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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text
+tuxun_combined* filter=lfs diff=lfs merge=lfs -text

geocomp/README.md

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+---
+language:
+- en
+- zh
+pretty_name: GeoComp
+tags:
+- GeoLocation
+size_categories:
+- 10M<n<100M
+
+---
+
+# GeoComp
+
+## Dataset description
+
+Inspired by [geoguessr.com](https://www.geoguessr.com/), we developed a free geolocation game platform that tracks participants' competition histories.
+Unlike most geolocation websites, including Geoguessr, which rely solely on samples from Google Street View, our platform integrates Baidu Maps and Gaode Maps to address coverage gaps in regions like mainland China, ensuring broader global accessibility.
+The platform offers various engaging competition modes to enhance user experience, such as team contests and solo matches.
+Each competition consists of multiple questions, and teams are assigned a "vitality score". Users mark their predicted location on the map, and the evaluation is based on the ground truth's surface distance from the predicted location. 
+Larger errors result in greater deductions from the team's vitality score. 
+At the end of the match, the team with the higher vitality score wins.
+We also provide diverse game modes, including street views, natural landscapes, and iconic landmarks.
+Users can choose specific opponents or engage in random matches.
+To prevent cheating, external search engines are banned, and each round is time-limited.
+To ensure predictions are human-generated rather than machine-generated, users must register with a phone number, enabling tracking of individual activities. 
+Using this platform, we collected **GeoComp**, a comprehensive dataset covering 1,000 days of user competition.
+
+## File Introduction
+
+The GeoComp dataset is now primarily provided in Parquet format within the `/data` directory for efficient access and processing. You can find the following files in this repository:
+
+* [**`/data/tuxun_combined.parquet`**](https://huggingface.co/datasets/ShirohAO/tuxun/tree/main/data): This is the main dataset file containing combined competition history in Parquet format.
+* [**`tuxun_sample.csv`**](https://huggingface.co/datasets/ShirohAO/tuxun/blob/main/tuxun_sample.csv): An example CSV file to preview the structure of the data.
+* [**`selected_panoids`**](https://huggingface.co/datasets/ShirohAO/tuxun/blob/main/selected_panoids): The 500 panoids we used in our work. You can add a `.csv` or `.json` suffix to this file.
+* [**`download_panoramas.py`**](https://huggingface.co/datasets/ShirohAO/tuxun/blob/main/download_panoramas.py): A script to download street view images using the provided panoids.
+
+## Requirement
+
+The **GeoComp** is only for research.
+
+## Start
+
+### Data format of tuxun_combined.csv
+
+The `tuxun_combined.parquet` file contains data in a same structure to the original `tuxun_combined.csv`.
+
+**Example Schema:**
+
+| id   | data                | gmt_create      | timestamp |
+| ---- | ------------------- | --------------- | --------- |
+| Game | Json style metadata | 1734188074762.0 |           |
+
+**Explanation:**
+
+* We hide data items that may reveal personal privacy like changing the value of key "userId" to "User", "hostUserId" to "HostUser", "playerIds" to "Players", "id" to "Game".
+* The data under the "data" column is in JSON style. This column contains detailed geolocation information like "lat", "lng", "nation", and "panoId".
+
+### Extracting Specific Fields from the 'data' Column
+
+The 'data' column contains rich game-specific information in a JSON string format. To access individual fields like `guessPlace`, `targetPlace`, `score`, or `panoId`, you'll need to parse this JSON string.
+
+Here’s a Python example using `pandas` and `json` to extract these fields from the `tuxun_combined.parquet` file:
+
+```python
+import pandas as pd
+import json
+
+# Assuming your Parquet file is at 'data/tuxun_combined.parquet'
+# Adjust the file_path if necessary
+file_path = 'data/tuxun_combined.parquet'
+
+# Read the Parquet file into a DataFrame
+df = pd.read_parquet(file_path)
+
+# Define a function to parse the 'data' column and extract desired information
+def extract_game_details(data_json_str):
+    try:
+        # Parse the JSON string into a Python dictionary
+        game_data = json.loads(data_json_str)
+
+        # Initialize variables to None in case a field is missing
+        guess_place = None
+        target_place = None
+        score = None
+        pano_id = None
+
+        # Extract guessPlace, targetPlace, and score from 'player' -> 'lastRoundResult'
+        if 'player' in game_data and 'lastRoundResult' in game_data['player']:
+            last_round_result = game_data['player']['lastRoundResult']
+            guess_place = last_round_result.get('guessPlace')
+            target_place = last_round_result.get('targetPlace')
+            score = last_round_result.get('score')
+
+        # Extract panoId from the first element of the 'rounds' list
+        if 'rounds' in game_data and len(game_data['rounds']) > 0:
+            first_round = game_data['rounds'][0]
+            pano_id = first_round.get('panoId')
+
+        return guess_place, target_place, score, pano_id
+    except json.JSONDecodeError:
+        print(f"Error decoding JSON for row: {data_json_str[:100]}...") # Print first 100 chars for context
+        return None, None, None, None
+    except KeyError as e:
+        print(f"Missing key: {e} in row: {data_json_str[:100]}...") # Print first 100 chars for context
+        return None, None, None, None
+
+# Apply the function to the 'data' column and create new columns in the DataFrame
+df[['guessPlace', 'targetPlace', 'score', 'panoId']] = df['data'].apply(
+    lambda x: pd.Series(extract_game_details(x))
+)
+
+# Display the first few rows with the newly extracted columns
+print(df[['id', 'guessPlace', 'targetPlace', 'score', 'panoId']].head())
+```
+
+## Additional Information
+
+### Citation Information
+
+```bibtex
+@misc{song2025geolocationrealhumangameplay,
+      title={Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework}, 
+      author={Zirui Song and Jingpu Yang and Yuan Huang and Jonathan Tonglet and Zeyu Zhang and Tao Cheng and Meng Fang and Iryna Gurevych and Xiuying Chen},
+      year={2025},
+      eprint={2502.13759},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2502.13759}, 
+}
+```
+
+### Links
+
+[arXiv](https://arxiv.org/abs/2502.13759)
+
+[Hugging Face](https://huggingface.co/papers/2502.13759)
+
+[github](https://github.com/ziruisongbest/geocomp)

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geocomp/download_panoramas.py

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+# Download panorama images (0, 90, 180, 270 stitching) with duplicate detection and 404 error handling
+# Input: CSV file (e.g., selected_data.csv) containing panoid field
+# Output: Panorama images saved to save_dir
+import os
+from PIL import Image
+import requests
+import io
+import csv
+from tqdm import tqdm
+import dotenv
+import pandas as pd
+import re
+from time import sleep
+import random
+
+dotenv.load_dotenv("../.env")
+API_KEY = os.getenv('GOOGLE_MAP_API_KEY')
+base_url = 'https://maps.googleapis.com/maps/api/streetview'
+save_dir = '/home/xiuying.chen/jingpu/data/panoramas'
+csv_path = '/home/xiuying.chen/jingpu/data/exist_pano.csv'
+
+headings = {
+    'north': '0',
+    'east': '90',
+    'south': '180',
+    'west': '270',
+}
+
+def get_street_view_image(pano_id):
+    params = {
+        'size': '640x640',
+        'pano': pano_id,
+        'key': API_KEY,
+        'pitch': '0',
+        'return_error_code': 'true'
+    }
+
+    images = []
+    error_panoids = []
+
+    # Download images facing east, south, west, and north
+    for direction, heading in headings.items():
+        params['heading'] = heading
+        sleep(random.randint(1, 5))
+        response = requests.get(base_url, params=params)
+        print(f'{direction} request status:', response.status_code)
+        if response.status_code == 200:
+            images.append(Image.open(io.BytesIO(response.content)))
+            image_filename = f'{pano_id}_{direction}.jpg'
+            save_path = os.path.join(save_dir, image_filename)
+            with open(save_path, 'wb') as f:
+                f.write(response.content)
+            print(f'Saved {image_filename}')
+        elif response.status_code == 404:
+            if pano_id not in error_panoids:
+                error_panoids.append(pano_id)
+            print(f'Failed to get the {direction} image, panoid: {pano_id} does not exist')
+            
+    # If there are panoids causing a 404, write them to 404panoid.csv
+    if error_panoids:
+        error_df = pd.DataFrame(error_panoids, columns=['panoid'])
+        error_df.to_csv('404panoid.csv', mode='a', header=not os.path.exists('404panoid.csv'), index=False)
+
+    # Stitch images together to form a panorama
+    try:
+        # Calculate the dimensions of the final panorama image
+        width = sum(img.width for img in images)
+        height = images[0].height
+        
+        # Create a new image and stitch the downloaded images together
+        panorama = Image.new('RGB', (width, height))
+        x_offset = 0
+        for img in images:
+            panorama.paste(img, (x_offset, 0))
+            x_offset += img.width
+        
+        # Save the panorama image
+        output_path = os.path.join(save_dir, f'{pano_id}_panoramic.jpg')
+        panorama.save(output_path, quality=95)
+
+        # Update exist_pano.csv with the new panoid information
+        new_row = {'panoid': pano_id, 'address': save_dir}
+        existing_data = pd.read_csv(csv_path) if os.path.exists(csv_path) else pd.DataFrame(columns=['panoid', 'address'])
+        existing_data = existing_data._append(new_row, ignore_index=True)
+        existing_data.to_csv(csv_path, index=False)
+        return output_path
+        
+    except Exception as e:
+        print(f"Error downloading panorama: {str(e)}")
+        return None
+
+def check_panoramas_exist(panoid):
+    """Check if the given panoid has already been crawled"""
+    csv_path = '/home/xiuying.chen/jingpu/data/exist_pano.csv'
+    
+    # Read existing panoid data
+    if not os.path.exists(csv_path):
+        return False  # If the file does not exist, return False
+
+    existing_data = pd.read_csv(csv_path)
+    
+    # Check if the panoid exists in the 'panoid' column
+    return panoid in existing_data['panoid'].values
+
+def read_pano_ids(file_path):
+    pano_ids = []
+    with open(file_path, 'r', encoding='utf-8') as f:
+        reader = csv.DictReader(f)  # Use DictReader to read the CSV file
+        for row in reader:
+            pano_id = row['panoID']  
+            pano_ids.append((pano_id))  # Add the panoid to the list 
+    return pano_ids
+
+def load_exist_pano(scaned_dir):
+    """Read .jpg files in the specified directory and update exist_pano.csv"""
+    # Read existing panoid data
+    csv_path = '/home/xiuying.chen/jingpu/data/exist_pano.csv'
+    if os.path.exists(csv_path):
+        existing_data = pd.read_csv(csv_path)
+    else:
+        print("exist_pano.csv not found, quit")
+        return
+
+    # Iterate over all .jpg files in the specified directory
+    for filename in os.listdir(scaned_dir):
+        if filename.endswith('.jpg'):
+            # Use regex to extract the panoid by removing the directional suffix
+            match = re.match(r'^(.*?)(?:_east|_north|_south|_west|_panoramic)\.jpg$', filename)
+            if match:
+                panoid = match.group(1)  # Extract the panoid part
+
+                # Check whether the panoid already exists
+                if panoid not in existing_data['panoid'].values:
+                    # Create a new row and save the file address
+                    new_row = {'panoid': panoid, 'address': scaned_dir}
+                    existing_data = existing_data._append(new_row, ignore_index=True)
+
+    # Write the updated data back to the CSV file
+    existing_data.to_csv(csv_path, index=False)
+
+if __name__ == '__main__':
+    pano_ids = read_pano_ids("GT.csv")
+    for pano_id in tqdm(pano_ids, desc="Downloading street view images"):
+        # Check whether the image has already been downloaded
+        if check_panoramas_exist(pano_id):
+            # print(f'{pano_id} already exists')
+            continue
+        else:
+            # print(f'{pano_id} does not exist')
+            sleep(5)
+            get_street_view_image(pano_id)
+            print(f'{pano_id} downloaded')

geocomp/selected_panoids

@@ -0,0 +1,501 @@
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geocomp/tuxun_sample.csv

@@ -0,0 +1,2 @@
+id,data,gmt_create,timestamp
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