Abstract
This study explores the integration of machine learning into urban aerial image analysis, with a focus on identifying infrastructure surfaces for cars and pedestrians and analyzing historical trends. It emphasizes the transition from convolutional architectures to transformer-based pre-trained models, underscoring their potential in global geospatial analysis. A workflow is presented for automatically generating geospatial datasets, enabling the creation of semantic segmentation datasets from various sources, including WMS/WMTS links, vectorial cartography, and OpenStreetMap (OSM) overpass-turbo requests. The developed code allows a fast dataset generation process for training machine learning models using openly available data without manual labelling. Using aerial imagery and vectorial data from the respective geographical offices of Madrid and Vienna, two datasets were generated for car and pedestrian surface detection. A transformer-based model was trained and evaluated for each city, demonstrating good accuracy values. The historical trend analysis involved applying the trained model to earlier images predating the availability of vectorial data 10 to 20 years, successfully identifying temporal trends in infrastructure for pedestrians and cars across different city areas. This technique is applicable for municipal governments to gather valuable data at a minimal cost.
Abstract (translated)
这项研究探讨了将机器学习技术融入城市航拍图像分析的过程，重点在于识别汽车和行人的基础设施表面，并对历史趋势进行分析。该研究强调从卷积架构向基于变压器的预训练模型过渡，突出了这些模型在全球地理空间分析中的潜力。文中提出了一种工作流程，用于自动生成地理空间数据集，可以从各种来源创建语义分割数据集，包括WMS/WMTS链接、矢量地图和OpenStreetMap (OSM) overpass-turbo请求。开发的代码允许利用公开可用的数据快速生成训练机器学习模型所需的大量数据，而无需手动标记。 使用马德里和维也纳地理办公室提供的航拍图像和矢量数据，分别针对汽车和行人路面检测生成了两个数据集。为每个城市训练并评估了一个基于变压器的模型，展示了良好的准确性值。历史趋势分析涉及将经过训练的模型应用于早于10到20年前没有矢量数据可用的早期图片上，成功识别出不同城市区域中人行道和汽车基础设施随时间的变化趋势。 这项技术对于市镇政府来说非常有用，可以在低成本下收集有价值的数据。
URL
https://arxiv.org/abs/2503.15653
