Many areas of the world are without basic information on the well-being of
the residing population due to limitations in existing data collection methods.
Overhead images obtained remotely, such as from satellite or aircraft, can help
serve as windows into the state of life on the ground and help "fill in the
gaps" where community information is sparse, with estimates at smaller
geographic scales requiring higher resolution sensors. Concurrent with improved
sensor resolutions, recent advancements in machine learning and computer vision
have made it possible to quickly extract features from and detect patterns in
image data, in the process correlating these features with other information.
In this work, we explore how well two approaches, a supervised convolutional
neural network and semi-supervised clustering based on bag-of-visual-words,
estimate population density, median household income, and educational
attainment of individual neighborhoods from publicly available high-resolution
imagery of cities throughout the United States. Results and analyses indicate
that features extracted from the imagery can accurately estimate the density
(R$^2$ up to 0.81) of neighborhoods, with the supervised approach able to
explain about half the variation in a population's income and education. In
addition to the presented approaches serving as a basis for further geographic
generalization, the novel semi-supervised approach provides a foundation for
future work seeking to estimate fine-scale information from overhead imagery
without the need for label data.

通过高分辨率航拍图像，利用监督卷积神经网络和基于视觉词袋的半监督聚类方法，从公开可获取的城市图像准确估计美国各地区的人口密度、家庭收入中位数和教育水平，并开拓用于地理概括和无需标记数据的细粒度信息估计的新的半监督方法。

尺度下的细粒度估计近邻社区福祉：基于高分辨率正射影像和混合学习

Granularity at Scale: Estimating Neighborhood Well-Being from  High-Resolution Orthographic Imagery and Hybrid Learning

In this paper we aim to determine the location and orientation of a
ground-level query image by matching to a reference database of overhead (e.g.
satellite) images. For this task we collect a new dataset with one million
pairs of street view and overhead images sampled from eleven U.S. cities. We
explore several deep CNN architectures for cross-domain matching --
Classification, Hybrid, Siamese, and Triplet networks. Classification and
Hybrid architectures are accurate but slow since they allow only partial
feature precomputation. We propose a new loss function which significantly
improves the accuracy of Siamese and Triplet embedding networks while
maintaining their applicability to large-scale retrieval tasks like image
geolocalization. This image matching task is challenging not just because of
the dramatic viewpoint difference between ground-level and overhead imagery but
because the orientation (i.e. azimuth) of the street views is unknown making
correspondence even more difficult. We examine several mechanisms to match in
spite of this -- training for rotation invariance, sampling possible rotations
at query time, and explicitly predicting relative rotation of ground and
overhead images with our deep networks. It turns out that explicit orientation
supervision also improves location prediction accuracy. Our best performing
architectures are roughly 2.5 times as accurate as the commonly used Siamese
network baseline.

利用深层卷积神经网络对地面和卫星俯视图进行交叉检索，实现图像地理定位，提出了一种新的损失函数，并采用多种匹配机制和旋转不变训练来提高图片匹配精度和效率。