This paper describes an investigation of the source of geospatial error in digital surface models (DSMs) constructed from multiple satellite images. In this study the uncertainty in surface geometry is separated into two spatial components; global error that affects the absolute position of the surface, and local error that varies from surface point to surface point. The global error component is caused by inaccuracy in the satellite imaging process, mainly due to uncertainty in the satellite position and orientation (pose) during image collection. A key result of the investigation is a new algorithm for determining the absolute geoposition of the DSM that takes into account the pose covariance of each satellite during image collection. This covariance information is used to weigh the evidence from each image in the computation of the global position of the DSM. The use of covariance information significantly decreases the overall uncertainty in global position. The paper also describes an approach to the prediction of local error in the DSM surface. The observed variance in surface position within a single stereo surface reconstruction defines the local horizontal error. The variance in the fused set of elevations from multiple stereo pairs at a single DSM location defines the local vertical error. These accuracy predictions are compared to ground truth provided by LiDAR scans of the same geographic region of interest.
Australian National University（澳大利亚国立大学）；University of Oxford（牛津大学）；University of Technology Sydney（悉尼科技大学）
This paper presents a new approach for synthesizing a novel street-view panorama given an overhead satellite image. Taking a small satellite image patch as input, our method generates a Google's omnidirectional street-view type panorama, as if it is captured from the same geographical location as the center of the satellite patch. Existing works tackle this task as an image generation problem which adopts generative adversarial networks to implicitly learn the cross-view transformations, while ignoring the domain relevance. In this paper, we propose to explicitly establish the geometric corres…
deSEO: Physics-Aware Dataset Creation for High-Resolution Satellite Image Shadow Removal
Austrian Institute of Technology（奥地利理工学院）；University of Cambridge（剑桥大学）；Technical University of Munich (TUM)（慕尼黑工业大学）；Friedrich Schiller University of Jena（耶拿弗里德里希·席勒大学）
How to Reduce Localization Ambiguity? Geometry-Semantic Constrained BEV Representation Learning for Satellite-Ground Localization
The Hong Kong Polytechnic University（香港理工大学）；Southern University of Science and Technology（南方科技大学）；Wuhan University（武汉大学）；The Chinese University of Hong Kong（香港中文大学）；Huawei Technologies Co., Ltd（华为技术有限公司）
Australian National University（澳大利亚国立大学）；Huawei（华为）；University of Oxford（牛津大学）；Data61/CSIRO（澳大利亚联邦科学与工业研究组织数据61所）
We address the problem of ground-to-satellite image geo-localization, that is, estimating the camera latitude, longitude and orientation (azimuth angle) by matching a query image captured at the ground level against a large-scale database with geotagged satellite images. Our prior arts treat the above task as pure image retrieval by selecting the most similar satellite reference image matching the ground-level query image. However, such an approach often produces coarse location estimates because the geotag of the retrieved satellite image only corresponds to the image center while the ground …
Sat2City v2: Native 3D City Asset Generation from a Single Satellite Image
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Chinese University of Hong Kong（香港中文大学）；Auckland University of Technology（奥克兰理工大学）；Department of Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）
EO-VGGT: Orbital Ray-Conditioned 3D Foundation Models for Satellite Multi-View Reconstruction
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；Hubei Luojia Laboratory（湖北珞珈实验室）
Longevity Associated Geometry Identified in Satellite Images: Sidewalks, Driveways and Hiking Trails
Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）；Dartmouth Hitchcock Medical Center（达特茅斯-希区柯克医疗中心）；The Dartmouth Institute for Health Policy and Clinical Practice（达特茅斯健康政策与临床实践研究所）
Geometric Consistency Protocol for Foundation Model Features in Multi-View Satellite Imagery
Hubei Province Key Research and Development Program（湖北省重点研发计划）；LIESMARS Special Research Funding（测绘遥感信息工程国家重点实验室专项研究基金）；National Science Fund for Distinguished Young Scholars（国家杰出青年科学基金）
GUPNet++: Geometry Uncertainty Propagation Network for Monocular 3D Object Detection
School of Electrical and Information Engineering, University of Sydney（悉尼大学电气与信息工程学院）；Shanghai AI Laboratory（上海人工智能实验室）；SenseTime Group Limited（商汤科技集团有限公司）；University of Science and Technology of China（中国科学技术大学）
