output
20162023
most citedIncremental Learning for Semantic Segmentation of Large-Scale Remote Sensing Data

150 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

7 papers

cs.CV2023★ 7 cited

A Survey and Benchmark of Automatic Surface Reconstruction from Point Clouds

Raphael Sulzer, Renaud Marlet, Bruno Vallet +1

We present a comprehensive survey and benchmark of both traditional and learning-based methods for surface reconstruction from point clouds. This task is particularly challenging f…

cs.CV2020★ 77 cited

DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images

Onur Tasar, Alain Giros, Yuliya Tarabalka +2

The domain adaptation of satellite images has recently gained an increasing attention to overcome the limited generalization abilities of machine learning models when segmenting la…

cs.CV2020★ 22 cited

Polygonal Building Segmentation by Frame Field Learning

Nicolas Girard, Dmitriy Smirnov, Justin Solomon +1

While state of the art image segmentation models typically output segmentations in raster format, applications in geographic information systems often require vector polygons. To h…

cs.CV2019★ 31 cited

ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks

Onur Tasar, S L Happy, Yuliya Tarabalka +1

Due to the various reasons such as atmospheric effects and differences in acquisition, it is often the case that there exists a large difference between spectral bands of satellite…

cs.LG2018★ 150 cited

Incremental Learning for Semantic Segmentation of Large-Scale Remote Sensing Data

Onur Tasar, Yuliya Tarabalka, Pierre Alliez

In spite of remarkable success of the convolutional neural networks on semantic segmentation, they suffer from catastrophic forgetting: a significant performance drop for the alrea…

cs.CV2018★ 8 cited

Coarse to fine non-rigid registration: a chain of scale-specific neural networks for multimodal image alignment with application to remote sensing

Armand Zampieri, Guillaume Charpiat, Yuliya Tarabalka

We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical…

cs.CV2016★ 75 cited

Recurrent Neural Networks to Correct Satellite Image Classification Maps

Emmanuel Maggiori, Guillaume Charpiat, Yuliya Tarabalka +1

While initially devised for image categorization, convolutional neural networks (CNNs) are being increasingly used for the pixelwise semantic labeling of images. However, the prope…