论文标题
从空中图像推论的城市模式的社会经济相关性：解释卷积神经网络的激活图
Socioeconomic correlations of urban patterns inferred from aerial images: interpreting activation maps of Convolutional Neural Networks
论文作者
论文摘要
城市化是现代社会的巨大挑战，有望在扩大社会经济不平等的同时更好地获得经济机会。准确地跟踪这一过程的发展方式对于传统的数据收集方法一直具有挑战性，而遥感信息为收集这些社会变化提供了更完整的视图提供了替代方案。通过用卫星图像喂养神经网络，可以恢复与该区域相关的社会经济信息，但是这些模型无法解释样品中的视觉特征如何触发给定的预测。在这里，我们通过从空中图像中预测整个法国的社会经济地位并用城市拓扑来解释类激活映射来缩小这一差距。我们表明，该模型无视城市阶级和社会经济地位之间存在的空间相关性来得出其预测。这些结果铺平了建立可解释模型的方式，这可能有助于更好地跟踪和理解城市化及其后果。
Urbanisation is a great challenge for modern societies, promising better access to economic opportunities while widening socioeconomic inequalities. Accurately tracking how this process unfolds has been challenging for traditional data collection methods, while remote sensing information offers an alternative to gather a more complete view on these societal changes. By feeding a neural network with satellite images one may recover the socioeconomic information associated to that area, however these models lack to explain how visual features contained in a sample, trigger a given prediction. Here we close this gap by predicting socioeconomic status across France from aerial images and interpreting class activation mappings in terms of urban topology. We show that the model disregards the spatial correlations existing between urban class and socioeconomic status to derive its predictions. These results pave the way to build interpretable models, which may help to better track and understand urbanisation and its consequences.
