The Southern Photometric Local Universe Survey (S-PLUS) is imaging ∼9300 deg² of the celestial sphere in 12 optical bands using a dedicated 0.8 m robotic telescope, the T80-South, at the Cerro Tololo Inter-american Observatory, Chile. The telescope is equipped with a 9.2k × 9.2k e2v detector with 10 |$\rm {\mu m}$| pixels, resulting in a field of view of 2 deg² with a plate scale of 0.55 arcsec pixel⁻¹. The survey consists of four main subfields, which include two non-contiguous fields at high Galactic latitudes (|b| > 30°, 8000 deg²) and two areas of the Galactic Disc and Bulge (for an additional 1300 deg²). S-PLUS uses the Javalambre 12-band magnitude system, which includes the 5 ugriz broad-band filters and 7 narrow-band filters centred on prominent stellar spectral features: the Balmer jump/[OII], Ca H + K, H δ, G band, Mg b triplet, H α, and the Ca triplet. S-PLUS delivers accurate photometric redshifts (δ_z/(1 + z) = 0.02 or better) for galaxies with r < 19.7 AB mag and z < 0.4, thus producing a 3D map of the local Universe over a volume of more than |$1\, (\mathrm{Gpc}/h)^3$|⁠. The final S-PLUS catalogue will also enable the study of star formation and stellar populations in and around the Milky Way and nearby galaxies, as well as searches for quasars, variable sources, and low-metallicity stars. In this paper we introduce the main characteristics of the survey, illustrated with science verification data highlighting the unique capabilities of S-PLUS. We also present the first public data release of ∼336 deg² of the Stripe 82 area, in 12 bands, to a limiting magnitude of r = 21, available at datalab.noao.edu/splus.

Feature Selection (FS) is the process of identifying and separating relevant features of a dataset to obtain the best solutions for a pattern-classification or regression problem. The main benefits of FS include more accurate classification models, simplified interpretation of models and a reduction in the processing time required for classification. One of the main approaches used in FS involves wrappers. In this approach features are selected based on an evaluation performed by a classification algorithm. Recently, an optimization bio-inspired metaheuristic based on the intelligent behavior of crows called Crow Search Algorithm (CSA) has been proposed. CSA works based on the idea that crows store their excess food in hiding places and retrieve it when the food is needed. The main reasons of using CSA are its easy implementation, few control parameters to adjust, fast convergence speed and high efficiency. To further enhance the performance of the classical CSA algorithm, this paper proposes a new wrapper based in a “v-shaped” binarization of the CSA. The wrapper, which is referred to here as Binary CSA (BCSA), is applied to six benchmark data sets. The paper compares and discusses the advantages and disadvantages of the proposed technique in terms of classification accuracy, number of selected features and computational cost against some classical and state-of-art algorithms. The results were encouraging and showed that BCSA achieved very good results in terms of classification accuracy and also selected subsets with a small number of features with a relatively low computational cost.