Using GIS databases to simulate night light imagery

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model and other image simulators provide the ability to utilize detailed, artificial scenes to generate spectrally and spatially realistic simulated imagery. Simulated imagery is useful in a myriad of ways, such as sensor modeling, algorithm performance assessment, and others. Actually making synthetic scenes, however, is often a time consuming process, requiring the manual placement of the many objects required to define the scene. This is particularly true for scenes of large spatial extent. Proposed is a technique to generate large-area night scenes for DIRSIG. This is accomplished by using freely available Geographic Information System (GIS) data to inform the placement of street light sources. Results to this point have demonstrated that this technique is a feasible way to model the radiance for large urban areas. This determination was made through comparison to real night time data collected by the Visible Infrared Imaging Radiometer Suite (VIIRS). The methodology is presented as a modular framework, so that future researchers can recreate the work done do this point, with the ability to easily substitute components of the workflow, such as using an alternate source of GIS data or a different simulation environment

Extracting Spatiotemporal Objects From Raster Data To Represent Physical Features and Analyze Related Processes

Numerous ground-based, airborne, and orbiting platforms provide remotely-sensed data of remarkable spatial resolution at short time intervals. However, this spatiotemporal data is most valuable if it can be processed into information, thereby creating meaning. We live in a world of objects: cars, buildings, farms, etc. On a stormy day, we don’t see millions of cubes of atmosphere; we see a thunderstorm ‘object’. Temporally, we don’t see the properties of those individual cubes changing, we see the thunderstorm as a whole evolving and moving. There is a need to represent the bulky, raw spatiotemporal data from remote sensors as a small number of relevant spatiotemporal objects, thereby matching the human brain’s perception of the world. This presentation reveals an efficient algorithm and system to extract the objects/features from raster-formatted remotely-sensed data. The system makes use of the Python object-oriented programming language, SciPy/NumPy for matrix manipulation and scientific computation, and export/import to the GeoJSON standard geographic object data format. The example presented will show how thunderstorms can be identified and characterized in a spatiotemporal continuum using a Python program to process raster data from NOAA’s High-Resolution Rapid Refresh v2 (HRRRv2) data stream

The hexatic phase of the two-dimensional hard disks system

We report Monte Carlo results for the two-dimensional hard disk system in the transition region. Simulations were performed in the NVT ensemble with up to 1024^2 disks. The scaling behaviour of the positional and bond-orientational order parameter as well as the positional correlation length prove the existence of a hexatic phase as predicted by the Kosterlitz-Thouless-Halperin-Nelson-Young theory. The analysis of the pressure shows that this phase is outside a possible first-order transition.Comment: 6 pages, 4 figures (minor changes

Liquid-Vapor Equilibrium of Multicomponent Cryogenic Systems

Liquid-vapor and solid-vapor equilibria at low to moderate pressures and low temperatures are important in many solar system environments, including the surface and clouds of Titan, the clouds of Uranus and Neptune, and the surfaces of Mars and Triton. The familiar cases of ideal behavior are limiting cases of a general thermodynamic representation for the vapor pressure of each component in a homogeneous multicomponent system. The fundamental connections of laboratory measurements to thermodynamic models are through the Gibbs-Duhem relation and the Gibbs-Helmholtz relation. Using laboratory measurements of the total pressure, temperature, and compositions of the liquid and vapor phases at equilibrium, the values of these parameters can be determined. The resulting model for vapor-liquid equilibrium can then conveniently and accurately be used to calculate pressures, compositions, condensation altitudes, and their dependencies on changing climatic conditions. A specific system being investigated is CH4-C2H6-N2, at conditions relevant to Titan's surface and atmosphere. Discussed are: the modeling of existing data on CH4-N2, with applications to the composition of Titan's condensate clouds; some new measurements on the CH4-C2H6 binary, using a high-precision static/volumetric system, and on the C2H6-N2 binary, using the volumetric system and a sensitive cryogenic flow calorimeter; and describe a new cryogenic phase-equilibrium vessel with which we are beginning a detailed, systematic study of the three constituent binaries and the ternary CH4-C2H6-N2 system at temperatures ranging from 80 to 105 K and pressures from 0.1 to 7 bar

Target Detection on Hyperspectral Images Using MCMC and VI Trained Bayesian Neural Networks

Neural networks (NN) have become almost ubiquitous with image classification, but in their standard form produce point estimates, with no measure of confidence. Bayesian neural networks (BNN) provide uncertainty quantification (UQ) for NN predictions and estimates through the posterior distribution. As NN are applied in more high-consequence applications, UQ is becoming a requirement. BNN provide a solution to this problem by not only giving accurate predictions and estimates, but also an interval that includes reasonable values within a desired probability. Despite their positive attributes, BNN are notoriously difficult and time consuming to train. Traditional Bayesian methods use Markov Chain Monte Carlo (MCMC), but this is often brushed aside as being too slow. The most common method is variational inference (VI) due to its fast computation, but there are multiple concerns with its efficacy. We apply and compare MCMC- and VI-trained BNN in the context of target detection in hyperspectral imagery (HSI), where materials of interest can be identified by their unique spectral signature. This is a challenging field, due to the numerous permuting effects practical collection of HSI has on measured spectra. Both models are trained using out-of-the-box tools on a high fidelity HSI target detection scene. Both MCMC- and VI-trained BNN perform well overall at target detection on a simulated HSI scene. This paper provides an example of how to utilize the benefits of UQ, but also to increase awareness that different training methods can give different results for the same model. If sufficient computational resources are available, the best approach rather than the fastest or most efficient should be used, especially for high consequence problems

Short-time dynamics of the positional order of the two-dimensional hard disk system

We investigate the positional order of the two-dimensional hard disk model with short-time dynamics and equilibrium simulations. The melting density and the critical exponents z and eta are determined. Our results rule out a phase transition as predicted by the Kosterlitz-Thouless-Halperin-Nelson-Young theory as well as a first-order transition.Comment: 8 pages, 4 figures, minor change