4 papers · 1 filter
GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation
Gaetano Chiriaco, Luca Barco, Andrea Bragagnolo +2
Geospatial foundation models aim to learn representations that transfer across regions and sensors, yet evaluating them on specific tasks requires large, high-quality, multi-modal…
Turin3D: Evaluating Adaptation Strategies under Label Scarcity in Urban LiDAR Segmentation with Semi-Supervised Techniques
Luca Barco, Giacomo Blanco, Gaetano Chiriaco +6
3D semantic segmentation plays a critical role in urban modelling, enabling detailed understanding and mapping of city environments. In this paper, we introduce Turin3D: a new aeri…
FMARS: Annotating Remote Sensing Images for Disaster Management using Foundation Models
Edoardo Arnaudo, Jacopo Lungo Vaschetti, Lorenzo Innocenti +4
Very-High Resolution (VHR) remote sensing imagery is increasingly accessible, but often lacks annotations for effective machine learning applications. Recent foundation models like…
Rapid Wildfire Hotspot Detection Using Self-Supervised Learning on Temporal Remote Sensing Data
Luca Barco, Angelica Urbanelli, Claudio Rossi
Rapid detection and well-timed intervention are essential to mitigate the impacts of wildfires. Leveraging remote sensed data from satellite networks and advanced AI models to auto…