Modern knowledge discovery systems, empowered by visual data exploration techniques, enable the user to discover and understand the data content. Considering patch-level processing, the visual exploration of Earth Observation archives aims to identify groups of items sharing similar semantic content. Each patch is further represented by certain descriptors, i.e., spectral signatures or Weber local descriptors, to capture structural signature. Later on, the content of the archive is illustrated by a 3-D projection of the high-dimensional space of the descriptors. Aspiring to prove the visual data mining potential, this letter intends to determine the capability of dimensionality reduction techniques to achieve a meaningful 3-D projection of the high-dimensional space. Several real-world data sets were used, i.e., University of California, Merced Land Use data set and a Landsat 7 Enhanced Thematic Mapper Plus image tiled into patches.

Sentinel-2 satellites provide systematic global coverage of land surfaces, measuring physical properties within 13 spectral intervals at a temporal resolution of 5 days. Computer-based data analysis is highly required to extract similarity by processing and to assist human understanding and semantic annotation in support of mapping Earth's surface. This paper proposes a data mining concept that uses advanced data visualization and explainable features to enhance relevant aspects in the Sentinel-2 data and enable semantic analysis. There is a two-stage process. At first, spectral, texture, and physical parameters related features are extracted from the data and included in a learning process that models the data content according to statistical similarities. In parallel, the second processing stage maximizes the data impact on the human visual system to help image understanding and interpretation. Target classes are subject to exploratory visual analysis, such that both visual and latent characteristics are revealed to the user. The concept is further implemented as Sentinel-2 dedicated data analysis (DAS-Tool) plugin for the Sentinel Application Platform (SNAP) and deployed as an open-source tool empowering the Earth Observation (EO) community with fast and reliable results. Accommodating multiple solutions for each processing phase, the plugin enables flexibility in information extraction and knowledge discovery that will bring the best accuracy in mapping applications. For demonstration purposes, the authors focus on a detailed benchmark against reference data (ground truth) for the Southern region of Romania, then use the selected algorithms in a forest fires scenario analysis for the Sydney region in Australia.

This paper introduces a tool designed to provide an innovative and insightful way of exploring Earth observation data content beyond visualization, by addressing a visual analytics process. The considered framework combines machine learning and visualization techniques, empowered through human interaction, to gain knowledge from the data. The proposed tool- eVADE leverages the methodologies developed in the fields of information retrieval, data mining and knowledge representation by the means of a visual analytics component. eVADE increases users capability to understand and extract meaningful semantic clusters together with quantitative measurements, presented in a suggestive visual way.

Doctors spend about half of their time doing the paperwork and writing medical reports. The use of speech recognition in the creation of medical transcription applications aims to ease the work of medical professionals, freeing them from writing reports. Moreover, these reports contain a larger volume of information, with more details included, the information being added while the doctor examines the patient in the case of a medical consultation, respectively while analyzing the images obtained in the case of a medical investigation. Medical transcription solutions reduce the time needed to complete the reports related to a medical examination or investigation, giving the medical staff more time to focus on the patient.