LandNET: A Multi-Modal Fusion Network for Classification
Jonathan Paul Schierl
There is a need for classifying land coverage by usage. As these classes are somewhat abstract, this provides a challenge in classifying them and a need for as much information as possible. We propose an architecture capable of classify such scenes, using 2D aerial imagery and 3D point clouds. This is done by fusing the learned feature space of each modality, to be classified with fully connected layers. This method provides a high degree of accuracy for each modality and then learns the benefits of data type, for more accurate classification.
Theus H. Aspiras
Primary Advisor's Department
Electrical and Computer Engineering
Stander Symposium project, School of Engineering
United Nations Sustainable Development Goals
Industry, Innovation, and Infrastructure; Decent Work and Economic Growth
"LandNET: A Multi-Modal Fusion Network for Classification" (2021). Stander Symposium Projects. 2367.