MO4.M3.3
GDNet: A Graph Convolution and Hybrid Dilated Convolution Network for Road Extraction from High-resolution Satellite Images
Xiangan Zheng, China University of Geosciences, China; MengXiong Tang, Guangzhou Construction Group Co., LTD, China; Jia Chen, Wuhan University of Science and Technology, China; Zhaozhao Zeng, Jun Li, China University of Geosciences, China
Session:
MO4.M3: Deep Learning for Semantic Segmentation & Change Detection in Remote Sensing I Oral
Track:
AI and Big Data
Location:
Mezzanine: Room M3
Presentation Time:
Monday, 4 August, 16:15 - 16:30
Session Co-Chairs:
Madeline Loui, MIT and Muhamad Risqi Utama Saputra, Monash University, Indonesia
Session MO4.M3
MO4.M3.1: Box-supervised instance segmentation for building rooftop delineation from high spatial resolution remotely sensed imagery
Hongjie He, University of Waterloo, Canada; Nan Chen, Xi’an Aeronautical University, China; Lingfei Ma, University of Finance and Economics, China; Jonathan Li, University of Waterloo, Canada
MO4.M3.2: MULTI-MODAL DEEP LEARNING APPROACHES TO SEMANTIC SEGMENTATION OF MINING FOOTPRINTS WITH MULTISPECTRAL SATELLITE IMAGERY
Muhamad Risqi Utama Saputra, Irfan Dwiki Bhaswara, Bahrul Ilmi Nasution, Monash University Indonesia, Indonesia; Michelle Ang Li Ern, University of Nottingham Malaysia, Malaysia; Nur Laily Romadhotul Husna, Tahjudil Witra, Vicky Feliren, Monash University Indonesia, Indonesia; John R. Owen, University of the Free State, South Africa; Deanna Kemp, The University of Queensland, Australia; Alex M. Lechner, Monash University Indonesia, Indonesia
MO4.M3.3: GDNet: A Graph Convolution and Hybrid Dilated Convolution Network for Road Extraction from High-resolution Satellite Images
Xiangan Zheng, China University of Geosciences, China; MengXiong Tang, Guangzhou Construction Group Co., LTD, China; Jia Chen, Wuhan University of Science and Technology, China; Zhaozhao Zeng, Jun Li, China University of Geosciences, China
MO4.M3.4: Multi-Feature Sensing Decoder(MFSD): Advancing Remote Sensing Segmentation encoder
Dai Zirui, Beijing Institute of Technology, China; Bi Fukun, North China University of Technology, China
Resources