VISTA Variables in the Via Lactea (VVV): The public ESO near-IR variability survey of the Milky Way

We describe the public ESO near-IR variability survey (VVV) scanning the Milky Way bulge and an adjacent section of the mid-plane where star formation activity is high. The survey will take 1929 h of observations with the 4-m VISTA telescope during 5 years (2010–2014), covering ～10⁹ point sources across an area of 520 deg², including 33 known globular clusters and ～350 open clusters. The final product will be a deep near-IR atlas in five passbands (0.9–2.5 μm) and a catalogue of more than 10⁶ variable point sources. Unlike single-epoch surveys that, in most cases, only produce 2-D maps, the VVV variable star survey will enable the construction of a 3-D map of the surveyed region using well-understood distance indicators such as RR Lyrae stars, and Cepheids. It will yield important information on the ages of the populations. The observations will be combined with data from MACHO, OGLE, EROS, VST, Spitzer, HST, Chandra, INTEGRAL, WISE, Fermi LAT, XMM-Newton, GAIA and ALMA for a complete understanding of the variable sources in the inner Milky Way. This public survey will provide data available to the whole community and therefore will enable further studies of the history of the Milky Way, its globular cluster evolution, and the population census of the Galactic Bulge and center, as well as the investigations of the star forming regions in the disk. The combined variable star catalogues will have important implications for theoretical investigations of pulsation properties of stars.     

A Hubble Space Telescope/NICMOS view of the prototypical giant Hii region NGC604 in M33

We present the first high-spatial-resolution near-infrared (NIR) imaging of NGC604, obtained with the NICMOS camera onboard the Hubble Space Telescope (HST). These NICMOS broad-band images reveal new NIR point sources, clusters, and diffuse structures. We find an excellent spatial correlation between the 8.4 GHz radio continuum and the 2.2 μm nebular emission. Moreover, massive young stellar object candidates appear aligned with these radio peaks, reinforcing the idea that those areas are star-forming regions. Three different scaled OB associations are recognized in the NICMOS images. The brightest NIR sources in our images have properties that suggest that they are red supergiant stars, of which one was previously known. This preliminary analysis of the NICMOS images shows the complexity of the stellar content of the NGC604 nebula.     

Visual-Semantic Modeling in Content-Based Geospatial Information Retrieval Using Associative Mining Techniques

Automatic learning of geospatial intelligence is challenging due to the complexity of articulating knowledge from visual patterns and to the ever-increasing quantities of image data generated on a daily basis. In this setting, human inspection and annotation is subjective and, more importantly, impractical. In this letter, we propose a knowledge-discovery algorithm that uses content-based methods to link low-level image features with high-level visual semantics in an effort to automate the process of retrieving semantically similar images. Our algorithm represents geospatial images by using a high-dimensional feature vector and generates a set of association rules that correlate semantic terms with visual patterns represented by discrete feature intervals. We also provide a mathematical model to customize the relevance of feature measurements to semantic assignments as well as methods of querying by semantics and by example.