Slide Fifth
Slide Second
Slide Fourth
Slide Sixth
Slide Sixth
International Geoscience and Remote Sensing Symposium

In 2021 a joint initiative of Belgium and The Netherlands

TH2.MM-4.8

Evaluating Different Deep Learning Models for Automatic Water Segmentation

Thales Akiyama, José Marcato Junior, UFMS - Federal University of Mato Grosso do Sul., Brazil; Wesley Gonçalves, Federal University of Mato Grosso do Sul, Brazil; Mario Carvalho, UFMS - Federal University of Mato Grosso do Sul., Brazil; Anette Eltner, Technische Universität Dresden, Germany
Session:
Multi-applications of Image Segmentation II
Track:
Data Analysis Methods (Optical, Multispectral,Hyperspectral, SAR)
Presentation Time:
Thu, 15 Jul, 11:35-11:40 (UTC)
Thu, 15 Jul, 13:35-13:40 Central Europe Summer Time (UTC +2)
Thu, 15 Jul, 19:35-19:40 China Standard Time (UTC +8)
Thu, 15 Jul, 07:35-07:40 Eastern Daylight Time (UTC -4)
Session Co-Chairs:
James Murphy, Tufts University and Krishna Karra, Impact Observatory
Session Manager:
Marcel Stefko
Presentation
Discussion
Resources
Session TH2.MM-4
TH2.MM-4.1: MULTISCALE CLUSTERING OF HYPERSPECTRAL IMAGES THROUGH SPECTRAL-SPATIAL DIFFUSION GEOMETRY
Sam Polk, James Murphy, Tufts University, United States
TH2.MM-4.2: Deep learning application for fracture segmentation over outcrop images from UAV-based digital photogrammetry
Ademir Marques Jr., Graciela Racolte, Unisinos University, Brazil; Eniuce de Souza, State University of Maringa, Brazil; Hiduino Domingos, Rafael Kenji Horota, João Gabriel Motta, Daniel Zanotta, Unisinos University, Brazil; Caroline Cazarin, Cenpes, Brazil; Luiz Gonzaga Jr, Maurício Veronez, Unisinos University, Brazil
TH2.MM-4.3: DEEP LEARNING AND GOOGLE EARTH ENGINE APPLIED TO MAPPING EUCALYPTUS
João Otavio Nascimento Firigato, José Marcato Junior, Universidade Federal de Mato Grosso do Sul, Brazil; Wesley Gonçalves, Federal University of Mato Grosso do Sul, Brazil; Vitor Matheus Bacani, Universidade Federal de Mato Grosso do Sul, Brazil
TH2.MM-4.4: SEMANTIC SEGMENTATION OF LAND USE / LAND COVER (LU/LC) TYPES USING F-CNNS ON MULTI-SENSOR (RADAR-IR-OPTICAL) IMAGE DATA
Usman Iqbal Ahmed, Arturo Velasco, Bernhard Rabus, Simon Fraser University, Canada
TH2.MM-4.5: Global land use / land cover with Sentinel 2 and deep learning
Krishna Karra, Caitlin Kontgis, Zoe Statman-Weil, Joseph Mazzariello, Mark Mathis, Steven Brumby, Impact Observatory, United States
TH2.MM-4.6: BUILDING FOOTPRINT EXTRACTION USING DEEP LEARNING SEMANTIC SEGMENTATION TECHNIQUES: EXPERIMENTS AND RESULTS
Philipe Borba, Felipe de Carvalho Diniz, Brazilian Army Geographic Service, Brazil; Nilton Correia da Silva, Edilson de Souza Bias, University of Brasilia, Brazil
TH2.MM-4.7: CORN CROPS IDENTIFICATION USING MULTISPECTRAL IMAGES FROM UNMANNED AIRCRAFT SYSTEMS
Fedra Trujillano, Pontifical Catholic University of Peru, Peru; Jessenia Gonzalez, Leipzig University, Germany; Carlos Saito, Andres Flores, Pontifical Catholic University of Peru, Peru; Daniel Racoceanu, Sorbonne University, France
TH2.MM-4.8: Evaluating Different Deep Learning Models for Automatic Water Segmentation
Thales Akiyama, José Marcato Junior, UFMS - Federal University of Mato Grosso do Sul., Brazil; Wesley Gonçalves, Federal University of Mato Grosso do Sul, Brazil; Mario Carvalho, UFMS - Federal University of Mato Grosso do Sul., Brazil; Anette Eltner, Technische Universität Dresden, Germany
TH2.MM-4.9: EXPLORING THE FUSION OF SENTINEL-1 SAR AND SENTINEL-2 MSI DATA FOR BUILT-UP AREA MAPPING USING DEEP LEARNING
Sebastian Hafner, Yifang Ban, Andrea Nascetti, KTH Royal Institute of Technology, Sweden

Quicklinks

Downloads

Programme guide