My current projects include:

Semi-Supervised Learning for Earth Observation

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Labelled datasets are more and more common in EO, and yet this is only a waterdrop in the ocean of unlabelled imagery. In Javiera Castillo-Navarro’s PhD, co-supervised with A. Boulch and S. Lefèvre, we explore semi-supervised strategies to harness unlabelled data for better semantic segmentation. In particular, we showed that common datasets were not suitable to assess real-life generalization issues (paper), released MiniFrance the 1st large-scale dataset designed for semi-supervised training and evaluation, and proposed semi-supervised neural nets (paper) with self-supervised losses (paper).

[ Mach. Learn. paper on Semi-supervised learning for EO / MiniFrance dataset ]

Semantic Change Detection

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With the very high resolution now available even from space, local changes can now be characterized precisely. Rodrigo Daudt, Alexandre Boulch, Yann Gousseau and I have proposed the first deep neural network architectures for change detection in Earth-observation. We also created and released OSCD, a dataset with reference data for training such nets. The last evolution of this line of work is Semantic Change Detection, which allows to characterize the modification of land use, and we propose a Multi-Task Learning network to solve this problem automatically along with the high-res HRSCD dataset.

[ ICIP paper on siamese nets for change detection / code / OSCD dataset / HRSCD dataset / arxiv ]

Depth Estimation from a Single Image

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Turning 2D images into depth is now possible with a monocular camera, without neither stereo nor active sensor. With Marcela Carvalho and Pauline Trouvé, we designed a dense network for depth estimation from a single image. We investigate how to model the right loss for such a network, and how blur from defocus can help us predict better estimates. This network ranks among the top ones of the state of the art on the NUYv2 dataset while being simpler to train in a single phase than most competitors.

[ ICIP’18 paper / ECCV/W’18 paper / video / code ]

Joint Use of EO Data and Cartography

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Cartography and especially crowd-sourced geographic information like OpenStreetMap is a great way to drive a neural network towards a correct classification. With Nicolas Audebert and Sébastien Lefèvre, we built fusion networks able handle efficiently this new input.

The SpaceNet Challenge round 2 winner is using a similar solution: see his blog post which mentions our paper. OSM as input is promising !

[ CVPR’17 paper / arxiv ]


Older projects can be found here