6 citations · 6 across the 3 of their papers we have counts for
4 papers
Not every day is a sunny day: Synthetic cloud injection for deep land cover segmentation robustness evaluation across data sources
Sara Mobsite, Renaud Hostache, Laure Berti Equille +2
Supervised deep learning for land cover semantic segmentation (LCS) relies on labeled satellite data. However, most existing Sentinel-2 datasets are cloud-free, which limits their…
U-NetMN and SegNetMN: Modified U-Net and SegNet models for bimodal SAR image segmentation
Marwane Kzadri, Franco Alberto Cardillo, Nanée Chahinian +3
Segmenting Synthetic Aperture Radar (SAR) images is crucial for many remote sensing applications, particularly water body detection. However, deep learning-based segmentation model…
Estimating ensemble likelihoods for the Sentinel-1 based Global Flood Monitoring product of the Copernicus Emergency Management Service
Christian Krullikowski, Candace Chow, Marc Wieland +8
The Global Flood Monitoring (GFM) system of the Copernicus Emergency Management Service (CEMS) addresses the challenges and impacts that are caused by flooding. The GFM system prov…
Extracting localized information from a Twitter corpus for flood prevention
Etienne Brangbour, Pierrick Bruneau, Stéphane Marchand-Maillet +4
In this paper, we discuss the collection of a corpus associated to tropical storm Harvey, as well as its analysis from both spatial and topical perspectives. From the spatial persp…