Developing a Geospatial Data Platform
Supporting machine learning on multisensor data from airborne remote sensing
The PlasticObs_plus research project (funding code: 67KI21014A) is part of the BMUV (Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection) funding initiative “AI lighthouse projects for the environment, climate, nature and resources”. It investigates machine learning on multi-sensor data from airborne remote sensing to combat plastic waste in oceans and rivers as part of the funding program “Kl-Lighthouses for Environmental Protection”. The project goal is to develop an integrated measurement system for routine, quasi-synoptic acquisition and visualization of the distribution of plastic debris on the ocean surface and on shorelines or coastal strips via remote sensing and artificial intelligence methods. In addition to the intended real-time data acquisition of plastic objects by airborne sensors, downstream data analysis plays a central role in the research project.
52°North’s role as a subcontractor is to support the development of a geodata management platform that will enable further processing of the raw data. This includes developing a central geo-portal, which will provide the results as web services. Ultimately, the platform will be accessible to the general public, stakeholders and responsible public authorities to promote sustainable solutions to environmental pollution and targeted, effective counter-strategies.
Following the requirements analysis and software launch in 2023, 52°North developed a REST API to trigger an AI inference based on a given input dataset. Together with DFKI, which trained and provided the actual AI models, 52°North made each model available via that REST API. Model metadata descriptions are used to dynamically create an OpenAPI of the REST API.
In addition, our team developed a GeoNode application that includes a backend and a frontend extension. The frontend extension includes the automatic form generation of the required input parameters for an AI inference. This is done based on the dynamically created model descriptions via the OpenAPI. The actual requests to the REST API are proxied via the extension’s backend component. Since the execution of AI inferences often leads to high resource use, it was decided that the REST API for triggering AI inferences should only be available to authorized users. To this end, a permission set was created via database migration that is enforced for all requests to the proxy REST API. once an inference is triggered (asynchronously), the result is uploaded back to GeoNode and linked to the input raster.
Partners
OPTIMARE Systems GmbH, Germany
Jade Hochschule Wilhelmshaven/Oldenburg/Elsflet, Germany
everwave GmbH, Germany
