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Computer Vision for Environmental an...

Sheng, Hao.

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Computer Vision for Environmental and Social Sustainability Policy.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computer Vision for Environmental and Social Sustainability Policy./
Author:	Sheng, Hao.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	158 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
Subject:	Vegetation. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28827988
ISBN:	9798494462022

Computer Vision for Environmental and Social Sustainability Policy.
Sheng, Hao.

Computer Vision for Environmental and Social Sustainability Policy. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 158 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Computer vision (CV) has succeeded in many benchmark datasets but has yet to be widely applied to large-scale spatial and temporal correlated image datasets to address environmental or social issues. This thesis demonstrates the challenge, solutions, and policy implications of using computer vision for satellite, aerial, and street view images at scale with four empirical studies: 1) Mapping Vegetation and Classifying the driver of deforestation in Indonesia; 2) Detecting and characterizing high-emission oil and gas facilities in the U.S.; 3) Estimating the prevalence and placement of surveillance cameras in 16 major cities; 4) Mapping the Trend of Crosswalks Visibility Enhancements in the U.S. Transit-Oriented Development (TOD) areas. We conclude with a systematic review of the opportunities and challenges of using computer vision in facility urban and environmental policy research.

ISBN: 9798494462022Subjects--Topical Terms:

3560047
Vegetation.

Computer Vision for Environmental and Social Sustainability Policy.
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520 $a Computer vision (CV) has succeeded in many benchmark datasets but has yet to be widely applied to large-scale spatial and temporal correlated image datasets to address environmental or social issues. This thesis demonstrates the challenge, solutions, and policy implications of using computer vision for satellite, aerial, and street view images at scale with four empirical studies: 1) Mapping Vegetation and Classifying the driver of deforestation in Indonesia; 2) Detecting and characterizing high-emission oil and gas facilities in the U.S.; 3) Estimating the prevalence and placement of surveillance cameras in 16 major cities; 4) Mapping the Trend of Crosswalks Visibility Enhancements in the U.S. Transit-Oriented Development (TOD) areas. We conclude with a systematic review of the opportunities and challenges of using computer vision in facility urban and environmental policy research.
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