Massively-Parallel Break Detection for Satellite Data

The field of remote sensing is nowadays faced with huge amounts of data. While this offers a variety of exciting research opportunities, it also yields significant challenges regarding both computation time and space requirements. In practice, the sheer data volumes render existing approaches too slow for processing and analyzing all the available data. This work aims at accelerating BFAST, one of the state-of-the-art methods for break detection given satellite image time series. In particular, we propose a massively-parallel implementation for BFAST that can effectively make use of modern parallel compute devices such as GPUs. Our experimental evaluation shows that the proposed GPU implementation is up to four orders of magnitude faster than the existing publicly available implementation and up to ten times faster than a corresponding multi-threaded CPU execution. The dramatic decrease in running time renders the analysis of significantly larger datasets possible in seconds or minutes instead of hours or days. We demonstrate the practical benefits of our implementations given both artificial and real datasets.Comment: 10 page

“Robin Hook”: The developmental effects of Somali piracy

Copyright @ 2011 Brunel UniversityNaval counter-piracy measures off Somalia have failed to change the incentives for pirates, raising calls for land-based approaches that may involve replacing piracy as a source of income. This paper evaluates the effects of piracy on the Somali economy to establish which (domestic) groups benefit from ransom monies. Given the paucity of economic data on Somalia, we evaluate province-level market data, nightlight emissions and high resolution satellite imagery. We show that significant amounts of ransom monies are spent within Somalia. The impacts appear to be spread widely, benefiting the working poor and pastoralists and offsetting the food price shock of 2008 in the pirate provinces. Pirates appear to invest their money principally in the main cities of Garowe and Bosasso rather than in the backward coastal communities

Recoiling Massive Black Holes in Gas-Rich Galaxy Mergers

The asymmetric emission of gravitational waves produced during the coalescence of a massive black hole (MBH) binary imparts a velocity "kick" to the system that can displace the hole from the center of its host. Here we study the trajectories and observability of MBHs recoiling in three (one major, two minor) gas-rich galaxy merger remnants that were previously simulated at high resolution, and in which the pairing of the MBHs had been shown to be successful. We run new simulations of MBHs recoiling in the major merger remnant with Mach numbers in the range 1<M<6, and use simulation data to construct a semi-analytical model for the orbital evolution of MBHs in gas-rich systems. We show that: 1) in major merger remnants the energy deposited by the moving hole into the rotationally supported, turbulent medium makes a negligible contribution to the thermodynamics of the gas. This contribution becomes significant in minor merger remnants, potentially allowing for an electromagnetic signature of MBH recoil; 2) in major merger remnants, the combination of both deeper central potential well and drag from high-density gas confines even MBHs with kick velocities as high as 1200 km/s within 1 kpc from the host's center; 3) kinematically offset nuclei may be observable for timescales of a few Myr in major merger remnants in the case of recoil velocities in the range 700-1,000 km/s; 4) in minor mergers remnants the effect of gas drag is weaker, and MBHs with recoil speeds in the range 300-600 km/s will wander through the host halo for longer timescales. When accounting for the probability distribution of kick velocities, however, we find that the likelihood of observing recoiling MBHs in gas-rich galaxy mergers is very low, typically below 10^-5 - 10^-6.Comment: Revised version, accepted for publication in the Astrophysical Journa

"Robin Hook": The Developmental Effects of Somali Piracy

Naval counter-piracy measures off Somalia have failed to change the incentives for pirates, raising calls for land-based approaches that may involve replacing piracy as a source of income. This paper evaluates the effects of piracy on the Somali economy to establish which (domestic) groups benefit from ransom monies. Given the paucity of economic data on Somalia, we evaluate province-level market data, nightlight emissions and high resolution satellite imagery. We show that significant amounts of ransom monies are spent within Somalia. The impacts appear to be spread widely, benefiting the working poor and pastoralists and offsetting the food price shock of 2008 in the pirate provinces. Pirates appear to invest their money principally in the main cities of Garowe and Bosasso rather than in the backward coastal communities.Somalia, piracy, cash transfers, economic development, remote sensing, satellite imaging

HR-SAR-Net: A Deep Neural Network for Urban Scene Segmentation from High-Resolution SAR Data

Synthetic aperture radar (SAR) data is becoming increasingly available to a wide range of users through commercial service providers with resolutions reaching 0.5m/px. Segmenting SAR data still requires skilled personnel, limiting the potential for large-scale use. We show that it is possible to automatically and reliably perform urban scene segmentation from next-gen resolution SAR data (0.15m/px) using deep neural networks (DNNs), achieving a pixel accuracy of 95.19% and a mean IoU of 74.67% with data collected over a region of merely 2.2km${}^2$. The presented DNN is not only effective, but is very small with only 63k parameters and computationally simple enough to achieve a throughput of around 500Mpx/s using a single GPU. We further identify that additional SAR receive antennas and data from multiple flights massively improve the segmentation accuracy. We describe a procedure for generating a high-quality segmentation ground truth from multiple inaccurate building and road annotations, which has been crucial to achieving these segmentation results

A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number