2026
Liu, Qingyang; Shatwell, David G.; Kulkarni, Parth Parag; Shah, Mubarak
Pixel-wise Geo-registration of Drone Images Conference
European Conference on Computer Vision (ECCV), Springer, 2026.
Abstract | Tags: ECCV, Geo-Localization, Remote Sensing | Links:
@conference{liu2026skyregb,
title = {Pixel-wise Geo-registration of Drone Images},
author = {Qingyang Liu and David G. Shatwell and Parth Parag Kulkarni and Mubarak Shah},
url = {https://qingyangliu.github.io/qingyang.github.io/static/Georegistration.pdf
https://ai.ucf.edu/wp-content/uploads/2026/09/Pixel-Wise-Geo-Registration-JPG.png
https://youtu.be/jgVuME3oFJ4?si=R-AkoBd3rlG_791Z},
year = {2026},
date = {2026-09-07},
urldate = {2026-09-07},
booktitle = {European Conference on Computer Vision (ECCV)},
publisher = {Springer},
abstract = {Cross-view geo-registration is the task of aligning a query image to a geodetically accurate reference (e.g., satellite image), so that each query pixel maps to a real-world GPS coordinate. Most existing work addresses the related problem of cross-view geo-localization, where the goal is typically to estimate the camera center using retrieval, classification, matching, or regression. Because these approaches do not provide dense supervision, they are poorly suited for learning and evaluating pixel-wise alignment. We introduce SkyReg, a geometry-aware geo-registration model that estimates the transformation between the query and reference images by explicitly modeling the 3D scene geometry. Applying this transformation to warp the query into the reference frame yields pixel-wise geo-localization, without relying on 2D point matches, and remains robust to occlusions and large viewpoint changes. To enable training and standardized evaluation, we release (i) SkyReg-Train, a large-scale dataset of drone–satellite images annotated with per-pixel GPS coordinates, depth maps, and camera parameters derived from LiDAR and structure-from-motion, and (ii) SkyReg-Bench, a held-out benchmark of unseen Urban and Suburban scenes with the same dense annotations. SkyReg achieves state-of-the-art performance against strong retrieval and homography baselines, demonstrating the value of geometry-aware models and dense geodetic benchmarks for cross-view geo-registration. Dataset available at https://parthpk.github.io/sk yreg-webpage.},
keywords = {ECCV, Geo-Localization, Remote Sensing},
pubstate = {published},
tppubtype = {conference}
}
Cross-view geo-registration is the task of aligning a query image to a geodetically accurate reference (e.g., satellite image), so that each query pixel maps to a real-world GPS coordinate. Most existing work addresses the related problem of cross-view geo-localization, where the goal is typically to estimate the camera center using retrieval, classification, matching, or regression. Because these approaches do not provide dense supervision, they are poorly suited for learning and evaluating pixel-wise alignment. We introduce SkyReg, a geometry-aware geo-registration model that estimates the transformation between the query and reference images by explicitly modeling the 3D scene geometry. Applying this transformation to warp the query into the reference frame yields pixel-wise geo-localization, without relying on 2D point matches, and remains robust to occlusions and large viewpoint changes. To enable training and standardized evaluation, we release (i) SkyReg-Train, a large-scale dataset of drone–satellite images annotated with per-pixel GPS coordinates, depth maps, and camera parameters derived from LiDAR and structure-from-motion, and (ii) SkyReg-Bench, a held-out benchmark of unseen Urban and Suburban scenes with the same dense annotations. SkyReg achieves state-of-the-art performance against strong retrieval and homography baselines, demonstrating the value of geometry-aware models and dense geodetic benchmarks for cross-view geo-registration. Dataset available at https://parthpk.github.io/sk yreg-webpage.