Geospatial data constitute a considerable part of Semantic Web data, but at the moment, its sources are not sufficiently interlinked with topological relations in the Linked Open Data cloud. Geospatial Interlinking aims to cover this gap through space tiling techniques, which significantly restrict the search space. Yet, the state-of-the-art techniques operate exclusively in a batch manner that produces results only after processing the entire input datasets. In each run, they are also restricted to searching for an individual topological relation, even though most operations are common for the 10 main relations. In this work, we address both issues: we introduce a batch algorithm that simultaneously computes all topological relations and define the task of Progressive Geospatial Interlinking, which produces results in a pay-as-you-go manner when the available computational or temporal resources are limited. We propose two progressive algorithms and explain how they can be adapted to massive parallelization with Apache Spark. We conduct a thorough experimental study over a six large, real datasets, demonstrating the superiority of our techniques over the current state-of-the-art.