Vehicle detection and counting from VHR satellite images: efforts and open issues
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).
Code (0)
등록된 구현이 없습니다.
Tasks
Segmentationvehicle detectionSimilar Papers 제목 키워드 기반
Vehicle Perception from Satellite
Satellites are capable of capturing high-resolution videos. It makes vehicle perception from satellite become possible. Compared to street surveillance, drive recorder or other equipments, satellite videos provide a much…
Density Estimationobject-detectionObject DetectionDeep Vehicle Detection in Satellite Video
This work presents a deep learning approach for vehicle detection in satellite video. Vehicle detection is perhaps impossible in single EO satellite images due to the tininess of vehicles (4-10 pixel) and their similarit…
vehicle detectionVME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond
Detecting vehicles in satellite images is crucial for traffic management, urban planning, and disaster response. However, current models struggle with real-world diversity, particularly across different regions. This cha…
Disaster ResponseDiversityObject DetectionObject Detection In Aerial Images+2On Learning Vehicle Detection in Satellite Video
Vehicle detection in aerial and satellite images is still challenging due to their tiny appearance in pixels compared to the overall size of remote sensing imagery. Classical methods of object detection very often fail i…
Inductive BiasObjectobject-detectionObject Detection+2Combining YOLO and Visual Rhythm for Vehicle Counting
Video-based vehicle detection and counting play a critical role in managing transport infrastructure. Traditional image-based counting methods usually involve two main steps: initial detection and subsequent tracking, wh…
Rhythmvehicle detection