A guided approach for cross-view geolocalization estimation with land cover semantic segmentation

Abstract

Geolocalization is a crucial process that leverages environmental information and contextual data to accurately identify a position. In particular, cross-view geolocalization utilizes images from various perspectives, such as satellite and ground-level images, which are relevant for applications like robotics navigation and autonomous navigation. In this research, we propose a methodology that integrates cross-view geolocalization estimation with a land cover semantic segmentation map. Our solution demonstrates comparable performance to state-of-the-art methods, exhibiting enhanced stability and consistency regardless of the street view location or the dataset used. Additionally, our method generates a focused discrete probability distribution that acts as a heatmap. This heatmap effectively filters out incorrect and unlikely regions, enhancing the reliability of our estimations. Code is available at https://github.com/nathanxavier/CVSegGuide.

Keywords

Cross-view geolocalization / Semantic segmentation / Satellite and ground image fusion / Simultaneous localization and mapping (SLAM)

CRediT authorship contribution statement

Nathan A.Z. Xavier: Writing - original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Conceptualization. Elcio H. Shiguemori: Writing - review & editing, Supervision. Marcos R.O.A. Maximo: Writing - review & editing, Supervision. Mubarak Shah: Writing - review & editing, Supervision, Resources, Project administration.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) (88887.929508/2023-00 and 88887.937224/2024-00).

Marcos Maximo is partially funded by the National Research Council of Brazil (CNPq) (307525/2022-8).

This research was developed within the IDeepS which is supported by the Laboratório Nacional de Computação Científica (LNCC/MCTI, Brazil) via resources of the SDumont supercomputer (http://sdumont.lncc.br).

Appendix. Supplementary material

This appendix contains supplemental material that provides additional details on the neural network architecture described in Section 4.

Tables A.1 and A.2 present the architecture for the segmentation and heatmap prediction blocks, respectively, within the FeatUp backbone model. Similarly, Tables A.3 and A.4 provide the corresponding architectural details for the MST model.

Each of these tables includes information on the layer type, input and output shapes, and the number of learnable parameters.

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