Data collection through censuses is conducted every 10 years on average in
Latin America, making it difficult to monitor the growth and support needed by
communities living in these settlements. Conducting a field survey requires
logistical resources to be able to do it exhaustively. The increasing
availability of open data, high-resolution satellite images, and free software
to process them allow us to be able to do so in a scalable way based on the
analysis of these sources of information. This case study shows the
collaboration between Dymaxion Labs and the NGO Techo to employ machine
learning techniques to create the first informal settlements census of
Tegucigalpa, Honduras.

该研究展示了 Dymaxion Labs 和 Techo NGO 之间的合作，利用机器学习技术通过高分辨率卫星图像创建洪都拉斯特古西格尔巴的首个非正式住区普查。

机器学习地图维护特古西加尔巴潜在非正规定居点进行地面勘测的案例研究

Case study: Mapping potential informal settlements areas in Tegucigalpa  with machine learning to plan ground survey

Millions of people worldwide are absent from their country's census.
Accurate, current, and granular population metrics are critical to improving
government allocation of resources, to measuring disease control, to responding
to natural disasters, and to studying any aspect of human life in these
communities. Satellite imagery can provide sufficient information to build a
population map without the cost and time of a government census. We present two
Convolutional Neural Network (CNN) architectures which efficiently and
effectively combine satellite imagery inputs from multiple sources to
accurately predict the population density of a region. In this paper, we use
satellite imagery from rural villages in India and population labels from the
2011 SECC census. Our best model achieves better performance than previous
papers as well as LandScan, a community standard for global population
distribution.

使用多种卫星图像输入的两个卷积神经网络结构有效地结合了来自印度乡村地区的卫星图像和 2011 年人口普查的人口标签，以准确预测一区域的人口密度，性能优于以往研究以及全球人口分布的标准 LandScan。

利用卫星图像进行的深度学习方法：印度农村缺失人口测绘

Mapping Missing Population in Rural India: A Deep Learning Approach with  Satellite Imagery

Knowing where people live is a fundamental component of many decision making
processes such as urban development, infectious disease containment, evacuation
planning, risk management, conservation planning, and more. While bottom-up,
survey driven censuses can provide a comprehensive view into the population
landscape of a country, they are expensive to realize, are infrequently
performed, and only provide population counts over broad areas. Population
disaggregation techniques and population projection methods individually
address these shortcomings, but also have shortcomings of their own. To jointly
answer the questions of "where do people live" and "how many people live
there," we propose a deep learning model for creating high-resolution
population estimations from satellite imagery. Specifically, we train
convolutional neural networks to predict population in the USA at a
$0.01^{\circ} \times 0.01^{\circ}$ resolution grid from 1-year composite
Landsat imagery. We validate these models in two ways: quantitatively, by
comparing our model's grid cell estimates aggregated at a county-level to
several US Census county-level population projections, and qualitatively, by
directly interpreting the model's predictions in terms of the satellite image
inputs. We find that aggregating our model's estimates gives comparable results
to the Census county-level population projections and that the predictions made
by our model can be directly interpreted, which give it advantages over
traditional population disaggregation methods. In general, our model is an
example of how machine learning techniques can be an effective tool for
extracting information from inherently unstructured, remotely sensed data to
provide effective solutions to social problems.

利用深度学习模型对美国每个 0.01 平方度单元格的高分辨率人口估计进行了研究，并比较了直接调查和解聚群体技术的优劣。

卫星图像人口估计的深度学习方法

A Deep Learning Approach for Population Estimation from Satellite  Imagery

Consider a tree network $T$, where each edge acts as an independent copy of a
given channel $M$, and information is propagated from the root. For which $T$
and $M$ does the configuration obtained at level $n$ of $T$ typically contain
significant information on the root variable? This problem arose independently
in biology, information theory and statistical physics.
For all $b$, we construct a channel for which the variable at the root of the
$b$-ary tree is independent of the configuration at level 2 of that tree, yet
for sufficiently large $B>b$, the mutual information between the configuration
at level $n$ of the $B$-ary tree and the root variable is bounded away from
zero. This is related to certain secret-sharing protocols.
We improve the upper bounds on information flow for asymmetric binary
channels (which correspond to the Ising model with an external field) and for
symmetric $q$-ary channels (which correspond to Potts models).
Let $\lam_2(M)$ denote the second largest eigenvalue of $M$, in absolute
value. A CLT of Kesten and Stigum~(1966) implies that if $b |\lam_2(M)|^2 >1$,
then the {\em census} of the variables at any level of the $b$-ary tree,
contains significant information on the root variable. We establish a converse:
if $b |\lam_2(M)|^2 < 1$, then the census of the variables at level $n$ of the
$b$-ary tree is asymptotically independent of the root variable. This contrasts
with examples where $b |\lam_2(M)|^2 <1$, yet the {\em configuration} at level
$n$ is not asymptotically independent of the root variable.

研究了信息在树状网络中的传输，建立了一些二进制和 $ q $ 进制通道模型，并通过特征值的分析来估计其信息流量。