In this paper, we study the problem of spatial link discovery (LD), focusing primarily on topological and proximity relations between spatial objects. The problem is timely due to the large number of sources that generate spatial data, including streaming sources (e.g., surveillance of moving objects) but also archival sources (such as static areas of interest). To address the problem of integrating data from such diverse sources, link discovery techniques are required to identify various spatial relations efficiently. Existing approaches typically adopt the filter and refine methodology by exploiting a blocking technique for effective filtering. In this paper, we present a new spatial LD technique, called MaskLink, that improves the effectiveness of the filtering step. We show that MaskLink outperforms the state-of-the-art algorithm for link discovery of topological relations, while also addressing some of its limitations, such as applicability for streaming data, low memory requirements, and parallelization. Furthermore, we show that MaskLink can be extended and generalized to the case of proximity-based link discovery, which has not been studied before for spatial data. Our empirical study demonstrates the superiority of MaskLink against the state-of-the-art in the case of topological relations, and its performance gain compared to a baseline technique in the case of proximity-based LD.