26 citations · 34 across the 4 of their papers we have counts for
5 papers
An evaluation of deep learning models for predicting water depth evolution in urban floods
Stefania Russo, Nathanaël Perraudin, Steven Stalder +4
In this technical report we compare different deep learning models for prediction of water depth rasters at high spatial resolution. Efficient, accurate, and fast methods for water…
Learning Graph Regularisation for Guided Super-Resolution
Riccardo de Lutio, Alexander Becker, Stefano D'Aronco +3
We introduce a novel formulation for guided super-resolution. Its core is a differentiable optimisation layer that operates on a learned affinity graph. The learned graph potential…
Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Deep Ensembles
Alexander Becker, Stefania Russo, Stefano Puliti +3
Monitoring and managing Earth's forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in…
Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data
Stefania Russo, Andy Disch, Frank Blumensaat +1
Due to the growing amount of data from in-situ sensors in wastewater systems, it becomes necessary to automatically identify abnormal behaviours and ensure high data quality. This…
Automatic dimensionality selection for principal component analysis models with the ignorance score
Stefania Russo, Guangyu Li, Kris Villez
Principal component analysis (PCA) is by far the most widespread tool for unsupervised learning with high-dimensional data sets. Its application is popularly studied for the purpos…