Many emerging applications benefit from the extraction of geospatial data specified at different resolutions for vieweing purposes. Data must also be topologically accurate and up-to-date as it often represents real-world changing phenomena. Current multiresolution schemes use complex opaque data types, which limit the capacity for in-database object manipulation. By using z-values and B⁺trees to support multiresolution retrieval, objects are fragmented in such a way that updates to objects or object parts are executed using standard SQL statements as opposed to procedural functions. Our approach is compared to a current model, using complex data types indexed under a 3D R-tree, and shows better performance for retrieval over realistic window sizes and data loads. Updates with the R-tree are slower and preclude the feasibility of its use in time-critical applications whereas, predictably, projecting the issue to a 1-dimensional index allows constant updates using z-values to be implemented more efficiently.