Wu52F/Image2GPS_dataset

🗺️ 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