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。

基于卷积神经网络的解释卫星预测贫困状况研究

Towards Explaining Satellite Based Poverty Predictions with  Convolutional Neural Networks

Major decisions from governments and other large organizations rely on
measurements of the populace's well-being, but making such measurements at a
broad scale is expensive and thus infrequent in much of the developing world.
We propose an inexpensive, scalable, and interpretable approach to predict key
livelihood indicators from public crowd-sourced street-level imagery. Such
imagery can be cheaply collected and more frequently updated compared to
traditional surveying methods, while containing plausibly relevant information
for a range of livelihood indicators. We propose two approaches to learn from
the street-level imagery: (1) a method that creates multi-household cluster
representations by detecting informative objects and (2) a graph-based approach
that captures the relationships between images. By visualizing what features
are important to a model and how they are used, we can help end-user
organizations understand the models and offer an alternate approach for index
estimation that uses cheaply obtained roadway features. By comparing our
results against ground data collected in nationally-representative household
surveys, we demonstrate the performance of our approach in accurately
predicting indicators of poverty, population, and health and its scalability by
testing in two different countries, India and Kenya. Our code is available at
this https URL

使用公共街道图像进行街区中薄弱群体的生计指标预测，方法分为图像分群和基于图像关系的两种方法，测试结果表明此方法对于贫困、人口和公共卫生等指标的预测精度高

利用社区生成的街景图像预测生计指标

Predicting Livelihood Indicators from Community-Generated Street-Level  Imagery

The combination of high-resolution satellite imagery and machine learning
have proven useful in many sustainability-related tasks, including poverty
prediction, infrastructure measurement, and forest monitoring. However, the
accuracy afforded by high-resolution imagery comes at a cost, as such imagery
is extremely expensive to purchase at scale. This creates a substantial hurdle
to the efficient scaling and widespread adoption of high-resolution-based
approaches. To reduce acquisition costs while maintaining accuracy, we propose
a reinforcement learning approach in which free low-resolution imagery is used
to dynamically identify where to acquire costly high-resolution images, prior
to performing a deep learning task on the high-resolution images. We apply this
approach to the task of poverty prediction in Uganda, building on an earlier
approach that used object detection to count objects and use these counts to
predict poverty. Our approach exceeds previous performance benchmarks on this
task while using 80% fewer high-resolution images. Our approach could have
application in many sustainability domains that require high-resolution
imagery.

通过强化学习的方法，结合高分辨率遥感图像和云计算技术，可以在保持精度的情况下降低获取成本，提高贫困预测任务的性能，为可持续发展的多个领域提供实用的应用。

使用深度强化学习实现高效贫困地图绘制

Efficient Poverty Mapping using Deep Reinforcement Learning

The lack of reliable data in developing countries is a major obstacle to
sustainable development, food security, and disaster relief. Poverty data, for
example, is typically scarce, sparse in coverage, and labor-intensive to
obtain. Remote sensing data such as high-resolution satellite imagery, on the
other hand, is becoming increasingly available and inexpensive. Unfortunately,
such data is highly unstructured and currently no techniques exist to
automatically extract useful insights to inform policy decisions and help
direct humanitarian efforts. We propose a novel machine learning approach to
extract large-scale socioeconomic indicators from high-resolution satellite
imagery. The main challenge is that training data is very scarce, making it
difficult to apply modern techniques such as Convolutional Neural Networks
(CNN). We therefore propose a transfer learning approach where nighttime light
intensities are used as a data-rich proxy. We train a fully convolutional CNN
model to predict nighttime lights from daytime imagery, simultaneously learning
features that are useful for poverty prediction. The model learns filters
identifying different terrains and man-made structures, including roads,
buildings, and farmlands, without any supervision beyond nighttime lights. We
demonstrate that these learned features are highly informative for poverty
mapping, even approaching the predictive performance of survey data collected
in the field.

通过机器学习从高分辨率遥感图像中提取大规模社会经济指标，以辅助政策决策和人道主义救助，特别是在发展中国家等缺乏可靠数据的地区。