Datasets:

Wu52F
/

Image2GPS_dataset

license: mit
task_categories:
  - image-to-text
tags:
  - gps
  - geolocation
  - computer-vision
  - regression
  - campus
pretty_name: Image2GPS Penn Campus
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data_car/01_Split_Dataset/train-*
      - split: validation
        path: data_car/01_Split_Dataset/validation-*
      - split: test
        path: data_car/01_Split_Dataset/test-*
dataset_info:
  features:
    - name: image
      dtype: image
    - name: Latitude
      dtype: float64
    - name: Longitude
      dtype: float64
  splits:
    - name: train
      num_bytes: 1879686949
      num_examples: 525
    - name: validation
      num_bytes: 106413976
      num_examples: 29
    - name: test
      num_bytes: 106913215
      num_examples: 30
  download_size: 2093075615
  dataset_size: 2093014140

🗺️ Image2GPS — Penn Campus Dataset

Geotagged image dataset for predicting GPS coordinates from photos taken on the University of Pennsylvania campus.

📋 Overview

	Detail
🎯 Task	Image → GPS regression (latitude, longitude)
📍 Region	Penn campus: 33rd & Walnut → 34th & Spruce
📷 Sources	Human-height (~1.5m) & car-height (~0.15m)
📊 Metric	Average Haversine distance (meters) ↓

📁 Structure

data_human/
└── 01_Split_Dataset/
    ├── train/          # ~1595 images + metadata.csv
    ├── validation/     # ~199 images + metadata.csv
    └── test/           # ~200 images + metadata.csv

data_car/              # Car-height images (exploratory)

Each metadata.csv contains:

Column	Description
`file_name`	Image filename
`Latitude`	GPS latitude in decimal degrees
`Longitude`	GPS longitude in decimal degrees

🚀 Quick Start

from datasets import load_dataset

# Load human-height data
dataset = load_dataset("Wu52F/Image2GPS_dataset", data_dir="data_human/01_Split_Dataset")

train = dataset["train"]
print(train[0])  # {'image': <PIL>, 'Latitude': 39.952, 'Longitude': -75.193}

📷 Collection Protocol

Photos taken along walkways on Penn campus
8 photos per location (rotating 360°)
Phone held upright, no zoom
GPS extracted from EXIF metadata
HEIC images converted to JPEG with EXIF preserved

👥 Team

CIS 5190 Applied Machine Learning — Spring 2026
Team 15: Tao Wu, Yuchen Xu