Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining

The 1935 Hsinchu-Taichung Earthquake

The history of natural disasters in Taiwan has frequently been linked to the practice of historical preservation, archival science, oral history, and museum curatorship. All are collectively hallmarks of a broad range of activities that fall under the umbrella of public history. The problem for Taiwan, however, concerns the legitimacy. Taiwan does not have a single national narrative. It has been subjected to waves of colonialism since the seventeenth century and does not presently have a fully post-colonial narrative. The earthquakes discussed in this paper occurred in two different periods of colonisation.  In order to situate the history of earthquakes into a public history discourse, the field of earthquake-based research in Taiwan has to incorporate different audiences and integrate into a much broader understanding. By this, I mean that the present regimental academic disciplines in Taiwan need to be cross disciplinary, especially since public history is by its very nature collaborative. It illuminates a shared authority over a much wider area. It needs to. It is my argument that it is in digital humanities that Taiwanese academics work best in collaboration. Efforts have been made to digitise the personal experiences of those involved in typhoon reconstruction efforts. A natural synergy, therefore, for the understanding of earthquakes, as public history, is to emphasise access and broad participation in the creation of knowledge. Digital humanities enables this. Attention to this is particularly important in historical preservation of particular sites on an island that frequently develops and re-develops brownfield sites

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

Use of a Small Unmanned Aerial System for the SR-530 Mudslide Incident near Oso, Washington

The Center for Robot-Assisted Search and Rescue deployed three commercially available small unmanned aerial systems (SUASs)—an AirRobot AR100B quadrotor, an Insitu Scan Eagle, and a PrecisionHawk Lancaster—to the 2014 SR-530 Washington State mudslides. The purpose of the flights was to allow geologists and hydrologists to assess the eminent risk of loss of life to responders from further slides and flooding, as well as to gain a more comprehensive understanding of the event. The AirRobot AR100B in conjunction with PrecisionHawk postprocessing software created two-dimensional (2D) and 3D reconstructions of the inaccessible “moonscape” region of the slide and provided engineers with a real-time remote presence assessment of river mitigation activities. The AirRobot was able to cover 30–40 acres from an altitude of 42 m (140 ft) in 48 min of flight time and generate interactive 3D reconstructions in 3 h on a laptop in the field. The deployment is the 17th known use of SUAS for disasters, and it illustrates the evolution of SUASs from tactical data collection platforms to strategic data-to-decision systems. It was the first known instance in the United States in which an airspace deconfliction plan allowed a UAS to operate with manned vehicles in the same airspace during a disaster. It also describes how public concerns over SUAS safety and privacy led to the cancellation of initial flights. The deployment provides lessons on operational considerations imposed by the terrain, trees, power lines, and accessibility, and a safe human:robot ratio. The article identifies open research questions in computer vision, mission planning, and data archiving, curation, and mining

Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics