Journal
REMOTE SENSING
Volume 13, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/rs13061060
Keywords
land cover; mapping; translation; nomenclature; Convolutional Neural Network; geographical encoding; CORINE Land Cover; operational
Categories
Funding
- AI4GEO project
- French National Research agency as a part of the MAESTRIA project [ANR-18-CE23-0023]
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The study focuses on translating a national scale remote sensed map into CORINE Land Cover using a Convolution Neural Network with positional encoding. The results show that this method achieves a superior performance compared to traditional semantic-based translation approach, with an accuracy of 81% overall in France, close to the targeted 85% accuracy of CLC.
CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC.
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