Connecting the time domain community with the Virtual Astronomical Observatory

The time domain has been identified as one of the most important areas of astronomical research for the next decade. The Virtual Observatory is in the vanguard with dedicated tools and services that enable and facilitate the discovery, dissemination and analysis of time domain data. These range in scope from rapid notifications of time-critical astronomical transients to annotating long-term variables with the latest modeling results. In this paper, we will review the prior art in these areas and focus on the capabilities that the VAO is bringing to bear in support of time domain science. In particular, we will focus on the issues involved with the heterogeneous collections of (ancillary) data associated with astronomical transients, and the time series characterization and classification tools required by the next generation of sky surveys, such as LSST and SKA.Comment: Submitted to Proceedings of SPIE Observatory Operations: Strategies, Processes and Systems IV, Amsterdam, 2012 July 2-

Automated Satellite-Based Landslide Identification Product for Nepal

Landslide event inventories are a vital resource for landslide susceptibility and forecasting applications. However, landslide inventories can vary in accuracy, availability, and timeliness as a result of varying detection methods, reporting, and data availability. This study presents an approach to use publicly available satellite data and open source software to automate a landslide detection process called the Sudden Landslide Identification Product (SLIP). SLIP utilizes optical data from the Landsat 8 OLI sensor, elevation data from the Shuttle Radar Topography Mission (SRTM), and precipitation data from the Global Precipitation Measurement (GPM) mission to create a reproducible and spatially customizable landslide identification product. The SLIP software applies change detection algorithms to identify areas of new bare-earth exposures that may be landslide events. The study also presents a precipitation monitoring tool that runs alongside SLIP called the Detecting Real-time Increased Precipitation (DRIP) model that helps identify the timing of potential landslide events detected by SLIP. Using SLIP and DRIP together, landslide detection is improved by reducing problems related to accuracy, availability, and timeliness that are prevalent in the state-of-the-art of landslide detection. A case study and validation exercise was performed in Nepal for images acquired between 2014 and 2015. Preliminary validation results suggest 56% model accuracy, with errors of commission often resulting from newly cleared agricultural areas. These results suggest that SLIP is an important first attempt in an automated framework that can be used for medium resolution regional landslide detection, although it requires refinement before being fully realized as an operational tool

Application of advanced technology to space automation

Automated operations in space provide the key to optimized mission design and data acquisition at minimum cost for the future. The results of this study strongly accentuate this statement and should provide further incentive for immediate development of specific automtion technology as defined herein. Essential automation technology requirements were identified for future programs. The study was undertaken to address the future role of automation in the space program, the potential benefits to be derived, and the technology efforts that should be directed toward obtaining these benefits

An Automated tool to detect variable sources in the Vista Variables in the Vía Láctea Survey. The VVV Variables (V^4) catalog of tiles d001 and d002

27 pages, 19 figuresTime-varying phenomena are one of the most substantial sources of astrophysical information, and their study has led to many fundamental discoveries in modern astronomy. We have developed an automated tool to search for and analyze variable sources in the near-infrared K s band using the data from the VISTA Variables in the Vía Láctea (VVV) ESO Public Large Survey. This process relies on the characterization of variable sources using different variability indices calculated from time series generated with point-spread function (PSF) photometry of sources under analysis. In particular, we used two main indices, the total amplitude and the eta index η, to identify variable sources. Once the variable objects are identified, periods are determined with generalized Lomb-Scargle periodograms and the information potential metric. Variability classes are assigned according to a compromise between comparisons with VVV templates and the period of the variability. The automated tool is applied on VVV tiles d001 and d002 and led to the discovery of 200 variable sources. We detected 70 irregular variable sources and 130 periodic ones. In addition, nine open-cluster candidates projected in the region are analyzed, and the infrared variable candidates found around these clusters are further scrutinized by cross-matching their locations against emission star candidates from VPHAS+ survey H α color cuts.Peer reviewedFinal Accepted Versio

An automated deep learning pipeline based on advanced optimisations for leveraging spectral classification modelling

Na modelagem de deep learning (DL) para dados espectrais, um grande desafio está relacionado à escolha da arquitetura de rede DL e à seleção dos melhores hiperparmetros. Muitas vezes, pequenas mudanças na arquitetura neural ou seu hiperparômetro podem ter uma influência direta no desempenho do modelo, tornando sua robustez questionável. Para lidar com isso, este estudo apresenta uma modelagem automatizada de aprendizagem profunda baseada em técnicas avançadas de otimização envolvendo hyperband e otimização bayesiana, para encontrar automaticamente a arquitetura neural ideal e seus hiperparmetros para alcançar modelos robustos de DL. A otimização requer uma arquitetura neural base para ser inicializada, no entanto, mais tarde, ajusta automaticamente a arquitetura neural e os hiperparmetros para alcançar o modelo ideal. Além disso, para apoiar a interpretação dos modelos DL, foi implementado um esquema de pesagem de comprimento de onda baseado no mapeamento de ativação de classe ponderada por gradiente (Grad-CAM). O potencial da abordagem foi mostrado em um caso real de classificação da variedade de trigo com dados espectrais quase infravermelhos. O desempenho da classificação foi comparado com o relatado anteriormente no mesmo conjunto de dados com diferentes abordagens DL e quimiométrica. Os resultados mostraram que, com a abordagem proposta, foi alcançada uma precisão de classificação de 94,9%, melhor do que a melhor precisão relatada no mesmo conjunto de dados, ou seja, 93%. Além disso, o melhor desempenho foi obtido com uma arquitetura neural mais simples em comparação com o que foi usado em estudos anteriores. O deep learning automatizado baseado na otimização avançada pode suportar a modelagem DL de dados espectrais.info:eu-repo/semantics/publishedVersio

Pattern recognition of satellite cloud imagery for improved weather prediction

The major accomplishment was the successful development of a method for extracting time derivative information from geostationary meteorological satellite imagery. This research is a proof-of-concept study which demonstrates the feasibility of using pattern recognition techniques and a statistical cloud classification method to estimate time rate of change of large-scale meteorological fields from remote sensing data. The cloud classification methodology is based on typical shape function analysis of parameter sets characterizing the cloud fields. The three specific technical objectives, all of which were successfully achieved, are as follows: develop and test a cloud classification technique based on pattern recognition methods, suitable for the analysis of visible and infrared geostationary satellite VISSR imagery; develop and test a methodology for intercomparing successive images using the cloud classification technique, so as to obtain estimates of the time rate of change of meteorological fields; and implement this technique in a testbed system incorporating an interactive graphics terminal to determine the feasibility of extracting time derivative information suitable for comparison with numerical weather prediction products

Capabilities of the NASA/IPAC Extragalactic Database (NED) in the Era of a Global Virtual Observatory

We review the capabilities of the NASA/IPAC Extragalactic Database (NED, http://ned.ipac.caltech.edu) for information retrieval and knowledge discovery in the context of a globally distributed virtual observatory. Since its inception in 1990, NED has provided astronomers world-wide with the results of a systematic cross-correlation of catalogs covering all wavelengths, along with thousands of extragalactic observations culled from published journal articles. NED is continuously being expanded and revised to include new catalogs and published observations, each undergoing a process of cross-identification to capture the current state of knowledge about extragalactic sources in a panchromatic fashion. In addition to assimilating data from the literature, the team is incrementally folding in millions of observations from new large-scale sky surveys such as 2MASS, NVSS, APM, and SDSS. At the time of writing the system contains over 3.3 million unique objects with 4.2 million cross-identifications. We summarize the recent evolution of NED from its initial emphasis on object name-, position-, and literature-based queries into a research environment that also assists statistical data exploration and discovery using large samples of objects. Newer capabilities enable "Web mining" of entries in geographically distributed astronomical archives that are indexed by object names and positions in NED, sample building using constraints on redshifts and object types, and an image archive. A pilot study demonstrates how NED is being used in conjunction with linked survey archives to characterize the properties of galaxy classes to form a training set for machine learning algorithms. Opportunities for tighter integration of NED capabilities into data mining tools for astronomy archives are also discussed.Comment: 15 pages, 6 figures; astro-ph file size limits required extensive degradation of the quality of the figures. A version with the original high resolution color Postscript figures is available in LEVEL5 at http://ned.ipac.caltech.edu/level5/Sept01/Mazzarella/NED_2001_SPIE_Mazz.ps.g

Literature review of the remote sensing of natural resources

Abstracts of 596 documents related to remote sensors or the remote sensing of natural resources by satellite, aircraft, or ground-based stations are presented. Topics covered include general theory, geology and hydrology, agriculture and forestry, marine sciences, urban land use, and instrumentation. Recent documents not yet cited in any of the seven information sources used for the compilation are summarized. An author/key word index is provided