XAI for Early Crop Classification
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Abstract (arXiv)
We propose an approach for early crop classification through identifying important timesteps with eXplainable AI (XAI) methods. Our approach consists of training a baseline crop classification model to carry out layer-wise relevance propagation (LRP) so that the salient time step can be identified. We chose a selected number of such important time indices to create the bounding region of the shortest possible classification timeframe. We identified the period 21st April 2019 to 9th August 2019 as having the best trade-off in terms of accuracy and earliness. This timeframe only suffers a 0.75% loss in accuracy as compared to using the full timeseries. We observed that the LRP-derived important timesteps also highlight small details in input values that differentiates between different classes and
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 16 references that are in the map. First posted on arXiv 10 Oct 2023.
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5 of 16 references.
References · 5
- 2022XAI for Transformers: Better Explanations through Conservative Propagationcited by 100
- 2019Self-attention for raw optical Satellite Time Series Classificationcited by 75
- 2019Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attentioncited by 71
- 2021EuroCrops: A Pan-European Dataset for Time Series Crop Type Classificationcited by 13
- 2019Early Classification for Agricultural Monitoring from Satellite Time Seriescited by 7
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