Effective spatio-temporal measurements of water surface elevation (water waves) in laboratory experiments are essential for scientific and engineering research. Existing techniques are often cumbersome, computationally heavy and generally suffer from limited wavenumber/frequency response. To address these challenges a novel method was developed, using polarization filter equipped camera as the main sensor and Machine Learning (ML) algorithms for data processing [1,2]. The developed method training and evaluation was based on in-house made supervised dataset. Here we present this supervised dataset of polarimetric images of the water surface coupled with the water surface elevation measurements made by a linear array of resistance-type wave gauges (WG). The water waves were mechanically generated in a laboratory waves basin, and the polarimetric images were captured under an artificial light source. Meticulous camera and WGs calibration and instruments synchronization supported high spatio-temporal resolution. The data set covers several wavefield conditions, from simple monochromatic wave trains of various steepness, to irregular wavefield of JONSWAP prescribed spectral shape and several wave breaking scenarios. The dataset contains measurements repeated in several camera positions relative to the wave field propagation direction.

MNRAS 000, 1-7 (2019) Preprint 20 November 2019 Compiled using MNRAS L A T EX style file v3.0 Eliminating artefacts in Polarimetric Images using Deep Learning D. Paranjpye, 1 null A. Mahabal, 2 A.N. Ramaprakash, 3 G. Received YYY; in original form ZZZ ABSTRACT Polarization measurements done using Imaging Polarimeters such as the Robotic Polarimeter are very sensitive to the presence of artefacts in images. Artefacts can range from internal reflections in a telescope to satellite trails that could contaminate an area of interest in the image. With the advent of wide-field polarimetry surveys, it is imperative to develop methods that automatically flag artefacts in images. In this paper, we implement a Convolutional Neural Network to identify the most dominant artefacts in the images. We find that our model can successfully classify sources with 98% true positive and 97% true negative rates. Such models, combined with transfer learning, will give us a running start in artefact elimination for near-future surveys like W ALOP. Key words: deep learning - image classification - artefact detection - polarimetry 1 INTRODUCTION RoboPol (Ramaprakash et al. 2019) is a four-channel optical polarimeter installed on the 1.3m telescope at the Ski-nakas Observatory in Crete, Greece that is primarily used for polarimetry of point sources in the R band.