EGMSpy: an open-source Python toolkit for scalable data handling, classification, clustering, and visualisation of Copernicus EGMS InSAR data

The Copernicus European Ground Motion Service (EGMS) provides millimetre-accuracy line-of-sight displacement measurements for over five billion coherent scatterers across Europe, derived from Sentinel-1 SAR interferometry over the period 2015--2023. Despite the unprecedented spatial coverage and measurement density of this dataset, no open-source integrated toolchain exists for processing, classifying, clustering, and interactively visualising EGMS data at the regional to continental scale without resorting to subsampling or proprietary software. In this paper, we introduce EGMSpy, a fully open-source Python pipeline that ingests raw EGMS L2b CSV tiles, compresses them into a federated split-GeoParquet database (metadata and time series stored separately, linked by a unique point identifier), applies a physics-informed hybrid rule/GMM time series classifier to assign each point to a geophysical deformation class, groups spatially and dynamically coherent deforming areas using a velocity-weighted three-dimensional DBSCAN algorithm, and serves the entire dataset to an interactive Leaflet.js web viewer via a Flask/DuckDB REST bridge capable of sub-100\,ms viewport queries over hundreds of millions of points. The pipeline is demonstrated on the northern Italy EGMS L2b dataset (2019--2023), comprising 434 tile pairs (approx. 300 million coherent scatterers, 104GB on disk), processed on a single workstation. Internal quality control confirms that the toolkit's OLS-derived velocity features are consistent with the official EGMS pre-computed velocities (RMSE=0.096mm\yr^-1, bias=-0.001mmyr^-1, r$=0.9993, n$=99,751). All code is released under the MIT licence and is available at https://github.com/fcatani/EGMSpy (repository in preparation).