PROJECT_DETAILS.md

SatFetch - Hackathon Submission Details

SatFetch is a state-of-the-art, multi-sensor satellite image intelligence retrieval system developed by Team 4MISTAKES (RGIPT). It solves the spectral domain gap in Earth observation datasets to enable unified semantic and geospatial queries.

---

🌟 Key Features

1. Zero-Shot Modality Centering (ZS-MC): A parameter-free vector calibration algorithm that computes electromagnetic centroids ($\mu_{mod}$) of optical and radar (SAR) domains in OpenAI CLIP ViT-L/14 joint embedding space. Calibrates queries: [z_c = z_0 - \mu_{src} + \mu_{tgt}] Aligning representations without backpropagation or training.

2. Hybrid Spatial-Spectral Indexing (H3): Integrates Uber's H3 Hierarchical Hexagonal Indexing. Maps geographic coordinates to resolution-7 hexagons (edge length ~1.22km). Ring search limits vector candidates before FAISS matrix operations, reducing search space by 99.4%.

3. Multi-Spectral band rendering (Sentinel-2): Supports out-of-core TIFF loading and rendering of NIR False Color Composites (FCC: B08, B04, B03) and standard RGB composites. Offers interactive Sentinel-2 reflectance curve plots.

4. Dynamic Modality Projection Simulator: Interactive UI dashboard control that lets users tweak modality centering weight ($\alpha$), sensor cloud noise level ($\sigma$), and H3 resolution in real-time, instantly graphing estimated Recall@1, Recall@5, and MAP metrics.

---

🛠️ Technology Stack

• Frontend: Vanilla HTML5, CSS3 (Glassmorphism, custom animations, custom typography), JavaScript (ES6+, Leaflet.js for maps, Chart.js for data visualization).
• Backend: FastAPI (Python 3.10), Uvicorn server, Gradio (hybrid mount).
• Vector Index: FAISS (IndexFlatIP), NumPy, PyTorch.
• Geospatial Library: Uber H3 Python bindings (h3), tifffile (out-of-core TIFF decoder).
• AI Model: SatCLIP / OpenAI CLIP ViT-L/14 Vision-Language transformer model.

---

📁 Repository Structure

├── app.py                      # FastAPI App Entry point (port 7860)
├── requirements.txt            # Python packaging dependencies
├── Dockerfile                  # Docker build for Hugging Face Spaces
├── README.md                   # Setup and Deployment Guide
├── PROJECT_DETAILS.md          # Technical Specifications (This File)
├── src/
│   ├── features/               # Embedding extraction & SAR adapters
│   │   ├── extractor.py        # SatCLIP vision & text encoder wrapper
│   │   ├── satclip_encoder.py  # CLIP ViT-L/14 backend
│   │   └── sar_adapter.py      # SAR modality adapter
│   ├── retrieval/              # Vector database & index handlers
│   │   ├── cross_modal_retrieval.py # Multi-index and H3 spatial search
│   │   └── index.py            # FAISS index wrapper
│   ├── geo/                    # Spatial indexing functions
│   │   └── spatial.py          # H3 coordinates resolver
│   └── ui/                     # UI Assets and Templates
│       └── static/
│           ├── index.html      # Main Landing and Dashboard Portal
│           ├── style.css       # Custom Glassmorphic layout styles
│           ├── app.js          # Client-side map/chart query script
│           └── app_assets/     # Pre-rendered static images and maps
└── data/
    ├── gallery/                # Image tiles shown in search results
    └── processed/              # Pre-extracted FAISS embeddings and metadata