YILDIZ-VPR: A Novel Dataset with Dense Coverage Under Diverse Environmental Conditions for Visual Place Recognition

YILDIZ-VPR：一个用于视觉地点识别的、在多种环境条件下具有密集覆盖的新型数据集

Visual Place Recognition (VPR) aims to recognize the location of a query image by comparing it with a set of geo-referenced images. Although many datasets have been proposed for VPR, collecting dense and diverse visual data from pedestrian-level viewpoints is still an important need. 视觉地点识别（VPR）旨在通过将查询图像与一组带有地理参考的图像进行比较，来识别查询图像的位置。尽管目前已经提出了许多用于 VPR 的数据集，但从行人视角收集密集且多样的视觉数据仍然是一项重要的需求。

In this paper, we introduce YILDIZ-VPR, a visual geo-localization dataset collected through repeated walking traversals on the Davutpasa campus of Yildiz Technical University. The dataset includes outdoor scenes captured at different times of day, seasons, and weather conditions. It contains a wide range of visual content, including historical buildings, modern structures, roads, green areas, and wooded regions. 在本文中，我们介绍了 YILDIZ-VPR，这是一个通过在伊尔迪兹技术大学（Yildiz Technical University）达武特帕萨（Davutpasa）校区进行反复步行遍历所收集的视觉地理定位数据集。该数据集包含了在不同时间、季节和天气条件下拍摄的户外场景。它涵盖了广泛的视觉内容，包括历史建筑、现代结构、道路、绿地和林区。

Each video was recorded with a GoPro 9 camera and synchronized with GPS sensor data to provide location labels for the extracted frames. In addition to GPS coordinates, the dataset also includes auxiliary sensor information such as gyroscope, speed, and temperature data. 每段视频均使用 GoPro 9 摄像机录制，并与 GPS 传感器数据同步，从而为提取的帧提供位置标签。除了 GPS 坐标外，该数据集还包含辅助传感器信息，如陀螺仪、速度和温度数据。

With its dense coverage and long-term visual variability, YILDIZ-VPR provides a useful resource for studying image-based and temporal visual place recognition under realistic outdoor conditions. 凭借其密集的覆盖范围和长期的视觉可变性，YILDIZ-VPR 为研究现实户外条件下的基于图像和时间序列的视觉地点识别提供了一个有用的资源。