UAV-VisLoc Dataset Preparation

Prepare UAV-VisLoc dataset for cross-view geo-localization retrieval training. Generates satellite crops, GPS-matched drone-crop pairs, and Index files compatible with UAV-GeoLoc format.

Pipeline

UAV_VisLoc_dataset/                    UAV_VisLoc_processed/
├── 01/                                ├── 01/
│   ├── drone/*.JPG (3976x2652)  --->  │   ├── drone/*.JPG (512x512)
│   ├── satellite01.tif          --->  │   ├── DB/img/crop_X_Y.png (512x512)
│   └── 01.csv                         │   ├── DB/db_postion.txt
│                                      │   ├── positive.json
├── ...                                │   └── semi_positive.json
├── 09/                                ├── ...
│   ├── satellite09_01-01.tif  --+     ├── 09/ (stitched from 4 tiles)
│   ├── satellite09_01-02.tif    +--> │   ├── DB/img/ (~22K crops)
│   ├── satellite09_02-01.tif    |     │   └── ...
│   └── satellite09_02-02.tif  --+     │
├── satellite_ coordinates_range.csv   └── Index/
├── visloc_train.csv                       ├── train_query.txt
└── visloc_test.csv                        ├── test_query.txt
                                           ├── train_db.txt
                                           ├── test_db.txt
                                           └── all_db.txt

Quick Start

python scripts/prepare_dataset.py \
    --src /path/to/UAV_VisLoc_dataset \
    --dst /path/to/UAV_VisLoc_processed \
    --crop-size 512 --stride 256 --target-size 256

To process specific routes only:

python scripts/prepare_dataset.py --src ... --dst ... --routes 01 02 03

Steps

1. Resize drone images -> 512x512 JPEG (quality=95)
2. Stitch satellite tiles for route 09 (4 tiles -> 44800x33280)
3. Crop satellite maps -> 512x512 patches, stride 256 (50% overlap), saved as 512x512 PNG
4. Compute GPS for each crop center from satellite bbox + grid position
5. Match drone -> crops via vectorized haversine (positive = closest, semi-positive = +-1 in grid)
6. Write metadata: positive.json, semi_positive.json, db_postion.txt (per route)
7. Generate Index files: train_query.txt, test_query.txt, train_db.txt, test_db.txt, all_db.txt

Output Format (UAV-GeoLoc compatible)

Index files

train_query.txt / test_query.txt:

01/drone/01_0001.JPG 0 01/DB/img/crop_5_18.png 01/DB/img/crop_4_17.png ...

Format: query_path label positive_crop semi_positive_crops...

train_db.txt / test_db.txt / all_db.txt:

01/DB/img/crop_0_0.png
01/DB/img/crop_0_1.png
...

Full gallery (all 74,807 crops), identical for train and test (split is by query).

Per-route metadata

positive.json:

{"0001": ["crop_5_18.png"], "0002": ["crop_5_19.png"], ...}

Keys are frame IDs (without route prefix).

semi_positive.json:

{"0001": ["crop_4_17.png", "crop_4_18.png", ...], ...}

8 neighbors (+-1 in grid) of the positive crop.

db_postion.txt (tab-separated, matching UAV-GeoLoc spelling):

crop_0_0.png	115.97197337	29.77349180	2.68e-06	-2.68e-06

Columns: name, longitude, latitude, scale_lon (deg/px), scale_lat (deg/px).

Dataset Statistics

Route	Drone	Crops	Region	Satellite (px)
01	817	3,811	Changjiang	9774x26762
02	1,071	5,676	Changjiang	11482x34291
03	768	12,648	Taizhou	35092x24308
04	738	10,281	Taizhou	18093x38408
05	473	805	Yunnan	9394x6144
06	344	1,110	Zhuxi	8082x9780
07	--	--	Excluded	3000x170 (too narrow)
08	1,033	10,416	Huzhou	43421x16294
09	766	22,446	Huzhou	44800x33280 (stitched)
10	144	432	Huailai	6593x5077
11	590	7,182	Shandan	29592x16582
Total	6,744	74,807

Split: 5,060 train / 1,684 test queries. Gallery: 74,807 crops (shared). Disk size: 25 GB.

Image Resolution

All images stored at 512x512 on disk:

• Drone: resized from 3976x2652 / 3000x2000 -> 512x512
• Satellite crops: cut at 512x512 from satellite map, no downscale

Resolution 512 chosen to support downstream tasks (segmentation, depth, normals, canopy height). Resize to 224/256 for model input should be done in the dataloader, not on disk.

GPS Matching Quality

• Median distance drone -> positive crop: 25.9m
• P99: 45.7m
• Known issue: 6 drones in route 06 (06_0093-06_0098) are outside satellite coverage (~1,091m to nearest crop)

Satellite Resolution

All maps: ~0.30 m/pixel (single Google Earth zoom level). One crop 512x512 covers ~154x154m on the ground.

Requirements

numpy
Pillow

Memory

Peak RAM usage: ~8.7 GB (route 09 stitching: 4 tiles + merged image). Other routes: 1-3 GB.