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  • Observation of Dynamic Climate Variation.:

    Climate is defined as long-term averages and variations in weather measured over a period of several decades. The Earth’s climate system includes the land surface, atmosphere, oceans, and ice. Many aspects of the global climate are changing rapidly, and the primary drivers of that change are human in origin. Evidence for changes in the climate system abounds, from the top of the atmosphere to the depths of the oceans

  • Remote-Sensing based Moving Object Detection.:

    Moving objects constitute a significant part of all technical objects for which the method of controlling their movement significantly affects both operating costs and the accuracy and safety of transport tasks. This applies to land, sea and air objects in terms of manned and unmanned facilities. Remote sensing devices such as radar, lidar and other highly specialized measurement solutions are used in the detection and control of moving objects. When planning and implementing motion control of objects, there are many possible acceptable solutions from which the best or optimal solution should be selected

  • Scene Classification in Remote Sensed Image.:

    Remote sensing image scene classification, which aims at labeling remote sensing images with a set of semantic categories based on their contents, has broad applications in a range of fields. Propelled by the powerful feature learning capabilities of deep neural networks, remote sensing image scene classification driven by deep learning has drawn remarkable attention and achieved significant breakthroughs.

  • Prediction and Evaluation of Natural Disasters.:

    Since 2000, there have been a number of spaceborne satellites and sensors that have changed the way we assess and predict natural disasters. These sensors are able to quantify geophyscial phenomena associated with the movements of the earth surface (earthquakes, mass movements), water (floods, tsunamis, storms), and fire (wildfires).

  • Accurate Indication of Ground Object Mobility.:

    The use of location-based data in clinical settings is often limited to real-time monitoring. In this study, we aim to develop a proximity-based localization system and show how its longitudinal deployment can provide operational insights related to staff and patients' mobility and room occupancy in clinical settings.