Research & Software 4D Objects-By-Change

Change Analysis of Natural Surfaces using 3D Time Series

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Objective

Geographic observation benefits from the increasing availability of time series of 3D geospatial data, which allow analysis of change processes at high temporal detail and over extensive periods. Time series-based change analysis can integrate the history of surface change by performing spatiotemporal segmentation with the concept of 4D objects-by-change (4D-OBC) (Anders et al., 2020). The method identifies areas in the scene where surfaces change similarly over time, within sub-periods in the time series at neighbouring locations.

Key features of time series-based change analysis are:

  • Removing the requirement to select and predefine periods for the analysis of changes
  • Extracting change forms at different (unknown) timing, change rates, durations of change processes, and persistence of (temporary) change forms
  • Separating spatially overlapping changes, which might be aggregated in bi-temporal change information of a scene

The time series-based approach thereby enables a generic extraction of surface changes in their varying spatial and temporal extents from large and dense 4D geospatial data (Anders et al., 2021).

EXAMPLE USE: COASTAL MONITORING

We applied fully automatic spatiotemporal segmentation for the use case of coastal monitoring at a sandy beach in The Netherlands (CoastScan project by TU Delft). A time series of hourly 3D point clouds was acquired by permanent terrestrial laser scanning (TLS) over a period of five months (~4,000 epochs). The analysis of around 15 billion laser points in the entire observation period revealed more than 2,000 4D objects-by-change (4D-OBCs). These 4D-OBCs represent temporary accumulation or erosion of sand that occurred in different locations at varying magnitudes and across various time periods. The study is presented with all methodological details in Anders et al. 2021.

height change over time

RELATED PUBLICATIONS

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CONNECT

TEAM

Katharina Anders (TU Munich, DE)
Bernhard Höfle
Lukas Winiwarter (University of Innsbruck, AT)

PROJECT PARTNER

Dr. R. C. (Roderik) Lindenbergh