  As low-quality housing and in particular certain roof characteristics are
associated with an increased risk of malaria, classification of roof types
based on remote sensing imagery can support the assessment of malaria risk and
thereby help prevent the disease. To support research in this area, we release
the Nacala-Roof-Material dataset, which contains high-resolution drone images
from Mozambique with corresponding labels delineating houses and specifying
their roof types. The dataset defines a multi-task computer vision problem,
comprising object detection, classification, and segmentation. In addition, we
benchmarked various state-of-the-art approaches on the dataset. Canonical
U-Nets, YOLOv8, and a custom decoder on pretrained DINOv2 served as baselines.
We show that each of the methods has its advantages but none is superior on all
tasks, which highlights the potential of our dataset for future research in
multi-task learning. While the tasks are closely related, accurate segmentation
of objects does not necessarily imply accurate instance separation, and vice
versa. We address this general issue by introducing a variant of the deep
ordinal watershed (DOW) approach that additionally separates the interior of
objects, allowing for improved object delineation and separation. We show that
our DOW variant is a generic approach that improves the performance of both
U-Net and DINOv2 backbones, leading to a better trade-off between semantic
segmentation and instance segmentation.
