Semi-supervised semantic segmentation in Earth observation: the MiniFrance suite, dataset analysis and multi-task network study

DOI10.1007/s10994-020-05943-yOpenAlexW3129983770MaRDI QIDQ2102368

Alexandre Boulch, Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre, Javiera Castillo-Navarro

Publication date: 28 November 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2010.07830

zbMATH Keywords

semi-supervised learningsemantic segmentationlarge-scale datasetEarth observationland use mapping

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Semi-supervised semantic segmentation in Earth observation: the MiniFrance suite, dataset analysis and multi-task network study

Uses Software

• DeepLab
• PyTorch
• ImageNet
• Adam
• t-SNE
• SegNet
• MS-COCO
• U-Net

Cites Work

• Unnamed Item
• Semi-supervised semantic segmentation in Earth observation: the MiniFrance suite, dataset analysis and multi-task network study
• Estimating the Support of a High-Dimensional Distribution
• Mumford–Shah Loss Functional for Image Segmentation With Deep Learning

This page was built for publication: Semi-supervised semantic segmentation in Earth observation: the MiniFrance suite, dataset analysis and multi-task network study