  Land cover (LC) segmentation plays a critical role in various applications,
including environmental analysis and natural disaster management. However,
generating accurate LC maps is a complex and time-consuming task that requires
the expertise of multiple annotators and regular updates to account for
environmental changes. In this work, we introduce SPADA, a framework for fuel
map delineation that addresses the challenges associated with LC segmentation
using sparse annotations and domain adaptation techniques for semantic
segmentation. Performance evaluations using reliable ground truths, such as
LUCAS and Urban Atlas, demonstrate the technique's effectiveness. SPADA
outperforms state-of-the-art semantic segmentation approaches as well as
third-party products, achieving a mean Intersection over Union (IoU) score of
42.86 and an F1 score of 67.93 on Urban Atlas and LUCAS, respectively.
