Automatic coastline classification based on machine learning is proven to be robust for sandy beaches on regional and global scales. However sandy beaches only make around one third of the world’s ice-free shoreline. The rest consists of mudflats, cliffs, different types of vegetation and human constructions. Classification of these features is
more challenging. For instance, mild foreshore...
Our research focuses on detecting and tracking ocean carbon regimes, which are useful tools for understanding the impacts of climate change on ocean carbon uptake. Geoscientific datasets in Earth System Sciences often contain local and distinct statistical distributions at a regional scale. This poses a significant challenge in applying conventional clustering algorithms for data analysis....
Compliant with the FAIR data principles ¹, long-term archiving of bathymetry data from multibeam echosounders – a highly added value for
the data life cycle - is the challenging task in the data information system PANGAEA. To cope with the increasing amount of data
(“bathymetry puzzle pieces”) acquired from research vessels and the demand for easy “map-based” means to find valuable data, new...
In an ongoing effort within the Helmholtz association to make research data FAIR, i.e. findable, accessible, interoperable and reusable, we also need to make biological samples visible and searchable. To achieve this, a first crucial step is to inventory already available samples, connect them to relevant metadata and assess the requirements for various sample types (e.g. experimental, time...
Monitoring environmental changes in terrestrial, coastal, and marine areas in remote locations, such as Greenland, can prove challenging due to harsh weather conditions and lack of infrastructure. It is therefor of great importance that any data gathered in these locations, is made freely available for viewing and reuse. To address this challenge, we developed the Greenland Drone Explorer...
Mangrove forests are threatened by multiple anthropogenic stressors, urging researchers to create improved monitoring methods for their conservation and management. Recent remote sensing efforts have found some success using high resolution imagery of mangrove forests with sparse vegetation. In this study we focus on stands of mangrove forests with dense vegetation, consisting of the endemic...
The incorporation of machine learning (ML) into wildlife monitoring has the potential to revolutionize data collection and conservation initiatives. We outline a human-in-the-loop ML approach for the identification of sharks caught along Tanzanian coasts. The image sources for this monitoring come from marketplaces or landing sites, resulting in a vast range of image quality, the state and...
In an interactive demonstration, we will showcase how already available APIs can be used to unlock the power of a federated workspace for marine research, allowing for collaboration and data sharing across multiple services and applications.
Through the use of APIs, distributed metadata-services, such as the AWI Sensor Registry and GEOMARs Ocean Science Information System, DSHIP systems...
