191 citations · 588 across the 15 of their papers we have counts for
16 papers
Brightearth roads: Towards fully automatic road network extraction from satellite imagery
Liuyun Duan, Willard Mapurisa, Maxime Leras +2
The modern road network topology comprises intricately designed structures that introduce complexity when automatically reconstructing road networks. While open resources like Open…
SimpliCity: Reconstructing Buildings with Simple Regularized 3D Models
Jean-Philippe Bauchet, Raphael Sulzer, Florent Lafarge +1
Automatic methods for reconstructing buildings from airborne LiDAR point clouds focus on producing accurate 3D models in a fast and scalable manner, but they overlook the problem o…
3D detection of roof sections from a single satellite image and application to LOD2-building reconstruction
Johann Lussange, Mulin Yu, Yuliya Tarabalka +1
Reconstructing urban areas in 3D out of satellite raster images has been a long-standing and challenging goal of both academical and industrial research. The rare methods today ach…
Input Similarity from the Neural Network Perspective
Guillaume Charpiat, Nicolas Girard, Loris Felardos +1
We first exhibit a multimodal image registration task, for which a neural network trained on a dataset with noisy labels reaches almost perfect accuracy, far beyond noise variance.…
Deep Neural Networks for automatic extraction of features in time series satellite images
Gael Kamdem De Teyou, Yuliya Tarabalka, Isabelle Manighetti +2
Many earth observation programs such as Landsat, Sentinel, SPOT, and Pleiades produce huge volume of medium to high resolution multi-spectral images every day that can be organized…
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…
StandardGAN: Multi-source Domain Adaptation for Semantic Segmentation of Very High Resolution Satellite Images by Data Standardization
Onur Tasar, Yuliya Tarabalka, Alain Giros +2
Domain adaptation for semantic segmentation has recently been actively studied to increase the generalization capabilities of deep learning models. The vast majority of the domain…
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…
SemI2I: Semantically Consistent Image-to-Image Translation for Domain Adaptation of Remote Sensing Data
Onur Tasar, S L Happy, Yuliya Tarabalka +1
Although convolutional neural networks have been proven to be an effective tool to generate high quality maps from remote sensing images, their performance significantly deteriorat…
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…
Noisy Supervision for Correcting Misaligned Cadaster Maps Without Perfect Ground Truth Data
Nicolas Girard, Guillaume Charpiat, Yuliya Tarabalka
In machine learning the best performance on a certain task is achieved by fully supervised methods when perfect ground truth labels are available. However, labels are often noisy,…
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…
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…
Progressive Tree-like Curvilinear Structure Reconstruction with Structured Ranking Learning and Graph Algorithm
Seong-Gyun Jeong, Yuliya Tarabalka, Nicolas Nisse +1
We propose a novel tree-like curvilinear structure reconstruction algorithm based on supervised learning and graph theory. In this work we analyze image patches to obtain the local…
High-Resolution Semantic Labeling with Convolutional Neural Networks
Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat +1
Convolutional neural networks (CNNs) have received increasing attention over the last few years. They were initially conceived for image categorization, i.e., the problem of assign…
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…