The elimination of poverty worldwide is the first of 17 UN Sustainable Development Goals for the year 2030. To track progress towards this goal, we need more frequent and more reliable data on the distribution of poverty than traditional data collection methods can provide. In this project, we propose an approach that combines machine learning with high-resolution satellite imagery to provide new data on socioeconomic indicators of poverty and wealth. For more information, check out... Our recently published Science paper: http://science.sciencemag.org/content... A project website featuring poverty maps of Nigeria, Tanzania, Uganda, Malawi, and Rwanda: http://sustain.stanford.edu/predictin...

Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries--Nigeria, Tanzania, Uganda, Malawi, and Rwanda--we show how a convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains.

This paper considers \emph{volume minimization} (VolMin)-based structured matrix factorization (SMF). VolMin is a factorization criterion that decomposes a given data matrix into a basis matrix times a structured coefficient matrix via finding the minimum-volume simplex that encloses all the columns of the data matrix. Recent work showed that VolMin guarantees the identifiability of the factor matrices under mild conditions that are realistic in a wide variety of applications. This paper focuses on both theoretical and practical aspects of VolMin. On the theory side, exact equivalence of two independently developed sufficient conditions for VolMin identifiability is proven here, thereby providing a more comprehensive understanding of this aspect of VolMin. On the algorithm side, computational complexity and sensitivity to outliers are two key challenges associated with real-world applications of VolMin. These are addressed here via a new VolMin algorithm that handles volume regularization in a computationally simple way, and automatically detects and {iteratively downweights} outliers, simultaneously. Simulations and real-data experiments using a remotely sensed hyperspectral image and the Reuters document corpus are employed to showcase the effectiveness of the proposed algorithm.

DigitalGlobe was the company that convinced the US government to lift its image resolution restrictions on private satellites. Shortly after, it launched its WorldView-3 constellation that can detect images as small as 12 inches (30cm) across. It can also scan short-wave infrared frequencies, letting it see forest fires through smoke that would block other satellites, for instance. There's no mention of Uber's ambitious self-driving vehicles in relation to the high-resolution imagery, but mapping is clearly key to the program. And unlike Google Maps or other sat views, DigitalGlobe can provide current maps with more detail than other private systems.

Machine learning artificial intelligence has unlocked big data as a source of military, weather and business intelligence that has opened up multiple options. Social Media giants Twitter and Facebook have been spending millions trying to keep their companies ahead of the flock, highlighted by Twitter Buys Machine Learning Artificial Intelligence Star Magic Pony Technology Pavel Machalek co-founder of Silicon Valley data analytics firm Spaceknow working with commercial satellite data says the convergence of computing power, machine learning and satellite imagery is a perfect storm that s just beginning to peak, ... We could not have done this five years ago. Chinese government economic reports are notoriously inaccurate. Spaceknow's China Satellite Manufacturing Index uses satellite imagery to monitor changes at 6,000 industrial facilities in China as an alternative. The above image courtesy of DigitalGlobe shows how geospatial data companies can track activity by identifying surface material as seen here with individual trees in a forest (above) and aircrafts on the tarmac (below).

In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing. Although kernelization or sparse methods are globally accepted solutions for processing data in high dimensions, we present here a study on the impact of the form of regularization used and its parametrization. We consider regularization via traditional squared (2) and sparsity-promoting (1) norms, as well as more unconventional nonconvex regularizers (p and Log Sum Penalty). We compare their properties and advantages on several classification and linear unmixing tasks and provide advices on the choice of the best regularizer for the problem at hand. Finally, we also provide a fully functional toolbox for the community.

Terrapattern is a fun prototype that lets you search satellite imagery simply by clicking on a map. For example, you can click on a tennis court, and through machine learning, the application looks for similar areas. Terrapattern uses a deep convolutional neural network (DCNN), based on the ResNet ("Residual Network") architecture developed by Kaiming He et al. We trained a 34-layer DCNN using hundreds of thousands of satellite images labeled in OpenStreetMap, teaching the neural network to predict the category of a place from a satellite photo. In the process, our network learned which high-level visual features (and combinations of those features) are important for the classification of satellite imagery.

Right now, Terrapattern only covers four American cities: Pittsburgh, Detroit, San Francisco, and New York City. Terrapattern is so computing-hungry that it is effectively a proof of concept right now, at least for a team of artists working with less than 35,000. Each metro region takes about 10 gigabytes of RAM--not storage, but active memory. That said, Terrapattern is relatively technically straightforward. It's constructed from a convolutional neural network and CoverTree, an algorithm that remembers some descriptions and allows the searches to happen quickly.

In 2008, through something of a happy accident, a team of zoologists from the University of Duisburg-Essen in Germany discovered that grazing cows and deer tend to align their bodies with magnetic north. It was an odd thing to notice, particularly because the researchers had been perusing satellite imagery for something else entirely. But that's what happens when you look at something from 400 miles above the Earth's surface--change your perspective, and you'll change what you see. When Golan Levin, a professor of new media art at Carnegie Mellon University, heard about the cow discovery, he found it "to be simultaneously wonderful and very inspiring and totally useless." He was also overcome, he says, by the desire to make similar discoveries.

EXCLUSIVE: New satellite imagery obtained by Fox News shows that China, for the first time, has deployed a drone with stealth technology to a contested island in the South China Sea, in another sign of escalating tensions in the region. The new development comes as President Obama visits Japan. He lifted an arms embargo against Vietnam while visiting Hanoi earlier this week, drawing criticism from the Chinese government about stoking tensions in the region. The newly obtained satellite images from ImageSat International (ISI) show a Chinese Harbin BZK-005 long range reconnaissance drone on Woody Island in the South China Sea. The Chinese drone did not appear armed in the satellite image taken last month.