Context. Precise localization and characterization of active regions (AR) and coronal holes (CH) as observed by extreme ultra violet (EUV) imagers are crucial for a wide range of solar and helio-physics studies. Aims. We introduce a set of segmentation procedures (known as the SPoCA-suite) that allows one to retrieve AR and CH properties on EUV images taken from SOHO-EIT, STEREO-EUVI, PROBA2-SWAP, and SDO-AIA. Methods. We build upon our previous work on the Spatial Possibilistic Clustering Algorithm (SPoCA), that we have improved substantially in several ways. Results. We apply our algorithm on the synoptic EIT archive from 1997 to 2011 and decompose this dataset into regions that can clearly be identified as AR, quiet Sun, and CH. An antiphase between AR and CH filling factor is observed, as expected. The SPoCAsuite is next applied to datasets from EUVI, SWAP, and AIA. The time series pertaining to ARs or CHs are presented. Conclusions. The SPoCA-suite enables the extraction of several long time series of AR and CH properties from the data files of EUV imagers and also allows tracking individual ARs or CHs over time. For AIA images, AR and CH catalogs are available in near-real time from the Heliophysics Events Knowledgebase. The full code, which allows processing any EUV images, is available upon request to the authors.

Context. The Extreme Ultraviolet Imager (EUI) is part of the remote sensing instrument package of the ESA/NASA Solar Orbiter mission that will explore the inner heliosphere and observe the Sun from vantage points close to the Sun and out of the ecliptic. Solar Orbiter will advance the “connection science” between solar activity and the heliosphere. Aims. With EUI we aim to improve our understanding of the structure and dynamics of the solar atmosphere, globally as well as at high resolution, and from high solar latitude perspectives. Methods. The EUI consists of three telescopes, the Full Sun Imager and two High Resolution Imagers, which are optimised to image in Lyman-α and EUV (17.4 nm, 30.4 nm) to provide a coverage from chromosphere up to corona. The EUI is designed to cope with the strong constraints imposed by the Solar Orbiter mission characteristics. Limited telemetry availability is compensated by state-of-the-art image compression, onboard image processing, and event selection. The imposed power limitations and potentially harsh radiation environment necessitate the use of novel CMOS sensors. As the unobstructed field of view of the telescopes needs to protrude through the spacecraft’s heat shield, the apertures have been kept as small as possible, without compromising optical performance. This led to a systematic effort to optimise the throughput of every optical element and the reduction of noise levels in the sensor. Results. In this paper we review the design of the two elements of the EUI instrument: the Optical Bench System and the Common Electronic Box. Particular attention is also given to the onboard software, the intended operations, the ground software, and the foreseen data products. Conclusions. The EUI will bring unique science opportunities thanks to its specific design, its viewpoint, and to the planned synergies with the other Solar Orbiter instruments. In particular, we highlight science opportunities brought by the out-of-ecliptic vantage point of the solar poles, the high-resolution imaging of the high chromosphere and corona, and the connection to the outer corona as observed by coronagraphs.

Abstract. This paper presents SunPy (version 0.5), a community-developed Python package for solar physics. Python, a free, cross-platform, general-purpose, highlevel programming language, has seen widespread adoption among the scientific community, resulting in the availability of a large number of software packages,

The algorithms involved in this study are as follows:1. The Solar Monitor Active Region Tracker (SMART) extracts, characterises, and tracks the evolution of active regions across the solar disk using line-of-sight magnetograms and a combination of image processing techniques. 2. The Automated Solar Activity Prediction code (ASAP) converts continuum images from heliocentric coordinates to Carrington heliographic coordinates, detects and tracks sunspots using thresholding and morphological methods. 3. The Sunspot Tracking And Recognition Algorithm (STARA) is used to detect and track sunspots from continuum images using a technique known as the top-hat transform. 4. The Spatial Possibilistic Clustering Algorithm (SPoCA) is a multi-channel unsupervised spatiallyconstrained fuzzy clustering method that automatically segments solar EUV images into active regions, coronal holes and quiet Sun. In the present paper, it is used to detect, characterise and track coronal active regions.We describe the fundamental properties of each algorithm along with a detailed comparison of outputs obtained from the analysis of about one month of data from the SOHO-MDI and SOHO-EIT instruments during 12 May -23 June, 2003. We track two active regions over time to study their properties in detail, and exploit the entire dataset to investigate correlations between physical properties determined by the algorithms. This study allows us to prepare the algorithms in the best possible way for robust analysis of the large SDO data-stream.The detection rates of the algorithms are compared with findings of the National Oceanic and Atmospheric Administration (NOAA) and the Solar Influences Data Analysis Centre (SIDC). By performing an inter-comparison of the algorithms, the physical properties of the solar features detected are measured at different heights of the solar atmosphere. Solar Physics DOI: 10.1007/•••••-•••-•••-••••-•A multi-wavelength analysis of active regions and sunspots by comparison of automatic detection algorithmsThe launch of the Solar Dynamics Observatory (SDO) in early 2010 has provided the solar physics community with the most detailed view of the Sun to date. However, this presents new challenges for the analysis of solar data. Currently, SDO sends over 1 terabyte of data per day back to Earth and methods for fast and reliable analysis are more important than ever. This article details four algorithms developed separately at the Universities of Bradford and Glasgow, the Royal Observatory of Belgium and Trinity College Dublin for the purposes of automated detection of solar active regions (ARs) and sunspots at different levels of the solar atmosphere.The algorithms involved in this study are as follows:1. The Solar Monitor Active Region Tracker (SMART) extracts, characterises, and tracks the evolution of active regions across the solar disk using line-ofsight magnetograms and a combination of image processing techniques. 2. The Automated Solar Activity Prediction code (ASAP) converts continuum images from heliocentric coordin...

We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA), we prepared datasets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2011-2013. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed shape measures from the segmented binary maps as well as first order and second order texture statistics from the segmented regions in the EUV images and magnetograms. These attributes were used for data mining investigations to identify the most performant rule to differentiate between coronal holes and filament channels. We applied several classifiers, namely Support Vector Machine (SVM), Linear Support Vector Machine, Decision Tree, and Random Forest, and found that all classification rules achieve good results in general, with linear SVM providing the best performances (with a true skill statistic of % 0.90). Additional information from magnetic field data systematically improves the performance across all four classifiers for the SPoCA detection. Since the calculation is inexpensive in computing time, this approach is well suited for applications on real-time data. This study demonstrates how a machine learning approach may help improve upon an unsupervised feature extraction method.