Hierarchical progressive surveys. Multi-resolution HEALPix data structures for astronomical images, catalogues, and 3-dimensional data cubes

Scientific exploitation of the ever increasing volumes of astronomical data requires efficient and practical methods for data access, visualisation, and analysis. Hierarchical sky tessellation techniques enable a multi-resolution approach to organising data on angular scales from the full sky down to the individual image pixels. Aims. We aim to show that the Hierarchical progressive survey (HiPS) scheme for describing astronomical images, source catalogues, and three-dimensional data cubes is a practical solution to managing large volumes of heterogeneous data and that it enables a new level of scientific interoperability across large collections of data of these different data types. Methods. HiPS uses the HEALPix tessellation of the sphere to define a hierarchical tile and pixel structure to describe and organise astronomical data. HiPS is designed to conserve the scientific properties of the data alongside both visualisation considerations and emphasis on the ease of implementation. We describe the development of HiPS to manage a large number of diverse image surveys, as well as the extension of hierarchical image systems to cube and catalogue data. We demonstrate the interoperability of HiPS and Multi-Order Coverage (MOC) maps and highlight the HiPS mechanism to provide links to the original data. Results. Hierarchical progressive surveys have been generated by various data centres and groups for ~200 data collections including many wide area sky surveys, and archives of pointed observations. These can be accessed and visualised in Aladin, Aladin Lite, and other applications. HiPS provides a basis for further innovations in the use of hierarchical data structures to facilitate the description and statistical analysis of large astronomical data sets.Comment: 21 pages, 6 figures. Accepted for publication in Astronomy & Astrophysic

Integrated Object-Based Image Analysis for semi-automated geological lineament detection in Southwest England

This is the final version. Available on open access from Elsevier via the DOI in this record.Regional lineament detection for mapping of geological structure can provide crucial information for mineral exploration. Manual methods of lineament detection are time consuming, subjective and unreliable. The use of semi-automated methods reduces the subjectivity through applying a standardised method of searching. Object-Based Image Analysis (OBIA) has become a mainstream technique for classification of landcover, however, the use of OBIA methods for lineament detection is still relatively under-utilised. The Southwest England region is covered by high-resolution airborne geophysics and LiDAR data that provide an excellent opportunity to demonstrate the power of OBIA methods for lineament detection. Herein, two complementary but stand-alone OBIA methods for lineament detection are presented which both enable semi-automatic regional lineament mapping. Furthermore, these methods have been developed to integrate multiple datasets to create a composite lineament network. The top-down method uses threshold segmentation and sub-levels to create objects, whereas the bottom-up method segments the whole image before merging objects and refining these through a border assessment. Overall lineament lengths are longest when using the top-down method which also provides detailed metadata on the source dataset of the lineament. The bottom-up method is more objective and computationally efficient and only requires user knowledge to classify lineaments into major and minor groups. Both OBIA methods create a similar network of lineaments indicating that semi-automatic techniques are robust and consistent. The integration of multiple datasets from different types of spatial data to create a comprehensive, composite lineament network is an important development and demonstrates the suitability of OBIA methods for enhancing lineament detection.British Geological Survey (BGS)Natural Environment Research Council (NERC

A Systematic Literature Review on Image Information Needs and Behaviors

Purpose: With ready access to search engines and social media platforms, the way people find image information has evolved and diversified in the past two decades. The purpose of this paper is to provide an overview of the literature on image information needs and behaviors. Design/methodology/approach: Following an eight-step procedure for conducting systematic literature reviews, the paper presents an analysis of peer-reviewed work on image information needs and behaviors, with publications ranging from the years 1997 to 2019. Findings: Application of the inclusion criteria led to 69 peer-reviewed works. These works were synthesized according to the following categories: research methods, users targeted, image types, identified needs, search behaviors, and search obstacles. The reviewed studies show that people seek and use images for multiple reasons, including entertainment, illustration, aesthetic appreciation, knowledge construction, engagement, inspiration, and social interactions. The reviewed studies also report that common strategies for image searches include keyword searches with short queries, browsing, specialization, and reformulation. Observed trends suggest common deployment of query analysis, survey questionnaires, and undergraduate participant pools to research image information needs and behavior. Originality: At this point, after more than two decades of image information needs research, a holistic systematic review of the literature was long overdue. The way users find image information has evolved and diversified due to technological developments in image retrieval. By synthesizing this burgeoning field into specific foci, this systematic literature review provides a foundation for future empirical investigation. With this foundation set, the paper then pinpoints key research gaps to investigate, particularly the influence of user expertise, a need for more diverse population samples, a dearth of qualitative data, new search features, and information and visual literacies instruction

Packet analysis for network forensics: A comprehensive survey

Packet analysis is a primary traceback technique in network forensics, which, providing that the packet details captured are sufficiently detailed, can play back even the entire network traffic for a particular point in time. This can be used to find traces of nefarious online behavior, data breaches, unauthorized website access, malware infection, and intrusion attempts, and to reconstruct image files, documents, email attachments, etc. sent over the network. This paper is a comprehensive survey of the utilization of packet analysis, including deep packet inspection, in network forensics, and provides a review of AI-powered packet analysis methods with advanced network traffic classification and pattern identification capabilities. Considering that not all network information can be used in court, the types of digital evidence that might be admissible are detailed. The properties of both hardware appliances and packet analyzer software are reviewed from the perspective of their potential use in network forensics

Beyond Breast Cancer: An exploration of the experiences of middle-aged female breast cancer survivors in Australia.

Objective: Middle-aged women (40 – 65 years) who live with, through and beyond breast cancer (survivors), are a relatively under-researched population group, particularly within an Australian context. The unmet needs reported within this population include fatigue, psychological distress, body image concerns, early onset menopause, and a lack of information of these issues. The present study aims to explore how the experiences of breast cancer survivorship impact the lives of Australian middle-aged women (n = 644), and to inform future provision of care and support. Methods: This qualitative study used secondary survey data from the Australian Longitudinal Study of Women’s Health (ALSWH) middle-age cohort gathered between 1996 – 2013. Researchers conducted a thematic analysis using consensus coding on data collected from participants in this group who reported breast cancer (including metastasised) in any survey. Results: This cohort reported a unique experience of breast cancer survivorship due to their age. Analysis developed the following themes: the middle-aged context of breast cancer; care and support, body changes, overcoming fears and maintaining balance; and finding a ‘new normal’. Conclusions: Breast cancer survivorship is a subjective experience; for many it involves chronic limitations and challenges. Investigation and application of survivorship care plans in Australia would benefit from greater inclusion of multidisciplinary professionals. This will help satisfy heretofore unmet information needs and associated psychological distress of breast cancer survivors which go above their biomedical concerns. Further recommendations include development of online support groups providing access to rehabilitation professionals, especially for otherwise isolated rural women.Australian Longitudinal Study of Women's Healt