Land use/ land cover (LULC) datasets derived from remote sensing are widely used in geospatial applications for environmental management, natural capital assessment, and spatial planning. However, their spatial resolution, thematic consistency, and accuracy are often insufficient for complex analyses including landscape connectivity, fragmentation metrics and spatial inequality assessments. This paper presents Data4Land, an open-source Python-based workflow that systematically enriches land use/land cover datasets with auxiliary vector data, such as OpenStreetMap and the World Database on Protected Areas. The tool is demonstrated for two contrasting case study areas, using multi-temporal LULC time series: Catalonia in Spain (2012-2022) and Northern England (2020-2023). Integrating road, railway, watercourse and protected area features substantially changed habitat connectivity indices at multiple scales. Enrichment accuracy was validated against Ordnance Survey vector data using a confusion matrix approach. Data4Land is distributed as a modular Python package with Docker containerisation and is freely available on GitHub.