, that integrates a ResNet-50 encoder, supplemented with transfer learning methods, to be used in semantic segmentation in heterogeneous agricultural scenes. We used land-use data that were annotated according to the polygonal boundaries at a 0.5m spatial resolution thus accounting to the fragmented smallholder farming systems. The architecture combines the use of vegetation indices, attention, and dilated convolutional layers with the objective of enhancing the fine-scale crop boundary and sensitivity to the linearities. Empirical comparisons show that domain-specific pre-training is an effective approach that produces a significant increase in the Intersection over Union (IoU) measure, reaching as high as 81. The proposed model, therefore, is better than the state-of-the-art models in heterogeneous and mixed land-use environment.
Keywords
Agricultural Segmentation, Remote Sensing, Remotely Sensed Imagery, UNet, Transfer Learning, Land-Use Mapping.
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Author(s) thanks to University of Iceland for research lab and equipment support.
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© 2026 Agnar Alfons Ramel. The author(s) retain copyright of the work. The author(s) grant the Journal of Smart and Sustainable Farming (JSSF) and its publisher, Ansis Publications, the right of first publication and the right to identify itself as the original publisher of the article.
Cite this Article
Agnar Alfons Ramel, “Deep Residual UNet with Transfer Learning for Fine Grained Agricultural Land Segmentation”, Journal of Smart and Sustainable Farming, pp. 033-042, 2026, doi: 10.64026/JSSF/2026004.