Developed and integrated a site classification feature for the Pandora Converter within the JCSDA-internal/ioda-converters repository, enabling the enrichment of Pandora observation data with urban context. Leveraging Python, Pandas, and data processing techniques, the solution reads a site classification CSV and assigns both urban classification and percentage urban values to each observation based on geographic proximity. This approach enhances the contextual information available in the output data, supporting more informed downstream analytics. The work was delivered as a complete end-to-end feature in a single commit, focusing on robust data conversion and processing without introducing new bug fixes during the period.