Raster–vector clipping is a fundamental operation in geospatial analysis. In practice, however, it is typically implemented as a binary rule that assigns pixels as either fully inside or outside a target geometry. Although widely used, clipping is rarely defined as a generic spatial operator with explicit inclusion semantics. This paper introduces a Fractional Raster–Vector Clipping Operator that models pixel inclusion through proportional areal overlap and formalizes clipping as a mathematically specified operator. The formulation is independent of any specific software environment and is accompanied by a generic algorithm. The algorithm avoids costly universal containment tests by restricting explicit geometric evaluation to a boundary region derived from pixel dimensions. The operator is implemented natively in PostgreSQL/PostGIS and evaluated using Denmark’s national land-cover dataset (Basemap04, 2021) aggregated across multiple spatial resolutions. The results show that conventional binary clipping produces systematic and resolutiondependent underestimation relative to the fractional reference. Explicit boundary semantics therefore establish a consistent and resolution-aware basis for raster–vector aggregation in multi-scale spatial analysis.