Detection of Parking Cars in Stereo Satellite Images

Abstract

<jats:p>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.</jats:p>

Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

“…Using the nearest neighbor difference method, CC5, CC4, and CC3 were up-sampled twice (2× up), and elementwise addition ( In the field of computer vision, Faster R-CNN is a classic object detection model based on deep learning. The model has high recognition accuracy and efficiency when applied to large target areas and has been widely used for object detection from remote sensing images [10,12,23]. In this study, we introduced an improved Faster R-CNN model to make full use of the multispectral band information of GF-1 images and improve the precision of tailings pond detection, based on the research results in [23].…”

“…As such, the precision of automatic building detection from high-resolution UAV remote sensing imagery is improved. Zambanini et al [12] have also used Faster R-CNN, together with images from the WorldView-3 high spatial resolution Earth imaging satellite to automatically detect parked vehicles in an urban area. Wang et al [13] used transfer learning to improve the Mask R-CNN model [14], constructed an open-pit mine detection model using high-resolution remote sensing data, and achieved automatic identification and dynamic monitoring of open-pit mines.…”

An Improved Traffic Congestion Monitoring System Based on Federated Learning

“…The realization method of this research is to realize the target detection on the remote sensing data. In the study of Zambanini S. et al [4], a parking space detection method based on high-resolution stereo satellite images was proposed. This method can infer the movement of cars through time intervals.…”

“…The remote sensing data set used in this research has the characteristics of wide coverage and easy access, and is based on the application of deep learning described in research [6,7,[9][10][11] in remote sensing, combined with an existing research [4,5,8] target detection algorithm. The availability of remote sensing images has related improvements to the above-mentioned traffic monitoring methods.…”

Grid Screener: A Tool for Automated High-Throughput Screening on Biochemical and Biological Analysis Platforms

“…Thereby, grid structures of different spots are a common setup within high-throughput screening such as microtiter wellplates [11], Micropillar Microwell Array Chips (MIM-ICs) [12], or Droplet Microarrays (DMAs) [13], [14]. Besides, grid structures can occur in other research areas such as the analysis of parking lots using satellite images [15] or transmission electron microscopy [16].…”