Spatial co-location patterns are subsets of spatial features usually located together in geographic space. Recent literature has provided different approaches to discover co-location patterns over point spatial data. However, most approaches consider the neighborhood relationship among spatial objects as binary and are mainly designed for point spatial features, thus are not appropriate for extended spatial features such as line strings and polygons, the neighborhood relationship among which is naturally continuous. This paper adopts a buffer-based model for measuring the spatial relationship of extended objects and mining co-location patterns. While the buffer-based model has several advantages for extended spatial features, it involves high computational complexity due to the expensive buffer-level overlay operation. To tackle this challenge, we introduce a coarse-level co-location mining framework, which follows a filter-and-refine paradigm. Within the framework, we develop a serious of rigorous upper bounds based on geometric property and progressively prune search space with these upper bounds. Moreover, we develop a join-less schema to further reduce computation cost of size-k(k>2k>2) co-location patterns. Finally, we conduct experiments with large-scale spatial data to validate the efficiency of the developed algorithms against several state-of-art methods. All experimental results demonstrate the superiority of our methods.