Eodie - Earth Observation Data Information Extractor

Wittke, Samantha; Fouilloux, Anne; Lehti, Petteri; Varho, Juuso; Karjalainen, Mika; Vaaja, Matti; Puttonen, Eetu

doi:10.2139/ssrn.4067133

Cited by 1 publication

(1 citation statement)

References 41 publications

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“…Raw pixel values were extracted from the Level-2A product using a customized version of the EODIE toolkit (v0.1) [71] that builds on Python libraries. Pixels with the center point within the bounding polygon were included.…”

Section: Optical Time Series Datamentioning

confidence: 99%

Scalable Crop Yield Prediction with Sentinel-2 Time Series and Temporal Convolutional Network

et al. 2022

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One of the precepts of food security is the proper functioning of the global food markets. This calls for open and timely intelligence on crop production on an agroclimatically meaningful territorial scale. We propose an operationally suitable method for large-scale in-season crop yield estimations from a satellite image time series (SITS) for statistical production. As an object-based method, it is spatially scalable from parcel to regional scale, making it useful for prediction tasks in which the reference data are available only at a coarser level, such as counties. We show that deep learning-based temporal convolutional network (TCN) outperforms the classical machine learning method random forests and produces more accurate results overall than published national crop forecasts. Our novel contribution is to show that mean-aggregated regional predictions with histogram-based features calculated from farm-level observations perform better than other tested approaches. In addition, TCN is robust to the presence of cloudy pixels, suggesting TCN can learn cloud masking from the data. The temporal compositing of information do not improve prediction performance. This indicates that with end-to-end learning less preprocessing in SITS tasks seems viable.

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Section: Optical Time Series Datamentioning

confidence: 99%