  Semantic segmentation is crucial in remote sensing, where high-resolution
satellite images are segmented into meaningful regions. Recent advancements in
deep learning have significantly improved satellite image segmentation.
However, most of these methods are typically trained in fully supervised
settings that require high-quality pixel-level annotations, which are expensive
and time-consuming to obtain. In this work, we present a weakly supervised
learning algorithm to train semantic segmentation algorithms that only rely on
query point annotations instead of full mask labels. Our proposed approach
performs accurate semantic segmentation and improves efficiency by
significantly reducing the cost and time required for manual annotation.
Specifically, we generate superpixels and extend the query point labels into
those superpixels that group similar meaningful semantics. Then, we train
semantic segmentation models supervised with images partially labeled with the
superpixel pseudo-labels. We benchmark our weakly supervised training approach
on an aerial image dataset and different semantic segmentation architectures,
showing that we can reach competitive performance compared to fully supervised
training while reducing the annotation effort. The code of our proposed
approach is publicly available at: https://github.com/santiago2205/LSSQPS.
