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Decision fusion with multiple spatial supports by conditional random fields
Devis Tuia, Michele Volpi, Gabriele Moser
Classification of remotely sensed images into land cover or land use is highly dependent on geographical information at least at two levels. First, land cover classes are observed…
Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images
Michele Volpi, Devis Tuia
When approaching the semantic segmentation of overhead imagery in the decimeter spatial resolution range, successful strategies usually combine powerful methods to learn the visual…
Scale equivariance in CNNs with vector fields
Diego Marcos, Benjamin Kellenberger, Sylvain Lobry +1
We study the effect of injecting local scale equivariance into Convolutional Neural Networks. This is done by applying each convolutional filter at multiple scales. The output is a…
Change Detection between Multimodal Remote Sensing Data Using Siamese CNN
Zhenchao Zhang, George Vosselman, Markus Gerke +2
Detecting topographic changes in the urban environment has always been an important task for urban planning and monitoring. In practice, remote sensing data are often available in…
Detecting Mammals in UAV Images: Best Practices to address a substantially Imbalanced Dataset with Deep Learning
Benjamin Kellenberger, Diego Marcos, Devis Tuia
Knowledge over the number of animals in large wildlife reserves is a vital necessity for park rangers in their efforts to protect endangered species. Manual animal censuses are dan…
DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
Ilke Demir, Krzysztof Koperski, David Lindenbaum +6
We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite i…