TY - JFULL AU - Fatma El-zahraa El-taher and Ayman Taha and Jane Courtney and Susan Mckeever PY - 2022/11/ TI - Using Satellite Images Datasets for Road Intersection Detection in Route Planning T2 - International Journal of Computer and Systems Engineering SP - 410 EP - 418 VL - 16 SN - 1307-6892 UR - https://publications.waset.org/pdf/10012708 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 190, 2022 N2 - Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions is critical to decisions such as crossing roads or selecting safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition  problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset are examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of detection of intersections in satellite images is evaluated. ER -