Field identification in low-resolution satellite images: a performance analysis of semantic and instance segmentation models on the fields of the world dataset

Abstract

This study investigates the challenges and performance of deep learning architectures for agricultural field segmentation. We used a large, globally distributed dataset with a vast variability in field to train and evaluate semantic and instance segmentation models. Specifically ResUNet for semantic segmentation and Mask R-CNN for instance segmentation. We then compared their results to baseline Unet models. ResUNet achieves strong results in the 2-class configuration, with 92% (macro) pixel accuracy and a (macro) IoU of 0.92, demonstrating robust fields identification. However, introducing a third, “boundary” class reduces (macro) IoU to 0.62, revealing that multi-class segmentation remains challenging in agricultural contexts. Mask R-CNN’s performance is more limited, achieving only an AP of 0.121 overall and 0.252 at IoU = 0.50. Part of this apparent underperformance comes from imperfect ground truth data, i.e. many “false positives” correspond to actual fields that are absent from the annotations. Nonetheless, the difficulty of handling irregular shapes and define fine boundaries remain major bottlenecks. Overall, ResUNet significantly improve the performance over the baseline Unet in both accuracy and speed. However, challenges related to class imbalance, imperfect annotations, boundary complexity, and resource constraints remain active areas for improvement. These findings highlight the need for better dataset curation in order to achieve better performances to segment agricultural landscapes.