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Remote sensing applications often require the extraction of a small number of task-critical geospatial elements under severe class imbalance. This setting is challenging because dominant categories occupy most pixels, while targets of interest may be sparse, fragmented, or semantically ambiguous. In this study, we build our analysis on a remote sensing dataset consisting of 10,482 pixel-wise annotated RGB image tiles covering 14 semantic categories with pronounced long-tailed characteristics. Based on this dataset, we conduct an application-oriented comparative study of eight representative segmentation models on four key geospatial element categories with different sparsity levels and visual properties. Quantitative evaluation is performed using Intersection over Union, Precision, Recall, and F1-score, and representative qualitative cases are examined to analyze model behavior. An additional comparison with conventional multi-class segmentation shows that the target-oriented setting should be understood not as a universally superior alternative, but as a complementary application-oriented setting for analyzing target-specific delineation behavior under severe imbalance. The results further indicate that segmentation difficulty cannot be explained by target proportion alone, but is jointly associated with target morphology, spatial fragmentation, and semantic similarity to surrounding categories. These findings provide practical guidance for segmentation model selection in highly imbalanced remote sensing applications.
Jin et al. (Wed,) studied this question.