{"type":"ScholarlyArticle","title":"RGB UAV Imagery Segmentation: A Comparative Study","authors":[{"name":"Mathews Jere","slug":"mathews-jere","given":"Mathews","family":"Jere","affiliation":"Department of Computing , University of Malawi , Zomba , Malawi"},{"name":"Esnart Sandikonda","slug":"esnart-sandikonda","given":"Esnart","family":"Sandikonda","affiliation":"Department of Computing , University of Malawi , Zomba , Malawi"},{"name":"Kondwani G. Munthali","slug":"kondwani-g-munthali","given":"Kondwani G.","family":"Munthali","affiliation":"Department of Computing , University of Malawi , Zomba , Malawi"},{"name":"Yamikani D.J. Phiri","slug":"yamikani-d-j-phiri","given":"Yamikani D.J.","family":"Phiri","affiliation":"Department of Computing , University of Malawi , Zomba , Malawi"},{"name":"Augustine Simwela","slug":"augustine-simwela","given":"Augustine","family":"Simwela","affiliation":"Department of Computing , University of Malawi , Zomba , Malawi"},{"name":"Ramsey Njema","slug":"ramsey-njema","given":"Ramsey","family":"Njema","affiliation":"Department of Computing , University of Malawi , Zomba , Malawi"}],"abstract":"Semantic segmentation of remote sensing images is vital in computer vision for detailed scene understanding, with applications spanning agriculture, environmental monitoring, and urban planning. In this study, we conduct a comparative analysis of advanced RGB image segmentation methods using various Fully Convolutional Neural Network (FCN) architectures. The architectures evaluated include Attention U-Net, Deeplabv3+, U-Nets with backbones such as ResNetv2, MobileNetv2, VGG19, and EfficientNet. Our experiments on UAV-captured images from the Mpokwe Extension Planning Area (EPA) in Zomba, Malawi, reveal that the ResNetv2-based U-Net achieves the highest Intersection over Union (IoU) score of 0.91. This model effectively segments classes like maize, water, trees, bare land, built-up areas, and other vegetation types, facilitating applications in agricultural yield estimation, land use analysis, and environmental monitoring. The MobileNetv2-based U-Net provides a good balance between performance and efficiency, making it suitable for real-time applications. This work demonstrates the potential of leveraging cost-effective RGB sensors to achieve competitive performance in image segmentation, contributing to the Sustainable Development Goals (SDGs) by informing agricultural practices and environmental stewardship.","doi":"10.24963/ijcai.aai4g.2024/3","url":"https://ijcai.org/proceedings_aai4g/2024/3","pdf":"https://ijcai.org/proceedings_aai4g/2024/0003.pdf","markdown":"https://ijcai.org/proceedings_aai4g/2024/3.md","bibtex":"https://ijcai.org/proceedings_aai4g/2024/bibtex/3","volume":{"slug":"aai4g-2024","title":"Proceedings of the Second IJCAI AI for Good Symposium in Africa hosted by Deep Learning Indaba","short_title":"AI for Good Africa 2024","url":"https://ijcai.org/proceedings_aai4g/2024/","year":2024,"location":"Dakar, Senegal","dates":"1-7 September 2024","editors":"Avishkar Bhoopchand, Girmaw Abebe Tadesse, Sibusisiwe Makhanya, Frank Dignum, Georgina Curto","isbn":"978-1-956792-07-2","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.aai4g.2024"},"section":"Indaba","track":"Indaba","number":3,"pages":{"first":19,"last":24},"keywords":["Indaba: Indaba"],"videos":[],"references":[{"position":1,"raw":"[Baheti et al., 2020] Bhushan Baheti, Shubham Innani, Sarthak Gajre, and Sanjay Talbar. 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