191 citations · 588 across the 15 of their papers we have counts for
5 papers · 1 filter
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…