Interpretable socioeconomic status inference from aerial imagery through urban patterns
Interpretable socioeconomic status inference from aerial imagery through urban patterns
复制标题
DOI：
10.1038/s42256-020-00243-5
复制
发表时间：
2020-10-26
影响因子：
23.8
通讯作者：
Karsai, Marton
中科院分区：
文献类型：
--
作者：
Abitbol, Jacob Levy;Karsai, Marton
城市化是现代社会面临的一个巨大挑战，它带来了更好的经济机会，但也扩大了社会经济不平等。随着这一过程的展开，对传统的数据收集方法来说，准确地跟踪这一过程一直是一个挑战，但遥感信息提供了一种替代方法，可以更全面地了解这些社会变化。通过向神经网络提供卫星图像，可以恢复与该地区相关的社会经济信息。然而，这些模型缺乏解释样本中包含的视觉特征如何触发给定预测的能力。在这里，我们通过从航拍图像预测法国各地的社会经济地位，并根据城市拓扑结构解释类激活映射来缩小这一差距。我们发现，训练模型忽略了城市阶级和社会经济地位之间存在的空间相关性，以获得他们的预测。这些结果为建立更多可解释的模型铺平了道路，这可能有助于更好地跟踪和理解城市化及其后果。
Urbanization is a great challenge for modern societies, promising better access to economic opportunities, but widening socioeconomic inequalities. Accurately tracking this process as it unfolds has been challenging for traditional data collection methods, but remote sensing information offers an alternative way to gather a more complete view of these societal changes. By feeding neural networks with satellite images, the socioeconomic information associated with that area can be recovered. However, these models lack the ability 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 trained models disregard the spatial correlations existing between urban class and socioeconomic status to derive their predictions. These results pave the way to build more interpretable models, which may help to better track and understand urbanization and its consequences.