YTxFSGAMERz/ARGUS_DATASET

ARGUS_DATASET/
├── README.md                           # Standard Hugging Face Dataset Card & Documentation
├── .gitattributes                      # Git LFS configuration for binary payloads
│
├── ARGUS_DATASET/
│   ├── images/                         # 74,000+ Landmark visual assets categorized by ISO country
│   │   └── landmarks/
│   │       ├── AFG/                    # Afghanistan
│   │       ├── FRA/                    # France (e.g. Eiffel Tower, Louvre, Versailles)
│   │       ├── JPN/                    # Japan (e.g. Tokyo Tower, Fushimi Inari)
│   │       ├── USA/                    # United States (e.g. Statue of Liberty, Golden Gate)
│   │       └── ... [162 countries]
│   │
│   ├── places/
│   │   ├── places.csv                  # Master catalog of 3,806 landmarks with coordinates & IDs
│   │   ├── places.jsonl                # Line-delimited JSON representation of places
│   │   └── coverage/
│   │       ├── category_coverage.json  # Category-wise completeness and breakdown
│   │       └── country_coverage.json   # Country-wise landmark density distribution
│   │
│   ├── metadata/
│   │   ├── images.csv                  # Complete asset index: paths, dimensions, hashes, licensing
│   │   ├── images.jsonl                # Streamable JSONL image metadata records
│   │   ├── licenses.csv                # Legal taxonomy and redistribution rights mapping
│   │   └── sources.csv                 # Ingestion source endpoint provenance
│   │
│   ├── indexes/
│   │   ├── state_tracker.db            # Production SQLite database with spatial B-tree indexes
│   │   └── phash_bktree.index          # Serialized Burkhard-Keller tree for 64-bit DCT pHash lookup
│   │
│   └── reports/
│       ├── final_report.md             # Comprehensive audit, deduplication, and coverage report
│       └── license_audit.json          # Programmatic license verification log
│
├── data/
│   └── streetscapes/                   # NUS Global Streetscapes SVI dataset (10,000 observation frames)
│       ├── coords.csv                  # Geographic coordinates & headings
│       └── images/                     # 360-degree streetscape observation images
│
├── geodata/                            # 516 GeoJSON layers
│   ├── CAMERAS_WITH_NETWORK_DATA.geojson  # 178,674 surveillance camera nodes
│   ├── camera_networks.json               # Operator network cluster topologies
│   ├── police_precincts_usa.geojson       # US law enforcement precinct polygons
│   └── ...
│
└── geosent_chroma_db/                  # ChromaDB vector embedding index for semantic geosearch

import pandas as pd

# Load places catalog
places_url = "https://huggingface.co/datasets/YTxFSGAMERz/ARGUS_DATASET/raw/main/ARGUS_DATASET/places/places.csv"
df_places = pd.read_csv(places_url)
print(f"Loaded {len(df_places)} global landmarks.")
print(df_places[["name", "country", "city", "category", "latitude", "longitude"]].head())

# Load image metadata
images_url = "https://huggingface.co/datasets/YTxFSGAMERz/ARGUS_DATASET/raw/main/ARGUS_DATASET/metadata/images.csv"
df_images = pd.read_csv(images_url)
print(f"Indexed {len(df_images)} images across {df_images['place_id'].nunique()} landmarks.")

from huggingface_hub import hf_hub_download
from PIL import Image

# Download a specific landmark image
image_path = hf_hub_download(
    repo_id="YTxFSGAMERz/ARGUS_DATASET",
    repo_type="dataset",
    filename="ARGUS_DATASET/images/landmarks/FRA/argus-img-e41f57ff-ce30-562d-a174-3fc1145641fc.jpg"
)

img = Image.open(image_path)
print(f"Image Resolution: {img.size}")

import sqlite3
from huggingface_hub import hf_hub_download

db_path = hf_hub_download(
    repo_id="YTxFSGAMERz/ARGUS_DATASET",
    repo_type="dataset",
    filename="ARGUS_DATASET/indexes/state_tracker.db"
)

conn = sqlite3.connect(db_path)
cursor = conn.cursor()

# Query high-density landmark clusters in Italy
cursor.execute("""
    SELECT name, city, category, latitude, longitude 
    FROM places 
    WHERE country = 'ITA' 
    LIMIT 5
""")
for row in cursor.fetchall():
    print(row)

import pickle
from huggingface_hub import hf_hub_download

# Download pre-built Burkhard-Keller tree index
bktree_path = hf_hub_download(
    repo_id="YTxFSGAMERz/ARGUS_DATASET",
    repo_type="dataset",
    filename="ARGUS_DATASET/indexes/phash_bktree.index"
)

with open(bktree_path, "rb") as f:
    bktree = pickle.load(f)

# Query nearest visual matches within Hamming distance <= 10
# query_phash is an integer representation of a 64-bit DCT pHash
# results = bktree.query(query_phash, max_distance=10)

@dataset{argus_dataset_2026,
  author       = {YTxFSGAMERz},
  title        = {ARGUS DATASET: Multi-Domain Global Landmark, Streetscape, and Geospatial Intelligence Imagery},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/YTxFSGAMERz/ARGUS_DATASET}}
}