Fusion of multi-source high resolution remote sensing data for the mapping of water occurrence in the United States. Datasets included wall-to-wall Planet daily acquisitions, Worldview data, as well as products derived from aerial Lidar and high quality annotations. The study was done under supervision of USGS and assisted by partners from Google Cloud and Planet.Frontier Development Lab is an effort by SETI Institute and NASA in order to develop machine learning based methods to address the big questions of our time. To achieve the mapping we hence developed methods based on deep learning and utilizing Google Cloud Platform.

Involved in machine learning projects ranging from biomedical imaging to affinity analysis. Natively worked with Google Cloud Platform (e.g. BigQuery, Dataproc, AI-Platform); as well held workshops at Google (MasterClasses) for ML in Google Cloud.

Worked in agile environments as machine learning engineer and data scientist in projects covering computer vision, time series prediction. Involved as well in prototyping and design thinking.

Agricultural mapping of Sumatra and Borneo based on multi-temporal Landsat 8 OLI, ALOS-2, and Sentinel-1 data targeting seven different perennial crops and plantation types. The work was conducted using cloud resources (GCP, EarthEngine), as well as deep learning methods (ConvNets).

Continued research for the SenseCarbon research project, and participated in the Self-taught Learning research project, which aims at the development of self-taught learning based methodology in a remote sensing context.Under Prof. Dr. Waske participated in the founding of the Remote Sensing and Geoinformation research group. Have since taken on various academic and administrative duties to further its development, including teaching responsibilities and supervision of various theses.

Initial research for the SenseCarbon research project. The work was aimed at the development and adaptation of machine learning methodology for SAR and multispectral remote sensing in the context of deforestation in the Brazilian Amazon.