Geospatial Interlinking constitutes a crucial data integration task that associates pairs of geometries with topological relations. Its high computational cost, though, scales poorly to voluminous datasets. Progressive methods were recently proposed to reduce this cost by sacrificing recall to an affordable extent. They operate in a learning-free manner that relies on mere heuristics, which can be conservative (i.e., retaining too many unrelated pairs) or aggressive (i.e., discarding too many related pairs). In this work, we extend them with Supervised Scheduling, a quick and principled way of defining the processing order of the candidate geometry pairs that are likely to be topologically related, based on their classification probability. Our approach leverages generic features with low extraction cost but high discriminatory power. We integrate Supervised Scheduling into a progressive end-to-end algorithm that automatically labels the required training instances at a low computational cost. Thorough experiments verify the high performance and robustness of our features as well as the limited size of the training set that suffices for learning an accurate classification model. Our experiments also verify the superior performance of our approach in comparison to existing learning-free ones over five real, large datasets.