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What Data are needed for Semantic Segmentation in Earth Observation?
- Source :
- 2019 Joint Urban Remote Sensing Event (JURSE), 2019 Joint Urban Remote Sensing Event (JURSE), May 2019, Vannes, France. pp.1-4, ⟨10.1109/JURSE.2019.8809071⟩, JURSE
- Publication Year :
- 2019
- Publisher :
- HAL CCSD, 2019.
-
Abstract
- International audience; This paper explores different aspects of semantic segmentation of remote sensing data using deep neural networks. Learning with deep neural networks was revolutionized by the creation of ImageNet. Remote sensing benefited of these new techniques, however Earth Observation (EO) datasets remain small in comparison. In this work, we investigate how we can progress towards the ImageNet of remote sensing. In particular, two questions are addressed in this paper. First, how robust are existing supervised learning strategies with respect to data volume? Second, which properties are expected from a large-scale EO dataset? The main contributions of this work are: (i) a strong robustness analysis of existing supervised learning strategies with respect to remote sensing data, (ii) the introduction of a new, large-scale dataset named MiniFrance.
- Subjects :
- Earth observation
010504 meteorology & atmospheric sciences
RESEAU NEURONES
Computer science
LAND USE/LAND COVER MAPPING
02 engineering and technology
Machine learning
computer.software_genre
01 natural sciences
SEMANTIC SEGMENTATION
SUPERVISED LEARNING
Robustness (computer science)
0202 electrical engineering, electronic engineering, information engineering
Segmentation
TELEDETECTION
0105 earth and related environmental sciences
APPRENTISSAGE AUTOMATIQUE
business.industry
DEEP LEARNING
Deep learning
Supervised learning
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Deep neural networks
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- 2019 Joint Urban Remote Sensing Event (JURSE), 2019 Joint Urban Remote Sensing Event (JURSE), May 2019, Vannes, France. pp.1-4, ⟨10.1109/JURSE.2019.8809071⟩, JURSE
- Accession number :
- edsair.doi.dedup.....a9c210cee63b303749c86c52acee985a