Rethinking Landslide Mapping as Coarse Prediction Under Weakly Defined Supervision

Recent deep learning work usually treats landslide mapping as full-resolution semantic segmentation and seeks better performance through stronger backbones, multimodal fusion, or boundary refinement. Yet landslide supervision is often weakly defined: masks can be incomplete, visually interpreted, boundary-ambiguous, and misaligned with the region-level decisions that matter in practice. We revisit the task from a simpler angle: change only the output resolution. Without adding a new architecture, decoder, or inference stage, we vary the spatial granularity of labels and predictions and study how this alone changes performance. Across three datasets, moderate coarse prediction can improve region-level detection, although the gain is dataset-dependent and not monotonic with resolution. To separate useful coarse prediction from trivial task easing, we evaluate direct and pooled fine-resolution predictions on common grids, alongside label-faithfulness and anti-triviality diagnostics. The results point to a dataset-dependent reliable resolution sweet spot and suggest that output granularity is a practical design variable for landslide mapping rather than a minor implementation choice.