Deep convolutional neural networks (CNNs) have been shown to predict poverty
and development indicators from satellite images with surprising accuracy. This
paper presents a first attempt at analyzing the CNNs responses in detail and
explaining the basis for the predictions. The CNN model, while trained on
relatively low resolution day- and night-time satellite images, is able to
outperform human subjects who look at high-resolution images in ranking the
Wealth Index categories. Multiple explainability experiments performed on the
model indicate the importance of the sizes of the objects, pixel colors in the
image, and provide a visualization of the importance of different structures in
input images. A visualization is also provided of type images that maximize the
network prediction of Wealth Index, which provides clues on what the CNN
prediction is based on.

该论文通过细致分析深度卷积神经网络（CNNs）对卫星图像进行预测贫困和发展指标的详细反应，并解释了其预测基础。该 CNN 模型能够在比较低分辨率的白天和夜间卫星图像上优于人类主观观察高分辨率图像进行财富指数分类。多种可解释性实验表明对象大小、像素颜色以及不同结构在输入图像中的重要性，并提供了最大化网络财富指数预测的类型图像的可视化，这为 CNN 预测的依据提供了线索。