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• W3018950562 abstract "Big data that contain geo-referenced attributes havesignificantly reformed the way that I process and analyzegeospatial data. Compared with the expected benefits received inthe data-rich environment, more data have not always contributed tomore accurate analysis. “Big but valueless” has becoming a criticalconcern to the community of GIScience and data-driven geography. Asa highly-utilized function of GeoAI technique, deep learning modelsdesigned for processing geospatial data integrate powerfulcomputing hardware and deep neural networks into various dimensionsof geography to effectively discover the representation of data.However, limitations of these deep learning models have also beenreported when People may have to spend much time on preparingtraining data for implementing a deep learning model. The objectiveof this dissertation research is to promote state-of-the-art deeplearning models in discovering the representation, value and hiddenknowledge of GIS and remote sensing data, through three researchapproaches. The first methodological framework aims to unify variedshadow into limited number of patterns, with the convolutionalneural network (CNNs)-powered shape classification, multifariousshadow shapes with a limited number of representative shadowpatterns for efficient shadow-based building height estimation. Thesecond research focus integrates semantic analysis into a frameworkof various state-of-the-art CNNs to support human-levelunderstanding of map content. The final research approach of thisdissertation focuses on normalizing geospatial domain knowledge topromote the transferability of a CNN’s model to land-use/land-coverclassification. This research reports a method designed to discoverdetailed land-use/land-cover types that might be challenging for astate-of-the-art CNN’s model that previously performed well onland-cover classification only." @default.
• W3018950562 created "2020-05-01" @default.
• W3018950562 creator A5032393086 @default.
• W3018950562 date "2019-01-01" @default.
• W3018950562 modified "2023-09-27" @default.
• W3018950562 title "GeoAI-Enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data" @default.
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