The dataset viewer is not available because its heuristics could not detect any supported data files. You can try uploading some data files, or configuring the data files location manually.

Archaeological Site Dataset (CAA UK 2025)

Dataset Summary

This dataset provides a comprehensive multi-channel remote sensing dataset for training machine learning models to detect archaeological sites. The dataset combines Sentinel-2 satellite imagery, FABDEM elevation data, and derived spectral indices to create 11-channel representations of 1×1 km grid cells at 10m resolution.

Key Features:

Multi-modal data: 6 spectral bands + 3 spectral indices + 2 terrain features
Balanced dataset: Positives, integrated negatives, landcover negatives, and unlabeled samples
Extensive augmentation: Geometric (rotation) and radiometric augmentations
High resolution: 100×100 pixels per grid cell (10m/pixel)
Geographic context: Integrated negatives from same regions as archaeological sites

Dataset Structure

Data Instances

Each sample consists of:

11 channels stored as separate .npy files (float32, 100×100 pixels each)
Binary label: 1 (archaeological site), 0 (non-site), or -1 (unlabeled)
Metadata: Geographic coordinates, rotation angle, augmentation type, site information

Example directory structure:

grid_000001_rot000/
├── channels/
│   ├── B2.npy          # Sentinel-2 Blue
│   ├── B3.npy          # Sentinel-2 Green
│   ├── B4.npy          # Sentinel-2 Red
│   ├── B8.npy          # Sentinel-2 NIR
│   ├── B11.npy         # Sentinel-2 SWIR1
│   ├── B12.npy         # Sentinel-2 SWIR2
│   ├── NDVI.npy        # Normalized Difference Vegetation Index
│   ├── NDWI.npy        # Normalized Difference Water Index
│   ├── BSI.npy         # Bare Soil Index
│   ├── DEM.npy         # Elevation (FABDEM)
│   └── Slope.npy       # Terrain slope
├── labels/
│   ├── binary_label.npy
│   ├── pos_type.txt
│   └── neg_type.txt
└── info.json

Data Fields

Channel Schema (11 channels per grid)

Index	Channel	Source	Resolution	Wavelength/Description
0	B2	Sentinel-2	10m	Blue (490nm)
1	B3	Sentinel-2	10m	Green (560nm)
2	B4	Sentinel-2	10m	Red (665nm)
3	B8	Sentinel-2	10m	NIR (842nm)
4	B11	Sentinel-2	20m→10m	SWIR1 (1610nm)
5	B12	Sentinel-2	20m→10m	SWIR2 (2190nm)
6	NDVI	Calculated	10m	(B8-B4)/(B8+B4) - Vegetation
7	NDWI	Calculated	10m	(B3-B8)/(B3+B8) - Water
8	BSI	Calculated	10m	Bare Soil Index
9	DEM	FABDEM	30m→10m	Elevation (meters)
10	Slope	Derived	10m	Terrain slope (degrees)

Metadata Fields (`grid_metadata.parquet`)

Column	Type	Description
`grid_id`	string	Unique grid identifier (e.g., "grid_000001_rot000")
`centroid_lon`	float	Grid center longitude (WGS84)
`centroid_lat`	float	Grid center latitude (WGS84)
`label`	int	1 = site, 0 = non-site, -1 = unlabeled
`label_source`	string	Data source origin
`image_path`	string	Path to grid directory

Data Splits

CRITICAL: Prevent Data Leakage

Do NOT split randomly! Rotations and augmentations of the same site must stay in the same split.

Recommended approach:

Group samples by original site index (extracted from grid_id)
Split sites (not samples) into train/val/test
All rotations/augmentations of a site go to the same split

Suggested ratios:

Train: 70% of sites
Validation: 15% of sites
Test: 15% of sites

Dataset Composition

Sample Types

Given N known sites and rotation step of 120° (R=3 rotations):

Data Type	Count	Label	Description
Positives (base)	3×N	1	Original + 2 rotations per site
Positives (augmented)	9×N	1	3 radiometric variants × 3 rotations
Total Positives	12×N	1	All positive samples
Integrated Negatives (base)	3×N	0	From same areas as sites
Integrated Negatives (aug)	9×N	0	3 variants × 3 rotations
Total Integrated Neg.	12×N	0	Surrounding landscape context
Landcover Negatives	3×N	0	Urban/water/cropland
Unlabeled	~1.5×N	-1	Background samples
TOTAL	~28.5×N		Complete dataset

Data Augmentation

1. Geometric Augmentation (Rotation)

3 rotations per site: 0°, 120°, 240°
Extracted at 1.5× size, rotated, then center-cropped
Applied to positives and integrated negatives

2. Radiometric Augmentation Three variants per rotated sample:

aug1: +8% brightness, +5% contrast, noise σ=0.015
aug2: -8% brightness, -5% contrast, noise σ=0.015
aug3: No brightness/contrast, noise σ=0.025

Dataset Creation

Source Data

Satellite Imagery:

Sentinel-2: Multi-spectral optical imagery (2023-2024)
FABDEM: Forest And Buildings removed Copernicus DEM

Archaeological Sites:

Known archaeological site locations (latitude/longitude)
Site types may include geoglyphs, mounds, settlements, etc.

Negative Samples:

Integrated negatives: 4 corners of rotated grids (same geographic areas)
Landcover negatives: Urban (40%), water (30%), cropland (30%)
Unlabeled: Random background samples with exclusion buffer

Data Collection Pipeline

Known site extraction: Multi-channel data centered on archaeological sites
Rotation generation: Geometric augmentation (0°, 120°, 240°)
Integrated negatives: Corner sampling from same regions
Landcover negatives: Sampling from urban/water/crop areas
Unlabeled sampling: Random background with site exclusion
Radiometric augmentation: Brightness/contrast/noise variations

Usage

Loading the Dataset

import numpy as np
import pandas as pd
from pathlib import Path

# Load metadata
metadata = pd.read_parquet('grid_metadata.parquet')

# Load a single sample
def load_sample(grid_path):
    channels = {}
    channel_names = ['B2', 'B3', 'B4', 'B8', 'B11', 'B12', 
                     'NDVI', 'NDWI', 'BSI', 'DEM', 'Slope']
    
    for ch in channel_names:
        channels[ch] = np.load(f'{grid_path}/channels/{ch}.npy')
    
    # Stack into (11, 100, 100) tensor
    data = np.stack([channels[ch] for ch in channel_names], axis=0)
    
    # Load label
    label = np.load(f'{grid_path}/labels/binary_label.npy')
    
    return data, label

# Example
sample_data, sample_label = load_sample('grid_images/grid_000001_rot000')
print(f"Data shape: {sample_data.shape}")  # (11, 100, 100)
print(f"Label: {sample_label}")  # [1] or [0] or [-1]

PyTorch DataLoader with Proper Splitting

import torch
from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler
import pandas as pd
import numpy as np

class ArchaeologicalDataset(Dataset):
    def __init__(self, metadata_df, base_path):
        self.metadata = metadata_df
        self.base_path = base_path
        
    def __len__(self):
        return len(self.metadata)
    
    def __getitem__(self, idx):
        row = self.metadata.iloc[idx]
        grid_path = f"{self.base_path}/{row['image_path']}"
        
        # Load channels
        channel_names = ['B2', 'B3', 'B4', 'B8', 'B11', 'B12', 
                        'NDVI', 'NDWI', 'BSI', 'DEM', 'Slope']
        channels = [np.load(f'{grid_path}/channels/{ch}.npy') 
                   for ch in channel_names]
        data = torch.FloatTensor(np.stack(channels, axis=0))
        
        # Load label
        label = torch.FloatTensor(np.load(f'{grid_path}/labels/binary_label.npy'))
        
        return data, label, row['grid_id']

# Load metadata and create splits
df = pd.read_parquet('grid_metadata.parquet')

# Extract site index (CRITICAL: group by original site!)
df['site_index'] = df['grid_id'].str.extract(r'(grid|ineg)_(\d+)')[1]

# Split by sites, not samples
unique_sites = df[df['grid_id'].str.startswith('grid_')]['site_index'].unique()
np.random.seed(42)
np.random.shuffle(unique_sites)

n_train = int(0.7 * len(unique_sites))
n_val = int(0.15 * len(unique_sites))

train_sites = unique_sites[:n_train]
val_sites = unique_sites[n_train:n_train+n_val]
test_sites = unique_sites[n_train+n_val:]

# Assign splits
df['split'] = 'test'
df.loc[df['site_index'].isin(train_sites), 'split'] = 'train'
df.loc[df['site_index'].isin(val_sites), 'split'] = 'val'

# Create datasets
train_dataset = ArchaeologicalDataset(
    df[df['split'] == 'train'], 
    base_path='grid_images'
)

# Create balanced sampler for training
train_df = df[df['split'] == 'train']
weights = torch.zeros(len(train_df))
weights[train_df['label'] == 1] = 0.50 / (train_df['label'] == 1).sum()
weights[train_df['label'] == 0] = 0.40 / (train_df['label'] == 0).sum()
weights[train_df['label'] == -1] = 0.10 / (train_df['label'] == -1).sum()

sampler = WeightedRandomSampler(weights, len(train_df), replacement=True)
train_loader = DataLoader(train_dataset, batch_size=32, sampler=sampler)

Train/Val/Test Split Guidelines

CRITICAL: Prevent Data Leakage

NEVER split randomly! Rotations and augmentations of the same site must stay together.

Step-by-Step Guide

import pandas as pd
import numpy as np

# Load metadata
df = pd.read_parquet('grid_metadata.parquet')

# Extract base site index from grid_id
# Examples:
#   grid_000001_rot000_aug1 -> 000001
#   ineg_000045_rot120 -> 000045
df['site_index'] = df['grid_id'].str.extract(r'(grid|ineg)_(\d+)')[1]

# Get unique sites (positives only for stratification)
unique_sites = df[df['grid_id'].str.startswith('grid_')]['site_index'].unique()

# Shuffle and split SITES (not samples!)
np.random.seed(42)
np.random.shuffle(unique_sites)

n_train = int(0.7 * len(unique_sites))
n_val = int(0.15 * len(unique_sites))

train_sites = unique_sites[:n_train]
val_sites = unique_sites[n_train:n_train+n_val]
test_sites = unique_sites[n_train+n_val:]

# Assign splits based on site membership
df['split'] = 'test'
df.loc[df['site_index'].isin(train_sites), 'split'] = 'train'
df.loc[df['site_index'].isin(val_sites), 'split'] = 'val'

# Distribute landcover negatives and unlabeled randomly
mask = df['grid_id'].str.startswith(('lneg_', 'unla_'))
df.loc[mask, 'split'] = np.random.choice(
    ['train', 'val', 'test'],
    size=mask.sum(),
    p=[0.7, 0.15, 0.15]
)

# Verify no leakage
train_sites_set = set(df[df['split'] == 'train']['site_index'])
val_sites_set = set(df[df['split'] == 'val']['site_index'])
test_sites_set = set(df[df['split'] == 'test']['site_index'])

assert len(train_sites_set & val_sites_set) == 0, "Train-Val leakage!"
assert len(train_sites_set & test_sites_set) == 0, "Train-Test leakage!"
assert len(val_sites_set & test_sites_set) == 0, "Val-Test leakage!"

print(f"Train: {len(df[df['split']=='train'])} samples from {len(train_sites)} sites")
print(f"Val: {len(df[df['split']=='val'])} samples from {len(val_sites)} sites")
print(f"Test: {len(df[df['split']=='test'])} samples from {len(test_sites)} sites")

Considerations for Use

Data Characteristics

Integrated Negatives: Sampled from corners of the same geographic areas as positives (after rotation), representing surrounding landscape context. Sharp boundaries between corners with no blending.
Unlabeled Data (label = -1): Random background samples with exclusion buffer around known sites. May contain undiscovered archaeological sites. Suitable for semi-supervised learning or active learning scenarios.
Landcover Negatives: Explicitly sampled from urban areas (40%), water bodies (30%), and cropland (30%) to ensure the model learns to reject obvious non-archaeological features.
Cloud Cover: Maximum 20% cloud cover per image.

Citation

If you use this dataset, please cite:

@inproceedings{li2025fusing,
  title={{Fusing Text and Terrain}: {An LLM}-Powered Pipeline for Preparing Archaeological Datasets from Literature and Remote Sensing Imagery},
  author={Li, Linduo and Wu, Yifan and Wang, Zifeng},
  booktitle={{CAA UK 2025}: Computer Applications and Quantitative Methods in Archaeology},
  year={2025},
  month={December},
  address={University of Cambridge, UK},
  organization={CAA UK},
  note={Conference held 9--10 December 2025}
}

Presentation Links:

License

This dataset is released under the MIT License.

Acknowledgments

Sentinel-2 satellite imagery was provided by the European Space Agency (ESA) through the Copernicus Programme.
FABDEM elevation data were provided by the University of Bristol.
Google Earth Engine was used as the primary data processing and analysis platform.
Geoglyph location data were derived from publicly available archaeological compilations curated by James Q. Jacobs (2025), JQ Jacobs Archaeology, last modified July 31, 2025: https://jqjacobs.net/archaeology/geoglyph.html

Contact: linduo.li@ip-paris.fr

Last Updated: December 2025

Downloads last month: 4