LandNET: A Multi-Modal Fusion Network for Classification

LandNET: A Multi-Modal Fusion Network for Classification

    Presenter(s)

    Jonathan Paul Schierl

    Description

    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.

    Publication Date

    4-22-2021

    Project Designation

    Graduate Research

    Primary Advisor

    Theus H. Aspiras

    Primary Advisor's Department

    Electrical and Computer Engineering

    Keywords

    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

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