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.

The 2016 IEEE GRSS Data Fusion Contest, organized by the IADF TC, was opened on January 3, 2016. The submission deadline was April 29, 2016. Participants submitted open topic manuscripts using the VHR and video-from-space data released for the competition. Evaluation and ranking were conducted by the Award Committee. The winners are reported below along with the abstracts of the submitted papers.

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.

According to the United Nations (UN), more than 12 million people--including 5.6 million children--have fled Syria to escape the horrors of the country's ongoing civil war and invasion by ISIS. Worldwide, the UN reports an unprecedented 59.5 million people are displaced by crisis. The flow of refugees toward Europe from Syria and other war-torn nations has caused the continent's greatest refugee crisis since World War II. Finland-based Lucify, which creates interactive data visualizations to help organizations analyze and communicate important data, recently tackled the refugee migration to Europe. Using UN data from 2012 through December 2015, its interactive map offers a time-lapse view of refugee migration and country-by-country statistics.