Deploying a Quantum Annealing Processor to Detect Tree Cover in Aerial Imagery of California

Quantum annealing is an experimental and potentially breakthrough computational technology for handling hard optimization problems, including problems of computer vision. We present a case study in training a production-scale classifier of tree cover in remote sensing imagery, using early-generation quantum annealing hardware built by D-wave Systems, Inc. Beginning within a known boosting framework, we train decision stumps on texture features and vegetation indices extracted from four-band, one-meter-resolution aerial imagery from the state of California. We then impose a regulated quadratic training objective to select an optimal voting subset from among these stumps. The votes of the subset define the classifier. For optimization, the logical variables in the objective function map to quantum bits in the hardware device, while quadratic couplings encode as the strength of physical interactions between the quantum bits. Hardware design limits the number of couplings between these basic physical entities to five or six. To account for this limitation in mapping large problems to the hardware architecture, we propose a truncation and rescaling of the training objective through a trainable metaparameter. The boosting process on our basic 108- and 508-variable problems, thus constituted, returns classifiers that incorporate a diverse range of color- and texture-based metrics and discriminate tree cover with accuracies as high as 92% in validation and 90% on a test scene encompassing the open space preserves and dense suburban build of Mill Valley, CA

Satellite Monitoring of Terrestrial Plastic Waste

Plastic waste is a significant environmental pollutant that is difficult to monitor. We created a system of neural networks to analyze spectral, spatial, and temporal components of Sentinel-2 satellite data to identify terrestrial aggregations of waste. The system works at continental scale. We evaluated performance in Indonesia and detected 374 waste aggregations, more than double the number of sites found in public databases. The same system deployed across twelve countries in Southeast Asia identifies 996 subsequently confirmed waste sites. For each detected site, we algorithmically monitor waste site footprints through time and cross-reference other datasets to generate physical and social metadata. 19% of detected waste sites are located within 200 m of a waterway. Numerous sites sit directly on riverbanks, with high risk of ocean leakage.Comment: 14 pages, 14 figure

Holographic Protection of Chronology in Universes of the Godel Type

We analyze the structure of supersymmetric Godel-like cosmological solutions of string theory. Just as the original four-dimensional Godel universe, these solutions represent rotating, topologically trivial cosmologies with a homogeneous metric and closed timelike curves. First we focus on "phenomenological" aspects of holography, and identify the preferred holographic screens associated with inertial comoving observers in Godel universes. We find that holography can serve as a chronology protection agency: The closed timelike curves are either hidden behind the holographic screen, or broken by it into causal pieces. In fact, holography in Godel universes has many features in common with de Sitter space, suggesting that Godel universes could represent a supersymmetric laboratory for addressing the conceptual puzzles of de Sitter holography. Then we initiate the investigation of "microscopic" aspects of holography of Godel universes in string theory. We show that Godel universes are T-dual to pp-waves, and use this fact to generate new Godel-like solutions of string and M-theory by T-dualizing known supersymmetric pp-wave solutions.Comment: 35 pages, 5 figures. v2: typos corrected, references adde

Holographic Protection of Chronology in Universes of the Godel Type

We analyze the structure of supersymmetric Godel-like cosmological solutions of string theory. Just as the original four-dimensional Godel universe, these solutions represent rotating, topologically trivial cosmologies with a homogeneous metric and closed timelike curves. First we focus on "phenomenological" aspects of holography, and identify the preferred holographic screens associated with inertial comoving observers in Godel universes. We find that holography can serve as a chronology protection agency: The closed timelike curves are either hidden behind the holographic screen, or broken by it into causal pieces. In fact, holography in Godel universes has many features in common with de Sitter space, suggesting that Godel universes could represent a supersymmetric laboratory for addressing the conceptual puzzles of de Sitter holography. Then we initiate the investigation of "microscopic" aspects of holography of Godel universes in string theory. We show that Godel universes are T-dual to pp-waves, and use this fact to generate new Godel-like solutions of string and M-theory by T-dualizing known supersymmetric pp-wave solutions

Holographic Protection of Chronology in Universes of the Godel Type

We analyze the structure of supersymmetric Godel-like cosmological solutions of string theory. Just as the original four-dimensional Godel universe, these solutions represent rotating, topologically trivial cosmologies with a homogeneous metric and closed timelike curves. First we focus on "phenomenological" aspects of holography, and identify the preferred holographic screens associated with inertial comoving observers in Godel universes. We find that holography can serve as a chronology protection agency: The closed timelike curves are either hidden behind the holographic screen, or broken by it into causal pieces. In fact, holography in Godel universes has many features in common with de Sitter space, suggesting that Godel universes could represent a supersymmetric laboratory for addressing the conceptual puzzles of de Sitter holography. Then we initiate the investigation of "microscopic" aspects of holography of Godel universes in string theory. We show that Godel universes are T-dual to pp-waves, and use this fact to generate new Godel-like solutions of string and M-theory by T-dualizing known supersymmetric pp-wave solutions

QuantumAnnealingTreeCoverData.zip

<div>This repository contains datasets used and described in the paper:  "Deploying a Quantum Annealing Processor to Detect Tree Cover in Aerial Imagery of California." doi:10.1371/journal.pone.0172505 </div><div><br></div><div>Full citation: Boyda E, Basu S, Ganguly S, Michaelis A, Mukhopadhyay S, Nemani RR (2017) Deploying a quantum annealing processor to detect tree cover in aerial imagery of California. PLoS ONE 12(2): e0172505.  </div> <div><br></div><div><br></div

Deploying a quantum annealing processor to detect tree cover in aerial imagery of California.

Quantum annealing is an experimental and potentially breakthrough computational technology for handling hard optimization problems, including problems of computer vision. We present a case study in training a production-scale classifier of tree cover in remote sensing imagery, using early-generation quantum annealing hardware built by D-wave Systems, Inc. Beginning within a known boosting framework, we train decision stumps on texture features and vegetation indices extracted from four-band, one-meter-resolution aerial imagery from the state of California. We then impose a regulated quadratic training objective to select an optimal voting subset from among these stumps. The votes of the subset define the classifier. For optimization, the logical variables in the objective function map to quantum bits in the hardware device, while quadratic couplings encode as the strength of physical interactions between the quantum bits. Hardware design limits the number of couplings between these basic physical entities to five or six. To account for this limitation in mapping large problems to the hardware architecture, we propose a truncation and rescaling of the training objective through a trainable metaparameter. The boosting process on our basic 108- and 508-variable problems, thus constituted, returns classifiers that incorporate a diverse range of color- and texture-based metrics and discriminate tree cover with accuracies as high as 92% in validation and 90% on a test scene encompassing the open space preserves and dense suburban build of Mill Valley, CA