Estimating snow cover from publicly available images

In this paper we study the problem of estimating snow cover in mountainous regions, that is, the spatial extent of the earth surface covered by snow. We argue that publicly available visual content, in the form of user generated photographs and image feeds from outdoor webcams, can both be leveraged as additional measurement sources, complementing existing ground, satellite and airborne sensor data. To this end, we describe two content acquisition and processing pipelines that are tailored to such sources, addressing the specific challenges posed by each of them, e.g., identifying the mountain peaks, filtering out images taken in bad weather conditions, handling varying illumination conditions. The final outcome is summarized in a snow cover index, which indicates for a specific mountain and day of the year, the fraction of visible area covered by snow, possibly at different elevations. We created a manually labelled dataset to assess the accuracy of the image snow covered area estimation, achieving 90.0% precision at 91.1% recall. In addition, we show that seasonal trends related to air temperature are captured by the snow cover index.Comment: submitted to IEEE Transactions on Multimedi

RRR: Rank-Regret Representative

Selecting the best items in a dataset is a common task in data exploration. However, the concept of "best" lies in the eyes of the beholder: different users may consider different attributes more important, and hence arrive at different rankings. Nevertheless, one can remove "dominated" items and create a "representative" subset of the data set, comprising the "best items" in it. A Pareto-optimal representative is guaranteed to contain the best item of each possible ranking, but it can be almost as big as the full data. Representative can be found if we relax the requirement to include the best item for every possible user, and instead just limit the users' "regret". Existing work defines regret as the loss in score by limiting consideration to the representative instead of the full data set, for any chosen ranking function. However, the score is often not a meaningful number and users may not understand its absolute value. Sometimes small ranges in score can include large fractions of the data set. In contrast, users do understand the notion of rank ordering. Therefore, alternatively, we consider the position of the items in the ranked list for defining the regret and propose the {\em rank-regret representative} as the minimal subset of the data containing at least one of the top-$k$ of any possible ranking function. This problem is NP-complete. We use the geometric interpretation of items to bound their ranks on ranges of functions and to utilize combinatorial geometry notions for developing effective and efficient approximation algorithms for the problem. Experiments on real datasets demonstrate that we can efficiently find small subsets with small rank-regrets

The star formation histories of galaxies in the Sloan Digital Sky Survey

We present the results of a MOPED analysis of ~3 x 10^5 galaxy spectra from the Sloan Digital Sky Survey Data Release Three (SDSS DR3), with a number of improvements in data, modelling and analysis compared with our previous analysis of DR1. The improvements include: modelling the galaxies with theoretical models at a higher spectral resolution of 3\AA; better calibrated data; an extended list of excluded emission lines, and a wider range of dust models. We present new estimates of the cosmic star formation rate, the evolution of stellar mass density and the stellar mass function from the fossil record. In contrast to our earlier work the results show no conclusive peak in the star formation rate out to a redshift around 2 but continue to show conclusive evidence for `downsizing' in the SDSS fossil record. The star formation history is now in good agreement with more traditional instantaneous measures. The galaxy stellar mass function is determined over five decades of mass, and an updated estimate of the current stellar mass density is presented. We also investigate the systematic effects of changes in the stellar population modelling, the spectral resolution, dust modelling, sky lines, spectral resolution and the change of data set. We find that the main changes in the results are due to the improvements in the calibration of the SDSS data, changes in the initial mass function and the theoretical models used.Comment: replaced to match accepted version in MNRA

The DEEP2 Redshift Survey: Lyman Alpha Emitters in the Spectroscopic Database

We present the first results of a search for Lyman-alpha emitters (LAEs) in the DEEP2 spectroscopic database that uses a search technique that is different from but complementary to traditional narrowband imaging surveys. We have visually inspected ~20% of the available DEEP2 spectroscopic data and have found nine high-quality LAEs with clearly asymmetric line profiles and an additional ten objects of lower quality, some of which may also be LAEs. Our survey is most sensitive to LAEs at z=4.4-4.9 and that is indeed where all but one of our high-quality objects are found. We find the number density of our spectroscopically-discovered LAEs to be consistent with those found in narrowband imaging searches. The combined, averaged spectrum of our nine high-quality objects is well fit by a two-component model, with a second, lower-amplitude component redshifted by ~420 km/s with respect to the primary Lyman-alpha line, consistent with large-scale outflows from these objects. We conclude by discussing the advantages and future prospects of blank-sky spectroscopic surveys for high-z LAEs.Comment: Accepted for publication in Ap