Geography
Document Type
Article
Abstract
Mapping agricultural fields using high-resolution satellite imagery and deep learning (DL) models has advanced significantly, even in regions with small, irregularly shaped fields. However, effective DL models often require large, expensive labeled datasets, which are typically limited to specific years or regions. This restricts the ability to create annual maps needed for agricultural monitoring, as changes in farming practices and environmental conditions cause domain shifts between years and locations. To address this, we focused on improving model generalization without relying on yearly labels through a holistic approach that integrates several techniques, including an area-based loss function, Tversky-focal loss (TFL), data augmentation, and the use of regularization techniques like dropout. Photometric augmentations helped encode invariance to brightness changes but also increased the incidence of false positives. The best results were achieved by combining photometric augmentation, TFL, and Monte Carlo dropout, although dropout alone led to more false negatives. Input normalization also played a key role, with the best results obtained when normalization statistics were calculated locally (per chip) across all bands. Our U-Net-based workflow successfully generated multi-year crop maps over large areas, outperforming the base model without photometric augmentation or MC-dropout by 17 IoU points.
Publication Title
Remote Sensing
Publication Date
2-2025
Volume
17
Issue
3
ISSN
2072-4292
DOI
10.3390/rs17030474
Keywords
cropland, deep learning, remote sensing, semantic segmentation
Repository Citation
Khallaghi, Sam; Abedi, Rahebeh; Ali Abou, Hanan; Alemohammad, Hamed; Dziedzorm Asipunu, Mary; Alatise, Ismail; Ha, Nguyen; Luo, Boka; Mai, Cat; Song, Lei; Olertey Wussah, Amos; and Xiong, Sitian, "Generalization Enhancement Strategies to Enable Cross-Year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples" (2025). Geography. 996.
https://commons.clarku.edu/faculty_geography/996
Cross Post Location
Student Publications
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Conditions
Khallaghi, S., Abedi, R., Ali, H. A., Alemohammad, H., Asipunu, M. D., Alatise, I., ... & Estes, L. D. (2024). Generalization enhancement strategies to enable cross-year cropland mapping with convolutional neural networks trained using historical samples. arXiv preprint arXiv:2408.06467.
