CROCO: CROSS-MODAL CONTRASTIVE LEARNING FOR LOCALIZATION OF EARTH OBSERVATION DATA - Archive ouverte HAL Access content directly
Journal Articles ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Year : 2022

CROCO: CROSS-MODAL CONTRASTIVE LEARNING FOR LOCALIZATION OF EARTH OBSERVATION DATA

Abstract

Abstract. It is of interest to localize a ground-based LiDAR point cloud on remote sensing imagery. In this work, we tackle a subtask of this problem, i.e. to map a digital elevation model (DEM) rasterized from aerial LiDAR point cloud on the aerial imagery. We proposed a contrastive learning-based method that trains on DEM and high-resolution optical imagery and experiment the framework on different data sampling strategies and hyperparameters. In the best scenario, the Top-1 score of 0.71 and Top-5 score of 0.81 are obtained. The proposed method is promising for feature learning from RGB and DEM for localization and is potentially applicable to other data sources too. Source code is released at https://github.com/wtseng530/AVLocalization.

Dates and versions

hal-03991207 , version 1 (15-02-2023)

Identifiers

Cite

W.-H. Tseng, H.-A. Lê, A. Boulch, Sébastien Lefèvre, D. Tiede. CROCO: CROSS-MODAL CONTRASTIVE LEARNING FOR LOCALIZATION OF EARTH OBSERVATION DATA. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022, V-2-2022, pp.415-421. ⟨10.5194/isprs-annals-V-2-2022-415-2022⟩. ⟨hal-03991207⟩
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