Abstract Landsat imagery is an unparalleled freely available data source that allows reconstructing land-cover and land-use change, including urban form. This paper addresses the challenge of using Landsat data, particularly its 30 m spatial resolution, for monitoring three-dimensional urban densification. Unlike conventional convolutional neural networks (CNNs) for scene recognition resulting in resolution loss, the proposed semantic segmentation framework provides a pixel-wise classification and improves the accuracy of urban form mapping. We compare temporal and spatial transferability of an adapted DeepLab model with a simple fully convolutional network (FCN) and a texture-based random forest (RF) model to map urban density in the two morphological dimensions: horizontal (compact, open, sparse) and vertical (high rise, low rise). We test whether a model trained on the 2014 data can be applied to 2006 and 1995 for Denmark, and examine whether we could use the model trained on the Danish data to accurately map ten other European cities. Our results show that an implementation of deep networks and the inclusion of multi-scale contextual information greatly improve the classification and the model's ability to generalize across space and time. Between the two semantic segmentation models, DeepLab provides more accurate horizontal and vertical classifications than FCN when sufficient training data is available. By using DeepLab, the F1 score can be increased by 4 and 10 percentage points for detecting vertical urban growth compared to FCN and RF for Denmark. For mapping the ten other European cities with training data from Denmark, DeepLab also shows an advantage of 6 percentage points over RF for both horizontal and vertical dimensions. The resulting maps across the years 1985 to 2018 reveal different patterns of urban growth between Copenhagen and Aarhus, the two largest cities in Denmark, illustrating that those cities have used various planning policies in addressing population growth and housing supply challenges. In summary, we propose a transferable deep learning approach for automated, long-term mapping of urban form from Landsat images that is effective in areas experiencing a slow pace of urban growth or with small-scale changes.

中文翻译：

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使用 1985 年至 2018 年的 Landsat 时间序列绘制丹麦的水平和垂直城市密集化：语义分割解决方案

摘要 Landsat 影像是一种无与伦比的免费数据源，可以重建土地覆盖和土地利用变化，包括城市形态。本文解决了使用 Landsat 数据（尤其是其 30 m 空间分辨率）监测三维城市密集化的挑战。与传统的用于场景识别的卷积神经网络 (CNN) 导致分辨率损失不同，所提出的语义分割框架提供了像素级分类并提高了城市形态映射的准确性。我们将适应的 DeepLab 模型与简单的全卷积网络 (FCN) 和基于纹理的随机森林 (RF) 模型的时间和空间可转移性进行比较，以在两个形态维度上映射城市密度：水平（紧凑、开放、稀疏）和垂直（高层、低层）。我们测试了在 2014 年数据上训练的模型是否可以应用于丹麦的 2006 年和 1995 年，并检查我们是否可以使用在丹麦数据上训练的模型来准确绘制其他十个欧洲城市。我们的结果表明，深度网络的实现和多尺度上下文信息的包含极大地提高了分类和模型跨空间和时间泛化的能力。在两种语义分割模型之间，当有足够的训练数据可用时，DeepLab 提供比 FCN 更准确的水平和垂直分类。通过使用 DeepLab，与丹麦的 FCN 和 RF 相比，F1 分数可以提高 4 和 10 个百分点，用于检测垂直城市增长。使用丹麦的训练数据绘制其他十个欧洲城市的地图，DeepLab 还显示出在水平和垂直维度上比 RF 高 6 个百分点的优势。由此产生的 1985 年至 2018 年的地图揭示了丹麦最大的两个城市哥本哈根和奥胡斯之间不同的城市增长模式，说明这些城市使用了各种规划政策来应对人口增长和住房供应挑战。总之，我们提出了一种可迁移的深度学习方法，用于从 Landsat 图像自动、长期地绘制城市形态，该方法在城市发展缓慢或变化较小的地区有效。说明这些城市已使用各种规划政策来应对人口增长和住房供应挑战。总之，我们提出了一种可迁移的深度学习方法，用于从 Landsat 图像自动、长期地绘制城市形态，该方法在城市发展缓慢或变化较小的地区有效。说明这些城市已使用各种规划政策来应对人口增长和住房供应挑战。总之，我们提出了一种可迁移的深度学习方法，用于从 Landsat 图像自动、长期地绘制城市形态，该方法在城市发展缓慢或变化较小的地区有效。