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GeoAI-Enhanced Techniques to Support...

Zhou, Xiran.

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GeoAI-Enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	GeoAI-Enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data./
Author:	Zhou, Xiran.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	157 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-11, Section: B.
Contained By:	Dissertations Abstracts International80-11B.
Subject:	Geographic information science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13862099
ISBN:	9781392139899

GeoAI-Enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data.
Zhou, Xiran.

GeoAI-Enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 157 p.

Source: Dissertations Abstracts International, Volume: 80-11, Section: B.

Thesis (Ph.D.)--Arizona State University, 2019.

This item must not be added to any third party search indexes.

Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. "Big but valueless" has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN's model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN's model that previously performed well on land-cover classification only.

ISBN: 9781392139899Subjects--Topical Terms:

3432445
Geographic information science.

GeoAI-Enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data.
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520 $a Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. "Big but valueless" has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN's model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN's model that previously performed well on land-cover classification only.
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