Conference Agenda

Accurate land cover classification requires both spatial details and temporal information of remote sensing data. While publicly available satellite image time series (SITS) offer short revisit times, they suffer from limited spatial resolution. In contrast, aerial imagery provides fine-grained spatial details, but its temporal coverage is limited. Thus, combining data from those sensors is of interest as their properties are complementary w.r.t. the problem domain. However, the large gap in spatial resolution between these two sensors makes their integration challenging. Generating super-resolution-SITS (SR-SITS) before fusion can help to reduce this gap. In this work, we propose a new approach that integrates diffusion models for generating SR-SITS into a method for the joint pixel-wise classification of aerial and SITS data. Specifically, we employ a diffusion model to generate SR-SITS at an intermediate resolution from the raw SITS and aerial imagery of the same observed area. The SR-SITS are temporally encoded and fused with the aerial features using a cross attention module to produce pixel-wise classification at the geometrical resolution of aerial image. Experimental results on the existing FLAIR benchmark dataset indicate that our approach achieves state-of-the-art results, with a mean Intersection over Union score of 64.0% and an overall accuracy of 76.6%.

We present Sky-NeRF, a novel method for cloud topography estimation based on Dynamic Neural Radiance Fields. Similar to NeRF, we propose to model the 3D structure of clouds as a radiance field, encoded in the parameters of a neural representation. Our goal is to reconstruct the 3D geometry, appearance, and motion of the cloud using a stereo-video of high-resolution top of the atmosphere radiance images. In this paper, we evaluate a novel way of modeling the dynamic behavior of clouds, with the goal of extracting added-value physical information regarding the cloud such as advection speed and direction, velocity field and cloud trajectories. We investigate how to include a simple physical prior, advection, into the learning system and evaluate its impact. Our results show that Sky-NeRF is able to provide a more complete 4D reconstruction than traditional stereo-matching-based algorithms. Moreover, thanks to a physics-based interpolation, Sky-NeRF is able to generate coherent new images from unseen viewing angles, and at any time between the observed frames.

3D topographic change estimation is a fundamental task for understanding Earth surface dynamics in fields of photogrammetry and laser scanning. However, at the current state of research, it is still challenging to accurately separate and quantify various components of topographic surface changes (i.e., rigid spatial movement and non-rigid morphological deformation). In this paper, we conceptualize a surface change tensor to describe 3D surface change based on the displacement field, considering contribution of neighboring points to their center point on the surface. With this concept, we design a new method that is able to quantitatively separate rigid and non-rigid topographic change components from the mixed topographic change. Experiments on synthetic datasets demonstrate that our method is accurate and robust to quantify rigid and non-rigid surface changes, with superiority to the baseline method (M3C2). Additionally, real-world experiments on 3D point clouds collected at four epochs show the effectiveness of the proposed method for monitoring topographic dynamics and identifying geomorphological processes in complex large-scale mountain environments.

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.

Spatiotemporal analysis of geospatial time series data has gained increasing attention with the emergence of 4D point clouds and automatic acquisition technologies such as permanent laser scanning (PLS), time-lapse photogrammetry, and uncrewed aerial vehicle (UAV) platforms, enabling near-continuous monitoring of Earth surface dynamics for change detection and process characterization. However, facing massive data volumes through the temporal domain, current topographic data curation practices often rely on empirically determined data processing and management, which may significantly affect reusability, interoperability, and hence processing efficiency due to the absence or heterogeneous nature of metadata. The need for standardized approaches to manage time-dependent metadata has become critical as the demands for sharing data and reproducing analysis across tools and application domains increase. We propose a topographic 4D extension (topo4d) to the SpatioTemporal Asset Catalog (STAC) framework, which provides an open and extensible specification for automatic metadata curation and FAIR data management practices. This paper demonstrates how the topo4d extension facilitates the interoperability and reusability of 4D datasets and presents the corresponding metadata curation workflows applied to two real-world environmental monitoring applications.