In this paper, we present a Remote Sens. approach to localize parking cars in a city in order to enable the development of parking space availability models. We propose to use high-resolution stereo satellite images for this problem, as they provide enough details to make individual cars recognizable and the time interval between the stereo shots allows to reason about the moving or static condition of a car. Consequently, we describe a complete processing pipeline where raw satellite images are georeferenced, ortho-rectified, equipped with a digital surface model and an inclusion layer generated from Open Street Model vector data, and finally analyzed for parking cars by means of an adapted Faster R-CNN oriented bounding box detector. As a test site for the proposed approach, a new publicly available dataset of the city of Barcelona labeled with parking cars is presented. On this dataset, a Faster R-CNN model directly trained on the two ortho-rectified stereo images achieves an average precision of 0.65 for parking car detection. Finally, an extensive empirical and analytical evaluation shows the validity of our idea, as parking space occupancy can be broadly derived in fully visible areas, whereas moving cars are efficiently ruled out. Our evaluation also includes an in-depth analysis of the stereo occlusion problem in view of our application scenario as well as the suitability of using a reconstructed Digital Surface Model (DSM) as additional data modality for car detection. While an additional adoption of the DSM in our pipeline does not provide a beneficial cue for the detection task, the stereo images provide essentially two views of the dynamic scene at different timestamps. Therefore, for future studies, we recommend a satellite image acquisition geometry with smaller incidence angles, to decrease occlusions by buildings and thus improve the results with respect to completeness.

Historical aerial imagery archives contain valuable geospatial data for studying urban development, environmental changes, and historical events. However, the volume of data and inconsistencies in metadata and georeferencing complicate content classification. This paper explores the application of vision-language models, such as RemoteCLIP, GRAFT, GPT-4o mini, GeoCHAT, and RS-LLaVA, to automate classification and search within historical aerial imagery archives. We introduce a novel classification dataset with multiple annotated classes, including bridges, buildings, train tracks, bomb craters, and roads. Our exploratory analysis of model performance on this dataset provides initial insights into their capabilities and limitations in the historical imagery context. Automating image tagging with basic descriptors lays the groundwork for more sophisticated searches, enhancing access to cultural heritage. Future work should focus on optimizing feature map storage and scalability.