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Found 5 papers, 1 papers with code
An evaluation of deep learning models for predicting water depth evolution in urban floods
In this technical report we compare different deep learning models for prediction of water depth rasters at high spatial resolution.
Learning Graph Regularisation for Guided Super-Resolution
With the decision to employ the source as a constraint rather than only as an input to the prediction, our method differs from state-of-the-art deep architectures for guided super-resolution, which produce targets that, when downsampled, will only approximately reproduce the source.
Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Deep Ensembles
To demonstrate scalability, we provide Norway-wide maps for the five forest structure variables.
Digital Taxonomist: Identifying Plant Species in Community Scientists' Photographs
Automatic identification of plant specimens from amateur photographs could improve species range maps, thus supporting ecosystems research as well as conservation efforts.
Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data
We discuss the results and the challenge of labelling anomalies in complex time series.
