Spatiotemporal reconstruction of 4D point clouds at different time scales through implicit neural representations for topographic monitoring applications

Abstract. Monitoring surface change in dynamic environments is essential to preserve the integrity of human infrastructure and livelihoods from natural hazard consequences. With the advent of 4D remote sensing, near-continuous monitoring of dynamic scenes is unlocked. However, the unordered and irregular nature of point clouds, compounded by temporally variable occlusions and diverse acquisition conditions, hinders the accurate analysis of highly information-rich 4D data. This work addresses the challenge of irregular spatiotemporal sampling in time series of 3D point clouds for the case study of a dynamic sandy beach at different time scales. We explore the use of implicit neural representations (INRs) to model 4D data as continuous spatiotemporal functions that are optimised to estimate the beach topography continuously through space and time. By comparing four model variants and assessing their performance to reconstruct spatially and temporally subsampled data, we evaluate the applicability of INRs to high-frequency topographic monitoring, especially in the context of 4D change analysis. Our results show the ability to reconstruct missing epochs from time series of 3D point clouds with centimetric to decimetric accuracy at time scales ranging from seasonal to daily observations. Our findings highlight the importance of hyperparameter tuning to enable the capture of local details in complex spatiotemporal datasets. Through this, our work lays the foundation for continuous spatiotemporal representation of dynamic scenes, supporting a potentially broad range of change analysis applications.