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Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach

Abstract : 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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Submitted on : Thursday, March 11, 2021 - 2:57:39 PM
Last modification on : Monday, July 4, 2022 - 9:33:50 AM
Long-term archiving on: : Saturday, June 12, 2021 - 6:50:25 PM


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Luc Baudoux, Jordi Inglada, Clément Mallet. Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach. Remote Sensing, MDPI, 2021, 13 (6), pp.1060. ⟨10.3390/rs13061060⟩. ⟨hal-03166675⟩



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