LIGHTWEIGHT CNN FOR CROSS-VIEW GEO-LOCALIZATION USING AERIAL IMAGE

In the field of remote sensing, there has been a significant amount of research focused on linking different domains such as multi-resolution, multi-spectral, and multi-sensor imagery. One task that involves such multimodal data is cross-view geo-localization, which aims to identify the location of ground query images by matching them with aerial images in a database that is tagged with GPS information. Our study presents a novel lightweight convolutional neural network architecture that achieves comparable performance to a transformer-based model on a city-scale dataset while reducing the number of parameters without using any data augmentation and transformation. Furthermore, our experimental findings indicate that the presence or absence of the fully-connected layer, which is used for generating attention maps, has minimal influence on the model's accuracy. The code is available from https://github.com/ryotayagiABC/Light_CVGL.