Imaging the Earth’s surface and reconstructing its topography to study the landscape and (sub-)surface processes have advanced rapidly over the past two decades, sometimes separately within different geoscientific disciplines. Developments in satellite, airborne, uncrewed aerial vehicle (UAV), and ground-based sensing, alongside advances in photogrammetry, Structure-from-Motion (SfM), LiDAR, computer vision, and automated processing, have transformed our ability to reconstruct and monitor the Earth’s surface in 2D, 3D, and 4D (time series).
The spatial, temporal, and spectral resolutions of current measurements offer the opportunity to study the surface evolution from local to regional scale at unprecedented detail. Equipped with optimized workflows, either digitizing analogue data, such as historical aerial photographs, or processing near-continuous records of topographic change, geoscientists now have a variety of tools to better understand our rapidly changing environment and disentangle anthropogenic from natural drivers.
Yet, challenges still exist at both methodological and application levels. How can reliable data be acquired in harsh, remote, or non-ideal environments? How can observations from different time periods, scales, and platforms be reconstructed, co-registered, and compared? How should measurement precision, systematic errors, and uncertainty be quantified and propagated into estimates of surface change? And how can computer-vision, machine-learning, and automation extract meaningful information from large and complex 3D and 4D datasets?
In this session, we invite contributions from across the geosciences (e.g., geomorphology, glaciology, volcanology, hydrology, soil sciences) on the opportunities, limitations, and challenges of surface imaging, topographic reconstruction, and change analysis across diverse environments.
We especially welcome contributions addressing (1) novel data acquisition in complex, remote, or rapidly changing environments; (2) image and point-cloud processing, including image matching, reconstruction, co-registration, and correction of sensor distortions; (3) precision and uncertainty assessment, including systematic errors; (4) construction and analysis of multitemporal and 4D datasets, from historical records to high-frequency and near-continuous observations; (5) automated processing, analysis, and information extraction from large and complex datasets; and (6) innovative applications in geosciences.