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.
View on arXiv@article{pliego2025_2503.15653,
title={ Transport-Related Surface Detection with Machine Learning: Analyzing Temporal Trends in Madrid and Vienna },
author={ Miguel Ureña Pliego and Rubén Martínez Marín and Nianfang Shi and Takeru Shibayama and Ulrich Leth and Miguel Marchamalo Sacristán },
journal={arXiv preprint arXiv:2503.15653},
year={ 2025 }
}