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.

中文翻译：

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使用视频预测技术为城市发展建模：时间依赖的卷积编码器-解码器体系结构

本文提出了一种使用机器学习方法进行城市增长预测的新方法。该方法将市区的连续历史卫星图像视为可以预测未来帧的视频。它采用了与时间有关的卷积编码器-解码器体系结构。该方法的输入包括基准年和预测范围的卫星图像。它构建了一个图像，该图像可预测指定范围内任何给定目标年份的市区面积增长。执行灵敏度分析以确定参数的最佳组合，以实现最高的预测性能。作为案例研究，该方法用于预测德克萨斯州达拉斯-沃思堡地区的城市增长模式，重点关注过去十年中实现显着增长的两个县。结果表明，该方法所产生的结果与对该地区进行的其他增长预测研究相一致。