ABSTRACT

Several methods have been proposed for using satellite imagery to model poverty. These include poverty mapping using convolutional neural networks applied either directly or using transfer learning to high resolution satellite images, or combinations of methods that combine satellite imagery with standard methods. However, these methods require proprietary imagery which, given their cost and infrequent acquisition, may render these advances impractical for most applications. The authors investigate how satellite-derived poverty maps may improve when incorporating features derived from Sentinel-2 and MODIS imagery, which are both open-source and freely and readily available. The authors estimate a poverty map for Belize which incorporates spatial and time series features derived from these sensors, with and without survey derived variables. They document an 8% percent improvement in model performance when including these satellite features and conclude by arguing that Open Data for Development should include open data pipelines where possible.

摘要

已经提出了几种使用卫星图像对贫困进行建模的方法。其中包括使用直接应用的卷积神经网络或使用转移学习对高分辨率卫星图像进行的贫困图绘制，或者将卫星图像与标准方法相结合的方法的组合。但是，这些方法需要专有的图像，考虑到它们的成本和不经常获得的图像，这些图像可能对于大多数应用来说都不可行。作者研究了将来自Sentinel-2和MODIS图像的特征合并后，如何改善卫星衍生的贫困图，Sentinel-2和MODIS图像都是开源的，可以自由，方便地获得。作者估计了伯利兹的贫困图，该图结合了从这些传感器获得的空间和时间序列特征，并带有或不带有调查得出的变量。