Satellite imagery is changing the way we understand and predict economic activity in the world. Advancements in satellite hardware and low-cost rocket launches have enabled near-real-time, high-resolution images covering the entire Earth. It is too labour-intensive, time-consuming and expensive for human annotators to analyse petabytes of satellite imagery manually. Current computer vision research exploring this problem still lack accuracy and prediction speed, both significantly important metrics for latency-sensitive automatized industrial applications. Here we address both of these challenges by proposing a set of improvements to the object recognition model design, training and complexity regularisation, applicable to a range of neural networks. Furthermore, we propose a fully convolutional neural network (FCN) architecture optimised for accurate and accelerated object recognition in multispectral satellite imagery. We show that our FCN exceeds human-level performance with state-of-the-art 97.67% accuracy over multiple sensors, it is able to generalize across dispersed scenery and outperforms other proposed methods to date. Its computationally light architecture delivers a fivefold improvement in training time and a rapid prediction, essential to real-time applications. To illustrate practical model effectiveness, we analyse it in algorithmic trading environment. Additionally, we publish a proprietary annotated satellite imagery dataset for further development in this research field. Our findings can be readily implemented for other real-time applications too.

卫星图像正在改变我们理解和预测世界经济活动的方式。卫星硬件和低成本火箭发射的进步使得覆盖整个地球的近实时、高分辨率图像成为可能。对于人类注释者来说，手动分析 PB 级的卫星图像过于劳力密集、耗时且昂贵。当前探索这个问题的计算机视觉研究仍然缺乏准确性和预测速度，这两个指标对于延迟敏感的自动化工业应用来说都是非常重要的指标。在这里，我们通过提出一系列适用于一系列神经网络的对象识别模型设计、训练和复杂性正则化的改进来解决这两个挑战。此外，我们提出了一种全卷积神经网络（FCN）架构，该架构针对多光谱卫星图像中准确和加速的目标识别进行了优化。我们证明，我们的 FCN 超越了人类水平的性能，在多个传感器上具有最先进的 97.67% 的准确度，它能够在分散的场景中进行泛化，并且优于迄今为止提出的其他方法。其轻量级计算架构可将训练时间缩短五倍并实现快速预测，这对于实时应用至关重要。为了说明实际模型的有效性，我们在算法交易环境中对其进行分析。此外，我们还发布了专有的带注释卫星图像数据集，以供该研究领域的进一步发展。我们的研究结果也可以很容易地应用于其他实时应用程序。