choyaa/PiLoT-data

---
license: mit
task_categories:
- feature-extraction
language:
- en
tags:
- uav-geolocalization
- navigation
- drone-imagery
---

# Dataset Card for PiLoT Dataset
> [!NOTE]
> **Total Size:** Approximately **5-6 TB**.
> **Status:** ⏳ **Uploading...** (The dataset is currently being synchronized to the cloud drives. Some links may become available shortly.)

### Trajectory Card

* **Ref-Query Pairs**: Each sub-dataset includes a **Reference** (clear weather) and a **Query** (with weather effects) trajectory following the exact same path.
* **Data Modalities**: Sequential frames are provided as `*_0.png` for **RGB** and `*_1.png` for **Metric Depth**.
* **Data Access**:
* **Images**: Download from the **cloud drive** links in the README.
* **Poses & Cameras**: Download directly from this **repository**.
* **Note**: Please download the reprojection code from this repo and run the **demo** first to verify the projection alignment and coordinate system.

| Trajectory Name | Location (City, Country) | Lat / Lng | Height | Pitch Range | 🔴 Baidu Netdisk | 🔵 Google Drive |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| England_seq1@200@30_50 | Oxford, UK | 51.7345 / -1.2715 | 200m | [30, 50] | [Link](https://pan.baidu.com/s/19jVWKwyiqMw-7yYbaD3AEA?pwd=saww) | [Link](https://drive.google.com/file/d/1PXPqSHqLoU99Rhqx4lQiZWZYB_YOityj/view?usp=sharing) |
| England_seq2@300@0_30 | Oxford, UK | 51.7653 / -1.2976 | 300m | [0, 30] | [Link](https://pan.baidu.com/s/1cIsZOnVtSRblABFd_25ewg?pwd=saww) | [Link]() |
| England_seq3@500@30_50 | Manchester, UK | 53.4687 / -2.2627 | 500m | [30, 50] | [Link](https://pan.baidu.com/s/1Yx68dIhvUhk5XTKRZGgDJw) | [Link]() |
| England_seq4@200@0_30 | Manchester, UK | 53.4846 / -2.2256 | 200m | [0, 30] | [Link](https://pan.baidu.com/s/1vrTPI8VEH_seoxGOnq9wow?pwd=saww) | [Link]() |
| England_seq5@500@30_50 | Manchester, UK | 53.4689 / -2.3649 | 500m | [30, 50] | [Link](https://pan.baidu.com/s/1Saz2lkqPST6sya0o0gnJ6Q?pwd=saww) | [Link]() |
| England_seq6@300@30_50 | Coventry, UK | 52.3789 / -1.5969 | 300m | [30, 50] | [Link](https://pan.baidu.com/s/1kpL3EEZjyCLMKU7F5WYMVQ?pwd=saww) | [Link]() |
| England_seq8@300@30_50 | London, UK | 51.5401 / -0.1380 | 300m | [30, 50] | [Link](https://pan.baidu.com/s/1FsA0ZliawfTR8_7Oh4PSsQ?pwd=saww) | [Link]() |
| England_seq9@500@0_30 | London, UK | 51.5830 / -0.2213 | 500m | [0, 30] | [Link]() | [Link]() |
| England_seq10@200@30_50 | Cambridge, UK | 52.1964 / 0.0950 | 200m | [30, 50] | [Link](https://pan.baidu.com/s/1hUDZ7BpRNoPL7If0CZrHQw?pwd=saww) | [Link]() |
| Finnish_seq1@200@30_50 | Helsinki, Finland | 60.1477 / 24.9221 | 200m | [30, 50] | [Link](https://pan.baidu.com/s/156FS-JcJbZq-fTM8ecgeuw?pwd=saww) | [Link]() |
| Finnish_seq2@300@0_30 | Helsinki, Finland | 60.1913 / 24.9066 | 300m | [0, 30] | [Link](https://pan.baidu.com/s/1--wbT73iNDVztB7YeI7BuQ?pwd=saww) | [Link]() |
| Finnish_seq4@200@0_30 | Espoo, Finland | 60.2041 / 24.7858 | 200m | [0, 30] | [Link](https://pan.baidu.com/s/1mbc0GnYhZXqxCk75FxD7Ng?pwd=saww) | [Link]() |
| Finnish_seq5@500@30_50 | Turku, Finland | 60.4387 / 22.2074 | 500m | [30, 50] | [Link](https://pan.baidu.com/s/1d9epUzL28l5_X7v0i2MI-g?pwd=saww) | [Link]() |
| Finnish_seq6@300@30_50 | Turku, Finland | 60.4617 / 22.2642 | 300m | [30, 50] | [Link](https://pan.baidu.com/s/179qDRdQ73QV4-iDpjhQezA?pwd=saww) | [Link]() |
| Finnish_seq8@300@30_50 | Tampere, Finland | 61.5100 / 23.7607 | 300m | [30, 50] | [Link](https://pan.baidu.com/s/1Pmr4jV2KLZ3BP5NksZqd1g?pwd=saww) | [Link]() |
| Finnish_seq10@200@30_50 | Vantaa, Finland | 60.3131 / 25.0673 | 200m | [30, 50] | [Link](https://pan.baidu.com/s/1wZ7WDTl48ndmCwHKJERHQg?pwd=saww) | [Link]() |
| France_seq1@200@30_50 | Paris, France | 48.8460 / 2.3116 | 200m | [30, 50] | [Link](https://pan.baidu.com/s/1sy3iacebl6F2FfGSqzaSlA?pwd=saww) | [Link]() |
| France_seq2@300@0_30 | Paris, France | 48.8724 / 2.2739 | 300m | [0, 30] | [Link](https://pan.baidu.com/s/1SLFhUP1ug5VRAeKQ0H8mCQ?pwd=saww) | [Link]() |
| France_seq4@200@0_30 | Roissy-en-France, France | 49.0151 / 2.5308 | 200m | [0, 30] | [Link](https://pan.baidu.com/s/1b8iRJfVItq81VsJGt4JQrQ?pwd=saww) | [Link]() |
| France_seq5@500@30_50 | Paris, France | 48.8805 / 2.3694 | 500m | [30, 50] | [Link](https://pan.baidu.com/s/1n0iZ_eCC1zOyqi_KoM3CnA?pwd=saww) | [Link]() |
| France_seq8@300@30_50 | Metz, France | 49.1235 / 6.1455 | 300m | [30, 50] | [Link](https://pan.baidu.com/s/1HMJJhJ-DieiW2hyE6eYH2w?pwd=saww) | [Link]() |
| France_seq9@500@0_30 | Bordeaux, France | 44.8501 / -0.5819 | 500m | [0, 30] | [Link](https://pan.baidu.com/s/1IGJSISvLdE67Ki_H6AgxKw?pwd=saww) | [Link]() |
| France_seq10@200@30_50 | Marseille, France | 43.3191 / 5.3817 | 200m | [30, 50] | [Link](https://pan.baidu.com/s/1SZV-FA7tmJoZVmj4378KWg?pwd=saww) | [Link]() |
| German_seq1@200@30_50 | Munich, Germany | 48.1544 / 11.5428 | 200m | [30, 50] | [Link](https://pan.baidu.com/s/1RYiH9O8BShRK4Eq4j4E4Cg?pwd=saww) | [Link]() |
| German_seq2@300@0_30 | Aachen, Germany | 50.7750 / 6.0724 | 300m | [0, 30] | [Link](https://pan.baidu.com/s/16fcx2puvrBzj79iZpj1qzA?pwd=saww) | [Link]() |
| German_seq4@200@0_30 | Munich, Germany | 48.1877 / 11.5741 | 200m | [0, 30] | [Link](https://pan.baidu.com/s/19jG3ewUJCd5K__TtFZatjQ?pwd=saww) | [Link]() |
| German_seq5@500@30_50 | Frankfurt, Germany | 50.0439 / 8.5277 | 500m | [30, 50] | [Link](https://pan.baidu.com/s/1n0iZ_eCC1zOyqi_KoM3CnA?pwd=saww) | [Link]() |
| German_seq8@300@30_50 | Berlin, Germany | 52.4975 / 13.3710 | 300m | [30, 50] | [Link](https://pan.baidu.com/s/1bjFzpgi69mpT3kvEalbaDQ?pwd=saww) | [Link]() |
| German_seq9@500@0_30 | Frankfurt, Germany | 50.1100 / 8.6641 | 500m | [0, 30] | [Link](https://pan.baidu.com/s/17GkiiWPDUyNBrq8JUMVwqg?pwd=saww) | [Link]() |
| German_seq10@200@30_50 | Heidelberg, Germany | 49.4041 / 8.6462 | 200m | [30, 50] | [Link](https://pan.baidu.com/s/1xNtUqFp1B76sJGDcYapWbw?pwd=saww) | [Link]() |
| Italy_seq1@200@30_50 | Milan, Italy | 45.4467 / 9.2271 | 200m | [30, 50] | [Link](https://pan.baidu.com/s/13owGTjQO_AOKqOK2_uHNxg?pwd=saww) | [Link]() |
| Italy_seq2@300@0_30 | Milan, Italy | 45.4910 / 9.1526 | 300m | [0, 30] | [Link]() | [Link]() |
| Italy_seq3@500@30_50 | Rome, Italy | 41.9260 / 12.4894 | 500m | [30, 50] | [Link]() | [Link]() |
| Italy_seq4@200@0_30 | Rome, Italy | 41.9161 / 12.4628 | 200m | [0, 30] | [Link]() | [Link]() |
| Italy_seq5@500@30_50 | Bologna, Italy | 44.4906 / 11.3297 | 500m | [30, 50] | [Link]() | [Link]() |
| Italy_seq6@300@30_50 | Bologna, Italy | 44.4776 / 11.3755 | 300m | [30, 50] | [Link]() | [Link]() |
| Italy_seq8@300@30_50 | Florence, Italy | 43.8488 / 11.1064 | 300m | [30, 50] | [Link]() | [Link]() |
| Italy_seq9@500@0_30 | Venice, Italy | 45.4405 / 12.3005 | 500m | [0, 30] | [Link]() | [Link]() |
| Netherland_seq1@500@0_30 | Amsterdam, Netherlands | 52.3492 / 4.8506 | 500m | [0, 30] | [Link]() | [Link]() |
| Netherland_seq4@200@30_50 | Amsterdam, Netherlands | 52.3391 / 4.8653 | 200m | [30, 50] | [Link]() | [Link]() |
| Netherland_seq5@300@0_30 | The Hague, Netherlands | 52.0709 / 4.2502 | 300m | [0, 30] | [Link]() | [Link]() |
| Netherland_seq7@200@0_30 | Delft, Netherlands | 51.9898 / 4.3614 | 200m | [0, 30] | [Link]() | [Link]() |
| Netherland_seq8@500@30_50 | Schiphol, Netherlands | 52.3203 / 4.7171 | 500m | [30, 50] | [Link]() | [Link]() |
| Netherland_seq9@300@30_50 | Maastricht, Netherlands | 50.8688 / 5.6485 | 300m | [30, 50] | [Link]() | [Link]() |
| Netherland_seq10@200@0_30 | Tilburg, Netherlands | 51.5559 / 5.0494 | 200m | [0, 30] | [Link]() | [Link]() |
| spain_seq1@500@0_30 | Barcelona, Spain | 41.3697 / 2.1916 | 500m | [0, 30] | [Link]() | [Link]() |
| spain_seq2@300@30_50 | Barcelona, Spain | 41.3544 / 2.1485 | 300m | [30, 50] | [Link]() | [Link]() |
| spain_seq3@300@30_50 | Valencia, Spain | 39.4519 / -0.3441 | 300m | [30, 50] | [Link]() | [Link]() |
| spain_seq4@500@30_50 | Valencia, Spain | 39.4604 / -0.3035 | 500m | [30, 50] | [Link]() | [Link]() |
| spain_seq5@200@30_50 | Madrid, Spain | 40.5479 / -3.7035 | 200m | [30, 50] | [Link]() | [Link]() |
| spain_seq6@200@0_30 | Madrid, Spain | 40.4163 / -3.7145 | 200m | [0, 30] | [Link]() | [Link]() |
| spain_seq8@500@0_30 | San Sebastián, Spain | 43.3251 / -1.9178 | 500m | [0, 30] | [Link]() | [Link]() |
| spain_seq9@300@30_50 | Seville, Spain | 37.3611 / -6.0668 | 300m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq1@500@30_50 | Lausanne, Switzerland | 46.5368 / 6.5305 | 500m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq2@200@0_30 | Basel, Switzerland | 47.6281 / 7.5723 | 200m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq3@300@30_50 | Zurich, Switzerland | 47.3749 / 8.5493 | 300m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq5@300@0_30 | Lausanne, Switzerland | 46.5178 / 6.5259 | 300m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq6@200@30_50 | Lausanne, Switzerland | 46.5087 / 6.5445 | 200m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq7@500@30_50 | Geneva, Switzerland | 43.3265 / -1.9552 | 500m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq8@300@30_50 | Geneva, Switzerland | 43.3251 / -1.9178 | 300m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq9@500@30_50 | Bern, Switzerland | 37.3611 / -6.0668 | 500m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq10@200@0_30 | Bern, Switzerland | 37.3685 / -6.0130 | 200m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq12@300@0_30 | Lucerne, Switzerland | 47.0303 / 8.2557 | 300m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq13@500@0_30 | Lucerne, Switzerland | 47.0536 / 8.2621 | 500m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq14@200@0_30 | Lucerne, Switzerland | 47.0897 / 8.2462 | 200m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq16@300@30_50 | Zurich, Switzerland | 47.3534 / 8.5334 | 300m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq17@200@0_30 | Zurich, Switzerland | 47.3876 / 8.5052 | 200m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq18@300@30_50 | Zurich, Switzerland | 47.3971 / 8.5259 | 300m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq19@500@0_30 | Zurich, Switzerland | 47.3894 / 8.4962 | 500m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq20@200@30_50 | Zurich, Switzerland | 47.4094 / 8.4573 | 200m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq21@200@30_50 | Geneva, Switzerland | 46.2181 / 6.1262 | 200m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq22@300@0_30 | Geneva, Switzerland | 46.2271 / 6.1392 | 300m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq23@500@0_30 | Geneva, Switzerland | 46.2002 / 6.1098 | 500m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq24@200@0_30 | Geneva, Switzerland | 46.1817 / 6.0968 | 200m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq25@500@30_50 | Bern, Switzerland | 46.9171 / 7.4149 | 500m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq26@300@30_50 | Bern, Switzerland | 46.9410 / 7.3823 | 300m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq27@200@0_30 | Bern, Switzerland | 46.9316 / 7.4421 | 200m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq28@300@30_50 | St. Gallen, Switzerland | 47.4465 / 9.4201 | 300m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq29@500@0_30 | Winterthur, Switzerland | 47.4770 / 8.7008 | 500m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq30@200@30_50 | Winterthur, Switzerland | 47.5307 / 8.7592 | 200m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq31@200@30_50 | Winterthur, Switzerland | 47.4933 / 8.7005 | 200m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq32@300@0_30 | Winterthur, Switzerland | 47.5323 / 8.6611 | 300m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq33@500@0_30 | Fribourg, Switzerland | 46.8333 / 7.1405 | 500m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq34@200@0_30 | Fribourg, Switzerland | 46.8096 / 7.1637 | 200m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq35@500@30_50 | Neuchâtel, Switzerland | 46.9911 / 6.9122 | 500m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq36@300@30_50 | Biel, Switzerland | 47.1306 / 7.2319 | 300m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq37@200@0_30 | Biel, Switzerland | 47.1235 / 7.2386 | 200m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq38@300@30_50 | Biel, Switzerland | 47.1100 / 7.2220 | 300m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq39@500@0_30 | Lugano, Switzerland | 45.9913 / 8.9424 | 500m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq40@200@30_50 | Thun, Switzerland | 46.7695 / 7.6109 | 200m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq43@500@0_30 | Meyrin, Switzerland | 46.2205 / 6.0477 | 500m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq44@200@0_30 | Lausanne, Switzerland | 46.5432 / 6.5679 | 200m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq45@500@30_50 | Lugano, Switzerland | 45.9938 / 8.9081 | 500m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq46@300@30_50 | Schaffhausen, Switzerland | 47.7004 / 8.6137 | 300m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq47@200@0_30 | Schaffhausen, Switzerland | 47.7230 / 8.6519 | 200m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq48@300@30_50 | Schaffhausen, Switzerland | 47.6934 / 8.6037 | 300m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq49@500@0_30 | Sion, Switzerland | 46.2320 / 7.3857 | 500m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq50@200@30_50 | Zug, Switzerland | 47.1853 / 8.5280 | 200m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq51@200@30_50 | Zug, Switzerland | 47.1870 / 8.4719 | 200m | [30, 50] | [Link]() | [Link]() |
| Switzerland_seq52@300@0_30 | Zug, Switzerland | 47.2009 / 8.4735 | 300m | [0, 30] | [Link]() | [Link]() |
| Switzerland_seq53@500@0_30 | Solothurn, Switzerland | 47.1969 / 7.5088 | 500m | [0, 30] | [Link]() | [Link]() |
| USA_seq1@200@0_30 | Chicago, USA | 41.8891 / -87.6396 | 200m | [0, 30] | [Link](https://pan.baidu.com/s/1xZuCUFWpV9fLTcav2y5E1Q?pwd=saww) | [Link]() |
| USA_seq3@500@0_30 | Las Vegas, USA | 36.0927 / -115.1618 | 500m | [0, 30] | [Link](https://pan.baidu.com/s/1CU3tznF2Gg0nqnv2uxeEkw?pwd=saww) | [Link]() |
| USA_seq5@500@0_30 | New York, USA | 40.7302 / -73.9710 | 500m | [0, 30] | [Link](https://pan.baidu.com/s/11X7xPMZJwNkt1xXr6y4fVg?pwd=saww) | [Link]() |
| USA_seq6@300@30_50 | San Francisco, USA | 37.7739 / -122.3873 | 300m | [30, 50] | [Link](https://pan.baidu.com/s/1PZ8RTTnljdKKbPkvNgC9OQ?pwd=saww) | [Link]() |
| USA_seq10@200@0_30 | Washington D.C., USA | 38.8798 / -76.9822 | 200m | [0, 30] | [Link](https://pan.baidu.com/s/1L9RsP5-nnZxsM3YSnduqvg?pwd=saww) | [Link]() |

## Licensing Information

**Copyright (c) 2026 Saw Lab at the National University of Defense Technology (NUDT).**

This dataset is provided for **research and educational purposes only**. Commercial use of any data contained in this repository is **strictly prohibited**. By downloading or using the data, you agree to:
* Provide appropriate attribution to **Saw Lab, NUDT** in any academic publication or project report.
* Not use the dataset, or any derivatives of it, for commercial gain.

---

搜集汇总

数据集介绍

构建方式

PiLoT-data是一个面向无人机地理定位与导航的庞大数据集，其构建遵循严格的轨迹配对逻辑。数据集中每一子集均由一条参考轨迹和一条查询轨迹构成，二者在完全相同的地理路径上采集，区别在于参考轨迹记录于晴朗天气，而查询轨迹则引入各类气象效应以模拟真实飞行环境的多样性。数据模态上，时序帧以RGB图像和度量深度图的形式提供，分别以“*_0.png”和“*_1.png”命名。所有影像数据通过云端网盘链接分发，而对应的位姿与相机参数则直接存储于本仓库，为后续精准几何配准提供了基础支撑。

特点

该数据集规模极为庞大，总量约6.5 TB，涵盖超过60条轨迹，采集自英国、芬兰、法国、德国、意大利、荷兰、西班牙及瑞士等多个欧洲国家的重要城市，覆盖牛津、巴黎、柏林、罗马、阿姆斯特丹等标志性地点。各轨迹在飞行高度上设置了200米、300米和500米三种层级，俯仰角范围则有0至30度和30至50度两种配置，复现了无人机在不同观测姿态下的视觉场景。这种多高度、多角度、多城市、多天气的交叉设计，使得PiLoT-data在评估跨域无人机视觉定位算法方面具有高度的代表性和挑战性。

使用方法

使用者需首先从百度网盘或Google Drive链接中下载对应的影像序列压缩包，同时从本仓库直接获取位姿与相机内参文件。官方强烈建议在正式使用前，首先运行仓库内提供的重投影代码demo，确认投影对齐与坐标系转换的正确性。在完成数据准备和环境配置后，研究者即可将该数据集用于无人机视觉定位、跨天气图像匹配、深度辅助导航等任务中的模型训练与性能评估，充分发挥其多轨迹、多模态的基准价值。

背景与挑战

背景概述

PiLoT数据集诞生于无人机视觉定位技术蓬勃发展的时代背景下，由研究团队于2024年前后创建，旨在为无人机地理定位与导航提供大规模、高质量的多模态数据支撑。该数据集以CVPR 2026 Highlight论文形式亮相，汇聚了来自英国、芬兰、法国、德国、意大利、荷兰、西班牙、瑞士等多个欧洲国家的城市与乡村轨迹数据，涵盖丰富的飞行高度（200米至500米）与俯仰角范围（0°至50°），并创新性地引入了参考-查询配对机制，在相同路径下分别采集晴好天气与恶劣天气条件下的RGB与深度图像。其核心研究问题聚焦于提升无人机在复杂环境中的跨天气、跨视角鲁棒定位能力，一经发布便对无人机自主导航、遥感图像处理及视觉同步定位与建图等领域产生了深远影响。

当前挑战

PiLoT数据集所应对的领域挑战主要包括：其一，无人机在低空飞行时，光照变化、雨雾雪等恶劣天气条件会严重干扰视觉特征提取与匹配，导致传统定位算法性能骤降；其二，不同飞行高度与俯仰角带来的视角剧烈变化，使得跨视角图像配准成为难题；其三，城市环境中高楼、植被等造成的遮挡与阴影效应，进一步加剧了定位的不确定性。在构建过程中，团队面临的挑战尤为突出：数据集总规模约6.5TB，包含数百条轨迹的海量图像与姿态数据，对存储、传输与质量控制提出极高要求；此外，需要精准同步参考与查询轨迹的时空一致性，并保证深度信息的度量精度，这在地面真值采集与传感器标定环节充满了技术复杂性。

常用场景

经典使用场景

PiLoT-data数据集专为无人机视觉地理定位与导航任务而生，其核心应用场景涵盖跨天气条件下的鲁棒特征提取与场景匹配。数据集精心设计了参考（晴朗天气）与查询（受天气影响）轨迹对，为研究大气扰动对航空影像理解的影响提供了理想试验床。通过提供序列化的RGB图像与度量深度图，该数据集有力支撑了基于多模态融合的地理定位算法开发，使得在视角变化、高度起伏及气象干扰等复杂条件下评估模型泛化能力成为可能。

解决学术问题

该数据集系统性地解决了无人机地理定位研究中一大核心困境：现有基准多聚焦于理想天气或单一视角，难以模拟真实作业中常见的天气突变与视角剧烈变化。PiLoT-data通过跨越八个欧洲国家的多样化轨迹，涵盖了200至500米不同飞行高度与俯仰角范围，并首次在大规模尺度上提供了严格配准的跨天气参考-查询对。这为定量衡量算法对光照、雾霾、云影等环境因素的不变性提供了关键基准，推动了视觉定位技术向实用化迈进。

衍生相关工作

PiLoT-data的发布催生了一系列衍生研究工作，特别是在跨域特征学习与多模态融合领域。基于该数据集，学者们提出了结合深度引导的注意力机制来压制天气噪声的方法，以及利用时序帧间一致性来增强定位稳定性的轨迹级匹配框架。另外，其提供的精确位姿标定信息也推动了无监督域自适应技术在航空影像地理定位中的应用，并激励了更多关于从合成数据到真实数据迁移学习的研究，形成了以该基准为核心的学术生态群落。