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- Title
MULTISENGE: A MULTIMODAL AND MULTITEMPORAL BENCHMARK DATASET FOR LAND USE/LAND COVER REMOTE SENSING APPLICATIONS.
- Authors
Wenger, R.; Puissant, A.; Weber, J.; Idoumghar, L.; Forestier, G.
- Abstract
This paper presents MultiSenGE that is a new large scale multimodal and multitemporal benchmark dataset covering one of the biggest administrative region located in the Eastern part of France. MultiSenGE contains 8,157 patches of 256 × 256 pixels for the Sentinel-2 L2A , Sentinel-1 GRD images in VV-VH polarization and a Regional large scale Land Use/Land Cover (LULC) topographic reference database. With MultiSenGE, we contribute to the recents developments towards shared data use and machine learning methods in the field of environmental science. The purpose of this dataset is to propose relevant and easy-access dataset to explore deep learning methods. We use MultiSenGE to evaluate the performance for urban areas using well-known deep learning techniques. These results serve as a baseline for future research on remote sensing applications using the multi-temporal and multimodal aspects of MultiSenGE. With all patches georeferenced at a 10 meters spatial resolution covering the whole Grand-Est Region, MultiSenGE provides an opportunity for environmental benchmark dataset will help to advance data-driven techniques for land use/land cover remote sensing applications.
- Subjects
FRANCE; LAND cover; REMOTE sensing; LAND use; DEEP learning; ENVIRONMENTAL sciences; MACHINE learning; CITIES &; towns
- Publication
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, 2022, Issue 3, p635
- ISSN
2194-9042
- Publication type
Article
- DOI
10.5194/isprs-annals-V-3-2022-635-2022