Nicolas Audebert

According to our database1, Nicolas Audebert authored at least 15 papers between 2016 and 2018.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2018
Classification de données massives de télédétection. (Classification of big remote sensing data).
PhD thesis, 2018

SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks.
Computers & Graphics, 2018

Large-Scale Semantic Classification: Outcome of the First Year of Inria Aerial Image Labeling Benchmark.
Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018

Object Detection in Remote Sensing Images with Center Only.
Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018

Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples.
Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018

2017
Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images.
Remote Sensing, 2017

Fusion of heterogeneous data in convolutional networks for urban semantic labeling.
Proceedings of the Joint Urban Remote Sensing Event, 2017

Deep learning for urban remote sensing.
Proceedings of the Joint Urban Remote Sensing Event, 2017

Deep learning for semantic segmentation of remote sensing images with rich spectral content.
Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium, 2017

Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017



Unstructured Point Cloud Semantic Labeling Using Deep Segmentation Networks.
Proceedings of the Eurographics Workshop on 3D Object Retrieval, 2017

2016
How useful is region-based classification of remote sensing images in a deep learning framework?
Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium, 2016

Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks.
Proceedings of the Computer Vision - ACCV 2016, 2016


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