Public PhD defence Maria Culman Forero
Overcoming deep learning data dependency in proximal and remote sensing
On the 15th of March 2023, Maria Culman Forero will publicly defend her PhD entitled “Overcoming deep learning data dependency in proximal and remote sensing: applications in agriculture”. She will take you on a fascinating journey along innovative deep learning and remote sensing science, looking for exotic palm trees and Belgian conference pear trees. This, to tell you her story for the hunt for solutions to overcome some major barriers that deep learning researchers and practitioners face when working with proximal and remote sensing data in an agricultural setting. Strategies to reduce the dependency on large-scale and high- quality datasets will be demonstrated.
She will formulate answers to the following questions:
- How to exploit noisy labels in deep learning?
- How to compensate for spatial coarse imagery in deep learning?
- How to reduce the manual annotation effort in deep learning?
The findings will be demonstrated through her research on palm tree and individual pear detection. For both applications, only noisy and/ or limited labelled datasets were available. The proposed strategies, namely, transfer learning from noisy labels, multimodal data integration, and automatic image annotation, enabled to either mitigate or circumvent the data dependency of deep learning in tree and fruit detection applications.
Fruit detections and segmentations from image sample
Related blog posts by Maria Culman Forero:
- Automatic fruit count to support crop management
- Palm tree mapping using deep learning on aerial images