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Searching for relevant knowledge across heterogeneous geospatial databases requires an extensive knowledge of the semantic meaning of images, a keen eye for visual patterns, and efficient strategies for collecting and analyzing data with minimal human intervention. In this paper, we present our recently developed content-based multimodal Geospatial(More)
Information technology offers great opportunities for supporting radiologists' expertise in decision support and training. However, this task is challenging due to difficulties in articulating and modeling visual patterns of abnormalities in a computational way. To address these issues, well established approaches to content management and image retrieval(More)
—Automatic learning of geospatial intelligence is challenging due to the complexity of articulating knowledge from visual patterns and to the ever-increasing quantities of image data generated on a daily basis. In this setting, human inspection and annotation is subjective and, more importantly, impractical. In this letter, we propose a knowledge-discovery(More)
The ability to search for and precisely compare similar phenotypic appearances within and across species has vast potential in plant science and genetic research. The difficulty in doing so lies in the fact that many visual phenotypic data, especially visually observed phenotypes that often times cannot be directly measured quantitatively, are in the form(More)
The large quantity of imagery generated by the geospatial domain constitutes a challenge for any system that manages, ranks, or classifies satellite images. Image analysts are able to evaluate only a fraction of this information and this trend is likely to increase in the future with the addition of new and higher resolution satellites and image modalities.(More)
UNLABELLED PREMISE OF THE STUDY Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge(More)
There are thousands of maize mutants, which are invaluable resources for plant research. Geneticists use them to study underlying mechanisms of biochemistry, cell biology, cell development, and cell physiology. To streamline the understanding of such complex processes, researchers need the most current versions of genetic and physical maps, tools with the(More)