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Measuring phenology uncertainty with large scale image processing

Abstract : One standard method to capture data for phenological studies is with digital cameras, taking periodic pictures of vegetation. The large volume of digital images introduces the opportunity to enrich these studies by incorporating big data techniques. The new challenges, then, are to efficiently process large datasets and produce insightful information by controlling noise and variability. On these grounds, the contributions of this paper are the following. (a) A histogram-based visualization for large scale phenological data. (b) Phenological metrics based on the HSV color space, that enhance such histogram-based visualization. (c) A mathematical model to tackle the natural variability and uncertainty of phenological images. (d) The implementation of a parallel workflow to process a large amount of collected data efficiently. We validate these contributions with datasets taken from the Phenological Eyes Network (PEN), demonstrating the effectiveness of our approach. The experiments presented here are reproducible with the provided companion material
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https://hal.inria.fr/hal-03130476
Contributor : Arnaud Legrand <>
Submitted on : Wednesday, February 3, 2021 - 3:53:59 PM
Last modification on : Thursday, February 4, 2021 - 3:28:38 AM

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Guilherme Alles, João Comba, Jean-Marc Vincent, Shin Nagai, Lucas Mello Schnorr. Measuring phenology uncertainty with large scale image processing. Ecological Informatics, Elsevier, 2020, 59, pp.101109. ⟨10.1016/j.ecoinf.2020.101109⟩. ⟨hal-03130476⟩

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