SOURCE-LINKED INTELLIGENCE

Multimodal multitAsk learninG for MultIsCale BATHYmetric mapping in shallow waters

CORDIS · observation · Publication date unknown

dedicated to satellite images, failing to address the challenges of shallow waters, being also inefficient for UAV images, preventing higher resolution results. MagicBathy will establish an advanced deep learning framework for low-cost shallow water mapping by developing a novel boundary-aware multitask, multiscale and multimodal learning approach for bathymetry and semantics together, exploiting single either UAV or satellite imagery. To overcome the domain gap, generalize and improve performance, self-supervised in-domain representation learning will be performed. To enhance the spatial resolution of low resolution satellite images and hence of the resulting bathymetric/semantic maps, a conditional generative adversarial network (cGAN)-based Super Resolution framework will be developed, dealing with the special challenges of shallow water imagery. Frameworks, models and results will be published in open access, enabling the rapid progress in shallow water mapping worldwide Remote Sensing, Deep Learning, Seabed mapping, Multitask learning, Semantic Segmentation, UAV and Satellite I

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

189687.36

unit

EUR