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In-season and dynamic crop mapping using 3D convolution neural networks and sentinel-2 time series
In this paper we present a Deep Neural Network-based approach capable of generating (i) a crop map of the current season at a specific point in time (“In season mapping” conventionally at the end of the current year), along with (ii) all intermediate maps during the season able to describe in near real-time the evolution of crop presence (“Dynamic-mapping” at the temporal granularity of satellite imagery revisiting, e. g., 5 days for Sentinel-2 data).
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