geo-localization

Contains data from three platforms, i.e., synthetic drones, satellites and ground cameras of 1,652 university buildings around the world. University-1652 is a drone-based geo-localization dataset and enables two new tasks, i.e., drone-view target localization and drone navigation. Source: University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization

The ShipSG dataset is the first public dataset of its kind for ship segmentation and georeferencing. The dataset has been made available for the development and evaluation of instance segmentation and georeferencing methods using computer vision and deep learning, thus advancing the research field of ship recognition for maritime situational awareness. ShipSG consists of 3505 images of a port location using two different cameras with static and oblique view. In total, 11625 ship masks grouped in seven ship classes were manually annotated. Moreover, each image contains one geographic annotation of one of the ship masks (latitude and longitude) present in the image.

CV-Cities comprises $223,736$ ground panoramic images and an equal number of satellite images all accompanied by high-precision GPS coordinates. These images represent sixteen representative cities across five continents. The ground images are $360^{\circ}$ panorama images with a resolution of $4,096 \times 2,048$ pixels, while the resolution of satellite images is $746 \times 746$ pixels, and are captured at a zoom level of $20$. The spatial resolution is $0.298 m$, corresponding to a latitude and longitude range of $0.002 \times 0.002^{\circ}$ (about $222 \times 222 m$). The images of each city in the dataset can be used for training and testing purposes.

Cross-View Geo-Localisation within urban regions is challenging in part due to the lack of geo-spatial structuring within current datasets and techniques. We propose utilising graph representations to model sequences of local observations and the connectivity of the target location. Modelling as a graph enables generating previously unseen sequences by sampling with new parameter configurations. SpaGBOL contains 98,855 panoramic streetview images across different seasons, and 19,771 corresponding satellite images from 10 mostly densely populated international cities. This translates to 5 panoramic images and one satellite image per graph node. Downloading instructions below.

DenseUAV is a dataset of drone and satellite perspectives collected from 14 universities in low-altitude urban scenes. The main features include real scene sampling, sampling perspective perpendicular to the ground, and dense sampling. A total of 3033 sampling points, including 9099 drone perspective images and 18198 satellite perspective images.

GTA-UAV dataset provides a large continuous area dataset (covering 81.3km²) for UAV visual geo-localization, expanding the previously aligned drone-satellite pairs to arbitrary drone-satellite pairs to better align with real-world application scenarios. Our dataset contains: - 33,763 simulated drone-view images, from multiple altitudes (80-650m), multiple attitudes, multiple scenes (urban, mountain, coast, forest, etc.). - 14,640 tiled satellite-view images from 4 zoom levels for arbitrarily pairing. - Overlap (in IoU) of FoV for each drone-satellite pair. - Drone (camera) 6-DoF labels for each drone image.

PDFM Embeddings are condensed vector representations designed to encapsulate the complex, multidimensional interactions among human behaviors, environmental factors, and local contexts at specific locations. These embeddings capture patterns in aggregated data such as search trends, busyness trends, and environmental conditions (maps, air quality, temperature), providing a rich, location-specific snapshot of how populations engage with their surroundings. Aggregated over space and time, these embeddings ensure privacy while enabling nuanced spatial analysis and prediction for applications ranging from public health to socioeconomic modeling.

This dataset builds upon the SpaGBOL dataset - a graph-based dataset covering numerous cities across the globe for the purpose of structured city-scale Cross-View Geo-Localisation (CVGL). We introduce the idea of Multi-View Geo-Localisation (MVGL) - localising in two steps within a graph. First using CVGL to retrieve the most likely primary nodes (road junctions), before performing relative pose estimation also candidate edges to increase precision. The corresponding initial network proposed reduced median error from hundreds of metres down to approximately 22m error across the entire dataset.