Modeling urban growth using video prediction technology: A time‐dependent convolutional encoder–decoder architecture

This paper presents a novel methodology for urban growth prediction using a machine learning approach. The methodology treats successive historical satellite images of an urban area as a video for which future frames are predicted. It adopts a time‐dependent convolutional encoder–decoder architectur...

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Bibliographic Details
Published in:Computer-aided civil and infrastructure engineering Vol. 35; no. 5; pp. 430 - 447
Main Authors: Jaad, Ahmed, Abdelghany, Khaled
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc 01.05.2020
Subjects:
Architecture
Coders
Horizon
Machine learning
Methodology
Parameter sensitivity
Satellite imagery
Sensitivity analysis
Time dependence
Urban areas
Urban development
ISSN:1093-9687, 1467-8667
Online Access:Get full text
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Summary:This paper presents a novel methodology for urban growth prediction using a machine learning approach. The methodology treats successive historical satellite images of an urban area as a video for which future frames are predicted. It adopts a time‐dependent convolutional encoder–decoder architecture. The methodology's input includes a satellite image for the base year and the prediction horizon. It constructs an image that predicts the growth of the urban area for any given target year within the specified horizon. A sensitivity analysis is performed to determine the best combination of parameters to achieve the highest prediction performance. As a case study, the methodology is applied to predict the urban growth pattern for the Dallas–Fort Worth area in Texas, with focus on two of its counties that observed significant growth over the past decade. The methodology is shown to produce results that are consistent with other growth prediction studies conducted for the areas.
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ISSN:1093-9687
1467-8667
DOI:10.1111/mice.12503