Understanding coastal zone dynamics covering large spatial and dense temporal scales is paramount to understand and mitigate emerging coastal issues relating to climatological changes. However, the uncertainties on the coastal zone risks are still too large to predict effectively impacts on an overall increasing coastal population. Prediction, by deploying numerical models for waves, tide, and currents in the coastal zone, requires accurate and up-to-date bottom boundary conditions (bathymetry) to simulate coastal hydrodynamics accurately, let alone predict morphological change. There are three main approaches for estimating coastal bathymetry using satellite sensors: 1) linking water depth to a spectral response (colour), 2) underwater photogrammetry and 3) linking wave kinematics to a water depth. Here, we present S2Shores (Satellite to Shores), a python library that detects and extracts local wave parameters (geometry and kinematics) to derive coastal bathymetry.