A drone-based framework for coral habitat mapping via weakly supervised segmentation

arXiv:2508.18958v2 Announce Type: replace-cross Abstract: Obtaining pixel-level annotations over large spatial extents remains a major bottleneck for deploying machine learning in ecological applications. Here we present a multi-scale weakly supervised semantic segmentation (WSSS) framework that enables training high-resolution segmentation models from dense, classification-based outputs. Our method combines fine-scale, multi-label predictions from underwater imagery with broad-coverage aerial data. We convert these point-level classifications into coarse supervision masks that can be used to

Source: arXiv cs.AI — read the full report at the original publisher.

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