Obtaining the geographical location of images through image geo-localization technology is a highly significant task. However, existing image geo-localization methods struggle with accuracy under difficult conditions such as viewpoint changes, illumination variations, seasonal changes, and occlusions. To address these challenges, we proposed an image geo-localization architecture based on a foundation vision model and feature mixing. The architecture involves truncating and fine-tuning the foundation vision model DINOv2 to extract robust image features, which are then aggregated using an MLP-Mixer-based mix module to obtain robust and generalized image global features. This architecture significantly improves the accuracy of image geo-localization under difficult conditions. Experimental results demonstrate that the proposed architecture outperforms state-of-the-art (SOTA) methods in image geo-localization accuracy. Compared to SOTA methods, our architecture achieves accuracy improvements of 6.35%, 4.06%, and 6.30% on the test sets Tokyo 24/7, Nordland, and SF-XL-testv1, respectively, with viewpoint changes, illumination changes, season changes, and occlusions.
Traditionally, the correction of panoramic images has been achieved through sundial projection or cubic projection. However, these methods have not adequately addressed the issues of overlap and fixed pose relationships in the corrected images. To address this, we propose an enhanced algorithm for correcting panoramic images using cubic projection. The transformation process involves mapping the spherical panoramic image onto a cube that is tangent to the panoramic sphere, with the cube's side length exceeding the diameter of the sphere. For the re-projection correction experiments, we selected five sets of panoramic images and compared feature extraction and matching using the SuperPoint, SuperGlue, and R2D2 algorithms. The experimental results demonstrate that our proposed method outperforms existing methods in terms of image re-projection, showing significant enhancements in image overlap, accuracy, and the number of feature matches.
Cross-view geolocation, which aims to geolocate ground-view images using reference satellite imagery, is a challenging task that requires effective strategies to minimize significant disparities between images. In this paper, we propose a novel approach to address this challenge. By exploiting the projection relationship between ground images and satellite images, we are able to convert ground-view images into satellite-view images, thereby mitigating the inherent disparities between the two perspectives. To enhance the converted images further, we employ a conditional generative adversarial network (CGAN). This network generates satellite perspective images that exhibit greater consistency with the actual satellite data, thereby reducing excessive disparities and improving overall image quality. Additionally, we adopt a joint training approach in a multi-task setting, wherein we synthesize satellite images from ground images and conduct cross-view image matching. This framework facilitates mutual learning between the tasks and leads to improved performance. Empirical evaluation of our proposed method showcases significant advancements in terms of retrieval accuracy and synthesis quality when compared to existing techniques. These findings underscore the potential of our approach in addressing the challenges associated with cross-view geolocation.
Cyberspace mapping has emerged as a new and compelling area of research within the field of cartography. This paper addresses the challenge of overlapping points in cyberspace visualization, which poses difficulties for visual analysis. To tackle this issue, we propose a cyberspace metaphorical cartogram map that combines metaphorical mapping and continuous cartogram technology. Additionally, based on our method, we use our method to construct a cyberspace map using global IPv6 data from the Asia-Pacific Network Information Center (APNIC) to analyze the current development pattern of the global IPv6 network. Experimental results demonstrate the effectiveness of our method in avoiding node duplication, aligning with the reading logic of geospatial maps, and providing an efficient visualization approach for data comprehension.
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