Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+task_categories:
+  - image-to-text
+tags:
+  - gps
+  - geolocation
+  - computer-vision
+  - regression
+  - campus
+pretty_name: Image2GPS Penn Campus
+size_categories:
+  - 1K<n<10K
+---
+
+# 🗺️ 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
+
+```python
+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