The Very Large Array Sky Survey (VLASS) is a synoptic, all-sky radio sky survey with a unique combination of high angular resolution (≈2 5), sensitivity (a 1σ goal of 70 μJy/beam in the coadded data), full linear Stokes polarimetry, time domain coverage, and wide bandwidth (2-4 GHz). The first observations began in 2017 September, and observing for the survey will finish in 2024. VLASS will use approximately 5500 hr of time on the Karl G. Jansky Very Large Array (VLA) to cover the whole sky visible to the VLA (decl. >−40°), a total of 33 885deg 2. The data will be taken in three epochs to allow the discovery of variable and transient radio sources. The survey is designed to engage radio astronomy experts, multi-wavelength astronomers, and citizen scientists alike. By utilizing an "on the fly" interferometry mode, the observing overheads are much reduced compared to a conventional pointed survey. In this paper, we present the science case and observational strategy for the survey, and also results from early survey observations.

EMU is a wide-field radio continuum survey planned for the new Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The primary goal of EMU is to make a deep (rms ,10 mJy/beam) radio continuum survey of the entire Southern sky at 1.3 GHz, extending as far North as þ308 declination, with a resolution of 10 arcsec. EMU is expected to detect and catalogue about 70 million galaxies, including typical star-forming galaxies up to z , 1, powerful starbursts to even greater redshifts, and active galactic nuclei to the edge of the visible Universe. It will undoubtedly discover new classes of object. This paper defines the science goals and parameters of the survey, and describes the development of techniques necessary to maximise the science return from EMU.

We present blobcat, new source extraction software that utilizes the flood fill algorithm to detect and catalogue blobs, or islands of pixels representing sources, in 2D astronomical images. The software is designed to process radio‐wavelength images of both Stokes I intensity and linear polarization, the latter formed through the quadrature sum of Stokes Q and U intensities or as a by‐product of rotation measure synthesis. We discuss an objective, automated method by which estimates of position‐dependent background root mean square noise may be obtained and incorporated into blobcat's analysis. We derive and implement within blobcat corrections for two systematic biases to enable the flood fill algorithm to accurately measure flux densities for Gaussian sources. We discuss the treatment of non‐Gaussian sources in light of these corrections. We perform simulations to validate the flux density and positional measurement performance of blobcat, and we benchmark the results against those of a standard Gaussian fitting task. We demonstrate that blobcat exhibits accurate measurement performance in total intensity and, in particular, linear polarization. blobcat is particularly suited to the analysis of large survey data.