Year: 2024-2025

Supervision: Jitte Waagen

Execution and research: Jitte Waagen, Wouter Verschoof-van der Vaart, Agnes Schneider, eScience engineers: Christiaan Meijer, Maurice de Kleijn

Project description: 

The DroneML project aims to develop machine learning-based software that can rapidly screen multiple feature types (e.g., regularly shaped features that contrast with natural soil and grassland surroundings) and multiple multimodal input layers simultaneously, to enable rapid processing of large datasets for subsequent manual assessment of identified features. After initial identification, an ensemble learning approach such as ‘stacking’ may provide a machine learning supported metamodel for interpretation. The optimal solution would be if such a tool could be realized as, for example, a plugin in QGIS, a FOSS geographic information system, that can be run on a decent laptop, or could make use of cloud processing.

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