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靛蓝地理创新团队的旗舰平台——靛蓝地图集提供了一幅世界食物系统的生动地图。阿特拉斯洞见的前身是特卢斯实验室,它从这个平台获得了无与伦比的、细致入微的细节。阿特拉斯公司的专有算法能够对局部土壤条件进行特征化,以米级精度绘制田地边界,并识别整个地区作物健康状况的细微差异,从而产生一系列高分辨率、实时和可操作的输出。地图集信息使种植者能够做出更明智的种植、收获和营销决策。它还赋予买方和消费者对其供应链更大程度的控制和选择。

Every month, the directors and data scientists behind Indigo's ag intelligence offer a closer look at our reports. See how we generate our insights, what they mean for the market, and how growers are reacting across the world.

Atlas’ models, powered by the application of machine learning to historical, weather, and remote sensing data, track the movement and impact of different weather patterns. Shown in the graph are first freeze dates for this year, on a county by county basis, along with how early or late they were compared to historical averages.

For decades, vegetation indices have seen many use cases across life sciences, but none has been tailored to the unique needs of agriculture. To track the dynamic variables affecting crop health, Indigo has created the Crop Health Index (CHI). We follow multiple crops across several geographies to better understand our global food system — and help you make better decisions in realtime.
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作为其世界粮食系统活地图的一部分,靛蓝通过一个名为阿特拉斯的卫星成像和机器学习平台预测全球的作物产量和产量。近年来,阿特拉斯的收益率预测能力已经超过了美国农业部。通过将机器学习和人工智能应用到卫星图像中,Indigo Atlas每天都能生成全球作物健康状况和表现。靛蓝利益相关者,包括种植者和合作伙伴,在整个季节中利用这些数据来实现无数的目标。

如果你看一下这里显示的密西西比河的图像,你可以看到被称为增强植被指数(EVI)的东西,其中的图像是由随着时间的推移堆叠的多个数据层组成的。这些输入图像是由欧洲航天局的两颗哨兵-2卫星在2018年整个生长季节采集的,每四天对地球成像一次。这些多时相图像合成是靛蓝领域边界识别和作物类型制图工作中使用的原始成分。

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