MagicBathy · Multimodal multitAsk learninG for MultIsCale BATHYmetric mapping in shallow waters
Horizon Europe — Marie Skłodowska-Curie Actions
- Duration
- 2023-02-01 → 2025-08-31
- EU contribution
- €189,687
- Participants
- 2
- Scheme
- HORIZON-TMA-MSCA-PF-EF
Lines connect the coordinator with its partners.
Results in brief
Multimodal multitAsk learninG for MultIsCale BATHYmetric mapping in shallow waters
Coastal zones are vital socio-ecological systems, supporting biodiversity, economic activities, and the livelihoods of millions of people. Yet, they are increasingly threatened by climate change, coastal erosion, sea-level rise, and human-induced degradation. Accurate and up-to-date information on seabed topography (bathymetry) and seabed type is critical for effective coastal zone management, ecosystem monitoring, marine spatial planning, and climate adaptation strategies. However, such information remains scarce, expensive to acquire, and fragmented across space and time. Traditional seabed mapping relies on in-situ surveys (e.g., echo-sounding, LiDAR), which are resource-intensive and geographically limited. Meanwhile, remote sensing (RS) imagery - from UAVs to satellites - offers broad spatial coverage, yet extracting accurate, high-resolution bathymetric and semantic information from it remains a significant scientific challenge. This is due to the complex physics of light propagation in water, diverse seabed characteristics, and the lack of robust, generalizable deep learning models tailored for underwater and shallow-water environments. The MagicBathy project responds to these gaps by proposing a novel deep learning framework for joint bathymetry and seabed type mapping, uniquely designed for optical imagery in shallow coastal waters. It leverages recent advances in multitask learning, in-domain representation learning, and super-resolution, aiming to transform how bathymetric and semantic information is derived from remote sensing data. The overarching objectives of the project were to: - Enable fine-grained, simultaneous prediction of depth and seabed type by developing multitask models that reflect the interdependent nature of the two outputs. - Establish domain-specific visual representations that account for seabed texture, water attenuation, and modality-induced variations in RS imagery. - Boost the resolution and usability of satellite and aerial data through a novel super-resolution framework informed by UAV-based data and contextual scene knowledge. Given its relevance to marine environmental governance, EU coastal policy, and the Digital Twin of the Ocean (DTO) initiative, MagicBathy's reults are expected to have a significant impact on both scientific advancement and societal needs. They will support data-driven decision-making, reduce monitoring costs, and democratize access to critical coastal information across Europe and beyond.
Data: CORDIS, © European Union
Project objective
Accurate, detailed and high-frequent bathymetry, coupled with the important visual and semantic information, is crucial for the undermapped shallow coastal areas being affected by intense climatological and anthropogenic pressures. Regular UAV and satellite imagery have the potential to frequently and consistently map those areas to different extents and detail, providing ground breaking key information. However, optical properties of water severely affect images and refraction is the main factor affecting their geometry. Current Structure from Motion (SfM) based solutions for refraction correction are slow and costly. Satellite Derived Bathymetry (SDB) methods deliver faster results over huge shallow areas albeit in lower spatial resolution, failing to handle non-homogeneous seabeds. Recent methods based on Convolutional Neural Networks (CNNs) deliver either only the bathymetry or the semantics of the scene, tackling those problems separately and in one scale/modality at a time. They are mostly 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
Original text from CORDIS.
Participants
Links
Data: CORDIS, © European Union