用遥感数据和Transformer模型，自动识别墨西哥作物类型。
Towards more efficient agricultural practices via transformer-based crop type classification
- 基于哨兵1/2影像时间序列，构建像素级二分类变压器模型。
- 初步结果显示模型在墨西哥可准确区分作物与非作物区域。
- 结合相似生态区数据的元学习法，有望提升多类作物分类性能。
机器学习在提升作物产量和应对气候变化方面潜力巨大。准确绘制农作物分布图是支持多项政策与研究应用的关键输入。本文提出初步工作，表明利用基于像素的二分类作物/非作物时间序列变压器模型，可从墨西哥的哨兵1和2卫星影像时间序列中准确分类作物。我们还发现，通过引入相似农业生态区的数据进行元学习，可能进一步提升模型表现。基于这些有前景的结果，我们提议进一步开发该方法，目标是在墨西哥哈利斯科州实现基于元学习的多类作物精确分类，使用包含相似生态区数据的训练集。
原文摘要 · Abstract (English)
Machine learning has great potential to increase crop production and resilience to climate change. Accurate maps of where crops are grown are a key input to a number of downstream policy and research applications. In this proposal, we present preliminary work showing that it is possible to accurately classify crops from time series derived from Sentinel 1 and 2 satellite imagery in Mexico using a pixel-based binary crop/non-crop time series transformer model. We also find preliminary evidence that meta-learning approaches supplemented with data from similar agro-ecological zones may improve model performance. Due to these promising results, we propose further development of this method with the goal of accurate multi-class crop classification in Jalisco, Mexico via meta-learning with a dataset comprising similar agro-ecological zones.
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