Multi-stage semantic segmentation to map small and sparsely distributed habitats

Land cover (LC) maps are used extensively for nature conservation and landscape planning, but low spatial resolution and coarse LC schemes typically limit their applicability to large, broadly-defined habitats. In order to target smaller and more specific habitats, LC maps must be developed at high resolution and fine class detail, using methods that can handle strong class imbalance. Multi-stage semantic segmentation to map small and sparsely distributed habitatsIn this work, we present a new aerial photography data set with 12.5 cm ground resolution, annotated using a detailed, hierarchical land cover schema. We show that splitting up the semantic segmentation process into multiple stages critically improves the predictive performance, in particular by including the rare LC classes. We then apply this method to create a new LC map of the Peak District National Park (1439 km2), England, at 12.5 cm resolution.