Researchers from the Earth Observation Center (EOC) of the German Aerospace Center (DLR) in Oberpfaffenhofen, the University of Bonn, the University of the Bundeswehr Munich and our Earth Observation Research Cluster (EORC) of the University of Würzburg teamed up for a study on the remote sensing-based temporal disaggregation of building footprint data. The paper titled “Making footprints move: Temporal disaggregation of building footprint data using Sentinel-2 imagery and Bayesian deep learning” was just published in the Remote Sensing of Environment journal by Manuel Huber, Christian Geiß, Jessy Ribaira, Michael Schmitt and Hannes Taubenböck.

Here is the abstract of the paper: High-resolution building footprints from Overture, Google, Meta, and OpenStreetMap are essential for urban and environmental studies. However, these datasets often lack temporal metadata, limiting their utility for applications, that require spatio-temporal information, such as risk assessment, population estimation, and urbanization monitoring. This study presents a novel method for temporally disaggregating static building footprints by leveraging Sentinel-2 satellite imagery and a Bayesian U-Net segmentation model. The approach allows the assignment of time labels to individual building footprints and probabilistic uncertainty estimation. Beyond temporal labeling, disaggregation greatly improves label quality, boosting 𝑅2 from −0.10 to 0.80 for building count and from 0.74 to 0.89 for built-up area accuracy. Overall, the method robustly generalizes, enabling flexible temporal disaggregation of high-resolution building footprints with uncertainty estimates that indicate prediction trustworthiness.

Here is the link to the full paper: https://www.sciencedirect.com/science/article/pii/S0034425726001835