Outdoor geolocation generally relies on the smartphone’s GPS (Global Positioning System) for positioning. However, GPS encounters difficulties when the sky is cloudy or in dense urban areas between tall buildings. This article proposes an alternative method of geolocation in the event of GPS unavailability or failure, using vision to locate oneself. The method is based on a neural network model based on MobileNet, followed by regression and logo detection using YOLO to help the user locate. We will compare our method with that used by Nilwong et al., AlexNet and ResNet50. The datasets used are images captured on a campus with a smartphone and used to train the model. The results are obtained using an Android application, which compares the predicted position from the input images with the actual position measured by GPS in good conditions (clear sky). The results show that our model can replace GPS for locating a pedestrian in an urban environment.