Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Xu, Zewei

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https://hdl.handle.net/2142/108467 Copy

Description

Title

Cybergis-enabled remote sensing data analytics for deep learning of landscape patterns and dynamics

Author(s)

Xu, Zewei

Issue Date

2020-07-15

Director of Research (if dissertation) or Advisor (if thesis)

Wang, Shaowen

Doctoral Committee Chair(s)

Wang, Shaowen

Committee Member(s)

• Guan, Kaiyu
• Jiang, Zhe
• Rhoads, Bruce
• Usery, E. Lynn

Department of Study

Geography & Geographic InfoSci

Discipline

Geography

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• CyberGIS
• Deep learning
• Landscape dynamics

Abstract

Mapping landscape patterns and dynamics is essential to various scientific domains and many practical applications. The availability of large-scale and high-resolution light detection and ranging (LiDAR) remote sensing data provides tremendous opportunities to unveil complex landscape patterns and better understand landscape dynamics from a 3D perspective. LiDAR data have been applied to diverse remote sensing applications where large-scale landscape mapping is among the most important topics. While researchers have used LiDAR for understanding landscape patterns and dynamics in many fields, to fully reap the benefits and potential of LiDAR is increasingly dependent on advanced cyberGIS and deep learning approaches. In this context, the central goal of this dissertation is to develop a suite of innovative cyberGIS-enabled deep-learning frameworks for combining LiDAR and optical remote sensing data to analyze landscape patterns and dynamics with four interrelated studies. The first study demonstrates a high-accuracy land-cover mapping method by integrating 3D information from LiDAR with multi-temporal remote sensing data using a 3D deep-learning model. The second study combines a point-based classification algorithm and an object-oriented change detection strategy for urban building change detection using deep learning. The third study develops a deep learning model for accurate hydrological streamline detection using LiDAR, which has paved a new way of harnessing LiDAR data to map landscape patterns and dynamics at unprecedented computational and spatiotemporal scales. The fourth study resolves computational challenges in handling remote sensing big data and deep learning of landscape feature extraction and classification through a cutting-edge cyberGIS approach.

Graduation Semester

2020-08

Type of Resource

Thesis

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http://hdl.handle.net/2142/108467

Copyright and License Information

Copyright 2020 Zewei Xu

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