Street View Images (SVIs) can be utilized in urban analysis to assess the exterior cladding materials of buildings. However, this process is labor-intensive and time-consuming, particularly when applied to extensive geographic areas. Currently, there is no automated study to streamline this manual process. This research introduces a deep learning approach suitable for image classification and demonstrates its application in two case study areas: London and Scotland. Six material types were identified: ‘Brick’, ‘Concrete’, ‘Glass’, ‘Stone’, ‘Mixed’, and ‘Others’. To ensure reliable accuracy, several advanced architectures were employed: InceptionV3, EfficientNetV2, ResNet-101, ResNet-152, and MobileNetV3. Transfer learning was applied to each architecture, and six distinct image augmentation techniques were utilized to artificially expand the training dataset. Consequently, ten models were developed for each area by combining five different architectures with two datasets: the original and the augmented. The best-performing model was selected for evaluation on an unseen dataset. For London and Scotland, the model using MobileNetV3 with augmented data emerged as the most effective, achieving average accuracy rates of 75.65 % and 73.45 %, respectively. Based on these findings, the paper explores the potential applications of the proposed approach.

中文翻译：

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使用深度学习从街景图像中自动检测城市地区的外部覆层材料


街景图像 (SVI) 可用于城市分析，以评估建筑物的外部覆层材料。然而，这个过程是劳动密集型且耗时的，特别是当应用于广泛的地理区域时。目前，还没有自动化研究来简化这个手动过程。这项研究介绍了一种适合图像分类的深度学习方法，并展示了其在伦敦和苏格兰两个案例研究领域的应用。确定了六种材料类型：“砖”、“混凝土”、“玻璃”、“石头”、“混合”和“其他”。为了确保可靠的准确性，采用了几种先进的架构：InceptionV3、EfficientNetV2、ResNet-101、ResNet-152 和 MobileNetV3。迁移学习应用于每个架构，并利用六种不同的图像增强技术来人为地扩展训练数据集。因此，通过将五种不同的架构与两个数据集（原始数据集和增强数据集）相结合，为每个领域开发了十个模型。选择性能最佳的模型来对未见过的数据集进行评估。对于伦敦和苏格兰，使用 MobileNetV3 和增强数据的模型最为有效，平均准确率分别达到 75.65% 和 73.45%。基于这些发现，本文探讨了所提出方法的潜在应用。