FaultDeform : Fast and Accurate Sub-pixel Displacement Estimation From Optical Satellite Images based on Deep Learning

Dataset

DOI

Contains the FaultDeform train, validation and test sub-datasets (10,000 samples). The three sub-datasets contain samples, built with pairs of large 1024x1024 windows that contain realistic synthetic faults, and their displacement maps associated.

Identifier
DOI	https://doi.org/10.57745/G02ZXZ
Metadata Access	https://entrepot.recherche.data.gouv.fr/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.57745/G02ZXZ

Provenance
Creator	Montagnon, Tristan ; Giffard-Roisin Sophie (ORCID: 0000-0001-5606-145X); Hollingsworth, James (ORCID: 0000-0003-0122-296X); Pathier Erwan ; Dalla Mura Mauro ; Marchandon Mathilde
Publisher	Recherche Data Gouv
Contributor	Giffard-Roisin Sophie; Institut des Sciences de la Terre; Entrepôt-Catalogue Recherche Data Gouv
Publication Year	2025
Rights	etalab 2.0; info:eu-repo/semantics/openAccess; https://spdx.org/licenses/etalab-2.0.html
OpenAccess	true
Contact	Giffard-Roisin Sophie (ISTerre ; UGA, CNRS, IRD, USMB, Université Gustave Eiffel ; France)

Representation
Resource Type	Dataset
Format	application/gzip
Size	517743042506
Version	1.0
Discipline	Geosciences; Earth and Environmental Science; Environmental Research; Natural Sciences