1 citations · 2 across the 10 of their papers we have counts for
11 papers
Data-driven Prediction of Satellite-observed Avalanche Activity from Snowpack Simulations
Jakob Grahn, Jakob Grah, Filippo Maria Bianchi +2
Avalanche forecasting requires knowledge of snowpack conditions and recent avalanche activity, but field observations are sparse across large mountain regions. We explore whether S…
Weakly Supervised Polar Low Segmentation in Sentinel-1 SAR Imagery
Andrea Federici, Jakob Grahn, Giacomo Boracchi +1
Polar lows are intense maritime cyclones that form rapidly at high latitudes. Deep learning can detect them in Synthetic Aperture Radar (SAR) imagery, but pixel-level segmentation…
Hierarchical Pooling for Sheaf Neural Networks
Dionisia Naddeo, Carlo Abate, Pietro Liò +2
Sheaf Neural Networks (SNNs) generalize Graph Neural Networks (GNNs) by replacing scalar node signals with stalk-valued signals and by using restriction maps to measure compatibili…
A Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks
Nicolas Tremblay, Benjamin Ricaud, Filippo Maria Bianchi
We propose the Tikhonov layer, a graph neural network layer that is interpretable by design: once trained, its learned parameters directly reveal which node features and which aspe…
Promptable Foundation Models for SAR Remote Sensing: Adapting the Segment Anything Model for Snow Avalanche Segmentation
Riccardo Gelato, Carlo Sgaravatti, Jakob Grahn +2
Remote sensing solutions for avalanche segmentation and mapping are key to supporting risk forecasting and mitigation in mountain regions. Synthetic Aperture Radar (SAR) imagery fr…
Torch Geometric Pool: the PyTorch library for pooling in Graph Neural Networks
Carlo Abate, Ivan Marisca, Filippo Maria Bianchi
Torch Geometric Pool (tgp) is a pooling library built on top of PyTorch Geometric. Graph pooling methods differ in how they assign nodes to supernodes, how they handle batches, wha…