Automating global landslide detection with heterogeneous ensemble deep-learning classification

no code implementations • 12 Sep 2023 • Alexandra Jarna Ganerød, Gabriele Franch, Erin Lindsay, Martina Calovi

This study addresses some of these limitations by using a diverse global landslide dataset, using different segmentation models, such as Unet, Linknet, PSP-Net, PAN, and DeepLab and based on their performances, building an ensemble model.

Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification

1 code implementation • 25 Apr 2023 • Jussi Leinonen, Ulrich Hamann, Daniele Nerini, Urs Germann, Gabriele Franch

We benchmark it against the GAN-based Deep Generative Models of Rainfall (DGMR) and a statistical model, PySTEPS.

Image Generation Uncertainty Quantification

MASS-UMAP: Fast and accurate analog ensemble search in weather radar archive

no code implementations • 1 Oct 2019 • Gabriele Franch, Giuseppe Jurman, Luca Coviello, Marta Pendesini, Cesare Furlanello

The most challenging aspect of using this approach in the context of operational radar applications is to be able to perform a fast and accurate search for similar spatiotemporal precipitation patterns in a large archive of historical records.

Dimensionality Reduction Retrieval +3