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Found 5 papers, 3 papers with code
Spotting Virus from Satellites: Modeling the Circulation of West Nile Virus Through Graph Neural Networks
In this paper, we seek to predict WNV circulation by feeding Deep Neural Networks (DNNs) with satellite images, which have been extensively shown to hold environmental and climatic features.
Generalising via Meta-Examples for Continual Learning in the Wild
Future deep learning systems call for techniques that can deal with the evolving nature of temporal data and scarcity of annotations when new problems occur.
Few-Shot Unsupervised Continual Learning through Meta-Examples
In real-world applications, data do not reflect the ones commonly used for neural networks training, since they are usually few, unlabeled and can be available as a stream.
The color out of space: learning self-supervised representations for Earth Observation imagery
We conduct experiments on land cover classification (BigEarthNet) and West Nile Virus detection, showing that colorization is a solid pretext task for training a feature extractor.
Spotting insects from satellites: modeling the presence of Culicoides imicola through Deep CNNs
Nowadays, Vector-Borne Diseases (VBDs) raise a severe threat for public health, accounting for a considerable amount of human illnesses.
