Vehicle detection and counting from VHR satellite images: efforts and open issues

TL;DR

This paper explores deep learning models for vehicle detection and counting in high-resolution satellite images, demonstrating high accuracy and discussing open challenges in the field.

Contribution

It introduces and compares segmentation and detection deep learning models tailored for vehicle counting in satellite imagery, with a new annotated dataset.

Findings

Both models achieved over 85% precision.

Segmentation model (Tiramisu) had 76.4% recall.

Detection model (YOLO) had 71.9% recall.

Abstract

Detection of new infrastructures (commercial, logistics, industrial or residential) from satellite images constitutes a proven method to investigate and follow economic and urban growth. The level of activities or exploitation of these sites may be hardly determined by building inspection, but could be inferred from vehicle presence from nearby streets and parking lots. We present in this paper two deep learning-based models for vehicle counting from optical satellite images coming from the Pleiades sensor at 50-cm spatial resolution. Both segmentation (Tiramisu) and detection (YOLO) architectures were investigated. These networks were adapted, trained and validated on a data set including 87k vehicles, annotated using an interactive semi-automatic tool developed by the authors. Experimental results show that both segmentation and detection models could achieve a precision rate higher than 85% with a recall rate also high (76.4% and 71.9% for Tiramisu and YOLO respectively).