pawlo2013/EarthLoc2_FAISS

🌍 EarthLoc2 FAISS Index (DINOv2 + SALAD)

This is the FAISS index used in the Hugging Face Space:
🔗 EarthLoc2: Image Geolocalization

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📦 What’s Inside?

This index was constructed by encoding all of the 2021 Sentinel-2 satellite images from the EarthLoc dataset: 👉 EarthLoc_2021_Database

Each image is encoded four times (with rotations of 0°, 90°, 180°, and 270°), and descriptors were extracted using:

https://huggingface.co/pawlo2013/EarthLoc2

Also see the corresponding faiss_index_webdataset.csv file which is used to map each index to the correct local path or shard path from EarthLoc_2021_Database.

📊 Index Stats

Metric	Value
Total descriptors	~600,000
Descriptor dimension	3072
FAISS index size	~8GB
Image rotations	0°, 90°, 180°, 270°

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🧪 How to Use

You can download the trained model from https://huggingface.co/pawlo2013/EarthLoc2

🔧 1. Preprocess Query Image

import torchvision.transforms as tfm

image_transform = tfm.Compose([
    tfm.Resize((model.image_size, model.image_size), antialias=True),
    tfm.ToTensor(),
    tfm.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

image_tensor = image_transform(query_image)

with torch.no_grad():
    descriptor = model(image_tensor.unsqueeze(0))  # Shape: (1, 3072)
descriptor_np = descriptor.cpu().numpy(

distances, indices = faiss_index.search(descriptor_np, num_results)
flat_indices = indices.flatten()


import torch.nn.functional as F

for faiss_idx in flat_indices:
    image_index = faiss_idx % num_db_images
    best_rotation_index = faiss_idx // num_db_images

    # Correct query image rotation (optional)
    query_tensor = F.rotate(query_tensor, [0, -90, -180, -270][best_rotation_index])