We present a method for refining photometric redshift galaxy catalogs based on a comparison of their color-space matching with overlapping spectroscopic calibration data. We focus on cases where photometric redshifts (photo-$z$) are estimated empirically. Identifying galaxies that are poorly represented in spectroscopic data is crucial, as their photo-$z$ may be unreliable due to extrapolation beyond the training sample. Our approach uses a self-organizing map (SOM) to project a multidimensional parameter space of magnitudes and colors onto a 2D manifold, allowing us to analyze the resulting patterns as a function of various galaxy properties. Using SOM, we compared the Kilo-Degree Survey's bright galaxy sample (KiDS-Bright), limited to $r&lt;20$ mag, with various spectroscopic samples, including the Galaxy And Mass Assembly (GAMA). Our analysis reveals that GAMA tends to underrepresent KiDS-Bright at its faintest ($r and highest-redshift ($z ranges; however, no strong trends are seen in terms of color or stellar mass. By incorporating additional spectroscopic data from the SDSS, 2dF, and early DESI, we identified SOM cells where the photo-$z$ values are estimated suboptimally. We derived a set of SOM-based criteria to refine the photometric sample and improve photo-$z$ statistics. For the KiDS-Bright sample, this improvement is modest, namely, it excludes the least represented 20&lt;!PCT!&gt; of the sample reduces photo-$z$ scatter by less than 10&lt;!PCT!&gt;. We conclude that GAMA, used for KiDS-Bright photo-$z$ training, is sufficiently representative for reliable redshift estimation across most of the color space. Future spectroscopic data from surveys such as DESI should be better suited for exploiting the full improvement potential of our method.

Enhancing photometric redshift catalogs through color-space analysis: Application to KiDS-bright galaxies