Ilin, R., Watson, T. and Kozma, R. (2017) Abstraction Hierarchy in Deep Learning Neural Networks. 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, 14-19 May 2017, 768-774.
https://doi.org/10.1109/IJCNN.2017.7965929

has been cited by the following article:

• TITLE: YOLOv2 Deep Learning Model and GIS Based Algorithms for Vehicle Tracking

  AUTHORS: Mohamed El Imame Malaainine, Hatim Lechgar, Hassane Rhinane

  KEYWORDS: Deep Learning, YOLOv2, Vehicle Tracking, GIS

  JOURNAL NAME: Journal of Geographic Information System, Vol.13 No.4, July 16, 2021

  ABSTRACT: The latest advances in Deep Learning based methods and computational capabilities provide new opportunities for vehicle tracking. In this study, YOLOv2 (You Only Look Once—version 2) is used as an open source Convolutional Neural Network (CNN), to process high-resolution satellite images, in order to generate the spatio-temporal GIS (Geographic Information System) tracks of moving vehicles. At first step, YOLOv2 is trained with a set of images of 1024 × 1024 resolution from the VEDAI database. The model showed satisfactory results, with an accuracy of 91%, and then at second step, is used to process aerial images extracted from aerial video. The output vehicle bounding boxes have been processed and fed into the GIS based LinkTheDots algorithm, allowing vehicles identification and spatio-temporal tracks generation in GIS format.