Cross-View Geo-Localization task is aimed at establishing correspondences between images captured from different perspectives within the same geographical region. The major challenge lies in the significant appearance variations of the same scene in different views. Current methods predominantly rely on learning a representation of the coarse-grained information from images and then evaluating the similarity, while the fine-grained features are usually not well-treated. In this paper, a novel method, named DAC (Domain Alignment and scene Consistency) is proposed, which leverages contrastive learning to acquire the global information of images and simultaneously employs a domain space alignment module to align the fine-grained features. The comprehensive utilization of multi-grained vision information guarantees better feature representations. Additionally, a cross-batch scene consistency strategy is proposed in the network to establish the global supervision of the positive samples based on scene correspondence, which improves the distinctiveness of the image representations. Advanced performance is shown by our method in drone-view target localization and drone navigation applications, outperforming state-of-the-art methods in comprehensive tests on the popular public datasets University-1652 and SUES-200. Our method also outperforms existing methods in cross-region localization, showing an average improvement of 5.6% in the R@1. Our codes and models are available at https://github.com/SummerpanKing/DAC.

Cross-View Geo-Localization (CVGL) task aims to utilize geographic data, such as maps or high-resolution satellite images, as reference to estimate the positions of a ground- or near-ground- captured query image. This task is particularly challenging due to the significant changes in visual appearance resulting from the extreme viewpoint variations. To address this challenge, a range of innovative methods have been proposed. However, intra-scene geometric information and inter-scene discriminative representation are not fully explored. In this paper, we propose a novel CVGL method using Contrastive Attributes Mining and position-aware Partitioning (CAMP), which incorporates a Position-aware Partition Branch (PPB) and a Contrastive Attributes Mining (CAM) strategy. PPB learns fine-grained local features of different parts and captures their spatial information, providing a comprehensive understanding of scenes from both textual and spatial perspectives. CAM establishes supervision of the negative samples based on the images from the same platform, empowering the model to better discern differences between distinct scenes without extra memory cost. The proposed CAMP surpasses existing methods, achieving state-of-the-art results on the satellite-drone CVGL datasets University-1652 and SUES-200. Additionally, our method also outperforms existing methods in cross-dataset generalization, achieving an 8.85% increase in R@1 when trained on the University-1652 dataset and tested on the SUES-200 dataset at a height of 150 meters. Our code and model are available at https://github.com/Mabel0403/CAMP.

Limited by the noise, missing data and varying sampling density of the point clouds, planar primitives are prone to be lost during plane segmentation, leading to topology errors when reconstructing complex building models. In this paper, a pipeline to recover the broken topology of planar primitives (TopoLAP) is proposed to reconstruct level of details 3 (LoD3) models. Firstly, planar primitives are segmented from the incomplete point clouds and feature lines are detected both from point clouds and images. Secondly, the structural contours of each plane segment are reconstructed by subset selection from intersections of these feature lines. Subsequently, missing planes are recovered by plane deduction according to the relationships between linear and planar primitives. Finally, the manifold and watertight polyhedral building models are reconstructed based on the optimized PolyFit framework. Experimental results demonstrate that the proposed pipeline can handle partial incomplete point clouds and reconstruct the LoD3 models of complex buildings automatically. A comparative analysis indicates that the proposed method performs better to preserve sharp edges and achieves a higher fitness and correction rate than rooftop-based modeling and the original PolyFit algorithm.

Nadir viewing satellite image is an effective data source to generate orthomosaics. Because of the georeferencing error of satellite images, block adjustment is the first step of orthomosaic generation over a large area. However, the geometric relationship of the neighboring orbits of the nadir viewing images is not rigid enough. This paper proposes a new rational function model (RFM) block adjustment approach that constrains the tie point elevation to enhance the relative geometric rigidity. By interpolating the elevations of tie points in a digital elevation model (DEM) and estimating the a priori errors of the interpolated elevations, better overall relative accuracy is obtained, and the local optimal solution problem is avoided. By constraining the adjusted model parameters according to the a priori error of RFMs, block adjustment without ground control point (GCP) is performed. By optimal initializing the object-space positions of tie points with multi-backprojection method, the needed iteration times of block adjustment are reduced. The proposed approach is investigated with 46 Ziyuan-3 sensor-corrected images, a 1:50 000 scale DEM, and 586 GCPs. Compared with Teo's approach that constrains the horizontal coordinates and elevations of tie points, the approach in this paper converges much faster when the GCPs are sparse, and meanwhile, the absolute and relative accuracy of the two approaches are almost the same. The result of block adjustment with only four GCPs shows that no accuracy degeneration occurred in the test area and the root-mean-square error of independent check point reaches about 1.5 ground resolutions. Different DEMs and number of tie points are used to investigate whether the block adjustment result is influenced by these factors. The results show that better DEM accuracy and denser tie points do improve the accuracy when the images have large side-sway angles. The proposed approach is also tested with 5118 IKONOS-2 images that cover the southern Europe without GCP. The result shows that the relative mosaicking accuracy is much better than that of Grodecki's approach.