This paper presents a scalable open-source system for national-level impervious-surface mapping and monitoring that combines GRASS, HDF5 and Python-based machine-learning libraries. Continuous Sentinel-2 multispectral monitoring is paired with targeted very high resolution (VHR) orthophoto segmentation to efficiently detect and delineate land transformation. Sentinel-2 imagery is retrieved from the Microsoft Planetary Computer, organized into a GRASS space-time raster dataset and classified using TorchGeo models fine-tuned on the EuroSAT dataset. Persistent transitions toward impervious-related classes identify disturbance candidates, and these areas trigger semantic segmentation of corresponding orthophoto tiles using GPU-accelerated Random Forests in RAPIDS cuML. The resulting outputs are vectorized, enriched with registry data and disseminated through spatial database services.</p> <p>Applied to Slovenia for the 2020–2025 period, the system detected 99 km² of new impervious surfaces across a 20,271 km² study area, corresponding to 0.5 % land transformation (0.1 % annually). These results demonstrate that integrating continuous multispectral time-series analysis with event-driven VHR segmentation provides an efficient, reproducible and high-detail approach for operational land-change detection and environmental assessment.