README.md

🌍 TerraVision

Sharper Earth. Brighter Decisions.

TerraVision is a GIS-based satellite imagery analysis platform that uses Sentinel-2 satellite data and the official ESA OpenSR LDSR-S2 + SEN2SR super-resolution pipeline to generate model-based 2.5 m resolution imagery from freely available Sentinel-2 data.

The platform combines satellite image super-resolution, geospatial processing, spectral analysis, visualization, and GeoTIFF export into a single web-based workflow.

---

🚀 Overview

Sentinel-2 provides freely available multispectral imagery, but its spatial resolution can be limiting for detailed geospatial analysis.

TerraVision addresses this challenge by:

1. Selecting a location using latitude/longitude or an interactive map.
2. Selecting a date range for Sentinel-2 imagery.
3. Retrieving Sentinel-2 L2A data.
4. Applying the ESA LDSR-S2 + SEN2SR super-resolution pipeline.
5. Generating a model-based 2.5 m resolution, 10-band output.
6. Producing multiple GIS analysis layers.
7. Providing an original-vs-super-resolved comparison.
8. Exporting georeferenced GeoTIFF files and a ZIP containing all outputs.

Important: The 2.5 m imagery generated by TerraVision is a model-based reconstruction. It is not native 2.5 m satellite observation.

---

✨ Key Features

🛰️ Sentinel-2 Data

• Sentinel-2 L2A imagery
• 10 spectral bands
• User-defined geographical location
• User-defined date range

🔬 AI-Based Super Resolution

• ESA OpenSR LDSR-S2 + SEN2SR
• 10 m input
• Model-generated 2.5 m output
• 4× spatial upscaling
• 10-band super-resolved output

🗺️ GIS Visualization

TerraVision generates:

• Super-Resolved RGB
• Original Sentinel-2 RGB
• False Color / Infrared
• SWIR Composite
• NDVI
• NDWI
• NDBI
• NBR
• LDSR-S2 Uncertainty

📊 Image Comparison

An interactive before/after comparison allows users to visually compare:

Original Sentinel-2 → Super-Resolved Output

💾 Geospatial Export

Users can download:

• Individual GeoTIFF layers
• Complete ZIP package containing all generated layers

All exported layers preserve their geospatial reference information.

---

🧠 Super-Resolution Pipeline

TerraVision uses the official ESA OpenSR approach combining:

Sentinel-2 L2A
      │
      ▼
Data Retrieval
      │
      ▼
10-band Sentinel-2 Input
      │
      ▼
LDSR-S2 + SEN2SR
      │
      ▼
2.5 m Super-Resolved Output
      │
      ├── B02
      ├── B03
      ├── B04
      ├── B05
      ├── B06
      ├── B07
      ├── B08
      ├── B8A
      ├── B11
      └── B12
      │
      ▼
GIS Analysis Layers

The pipeline combines the strengths of:

LDSR-S2

Latent diffusion super-resolution for Sentinel-2 RGB-NIR information.

SEN2SR

Super-resolution processing for the additional Sentinel-2 spectral bands.

Together, they provide the 10-band super-resolved output used by TerraVision.

---

🛰️ Sentinel-2 Bands

Band	Description	Resolution
B02	Blue	10 m
B03	Green	10 m
B04	Red	10 m
B05	Vegetation Red Edge	20 m
B06	Vegetation Red Edge	20 m
B07	Vegetation Red Edge	20 m
B08	NIR	10 m
B8A	Narrow NIR	20 m
B11	SWIR 1	20 m
B12	SWIR 2	20 m

The super-resolution pipeline produces these bands at the model-generated 2.5 m output scale.

---

📊 Spectral Analysis

TerraVision converts the super-resolved bands into several commonly used remote-sensing indices.

🌱 NDVI

Normalized Difference Vegetation Index

NDVI = (B08 - B04) / (B08 + B04)

Used to analyze vegetation density and vegetation condition.

---

💧 NDWI

TerraVision uses the Green-NIR formulation:

NDWI = (B03 - B08) / (B03 + B08)

Used to highlight water-related features.

---

🏙️ NDBI

Normalized Difference Built-up Index

NDBI = (B11 - B08) / (B11 + B08)

Used for identifying built-up and urban areas.

---

🔥 NBR

Normalized Burn Ratio

NBR = (B08 - B12) / (B08 + B12)

Useful for analyzing burn-affected areas and vegetation disturbance.

---

🎨 Visualization Layers

TerraVision provides several visualization products.

RGB Composite

R = B04
G = B03
B = B02

Provides a natural-color representation.

False Color Composite

R = B08
G = B04
B = B03

Useful for vegetation analysis.

SWIR Composite

R = B12
G = B11
B = B04

Useful for analyzing moisture, soil, built-up areas and burned regions.

---

📈 Uncertainty Map

TerraVision also exposes the uncertainty estimation available from the underlying LDSR-S2 RGB-NIR component.

The uncertainty map highlights areas where the super-resolution model has greater variation across generated samples.

Dark  → Lower estimated uncertainty
Bright → Higher estimated uncertainty

Important limitation

This should not be interpreted as a calibrated confidence score for the complete 10-band output.

The current implementation calculates uncertainty from the underlying 4-band LDSR-S2 RGB-NIR model.

---

🗺️ TerraVision Workflow

User
 │
 ├── Select Location
 │       └── Latitude / Longitude
 │
 ├── Select Date Range
 │
 ▼
Sentinel-2 L2A Data
 │
 ▼
Preprocessing
 │
 ▼
ESA LDSR-S2 + SEN2SR
 │
 ▼
2.5 m Super-Resolved Imagery
 │
 ├── RGB
 ├── False Color
 ├── SWIR
 ├── NDVI
 ├── NDWI
 ├── NDBI
 ├── NBR
 └── Uncertainty
 │
 ▼
Interactive Visualization
 │
 ▼
GeoTIFF / ZIP Export

---

🛠️ Technology Stack

Frontend / UI

• Python
• Gradio
• Interactive visualization
• Image comparison slider
• GIS map integration

Machine Learning

• PyTorch
• ESA OpenSR
• LDSR-S2
• SEN2SR

Geospatial Processing

• Rasterio
• Rioxarray
• GeoPandas
• PyProj
• Xarray
• Dask
• Cubo

Satellite Data

• Sentinel-2 L2A
• STAC-based data access

Model / Data Management

• MLSTAC
• Hugging Face ecosystem

Deployment

• Hugging Face Spaces
• Gradio
• ZeroGPU-compatible architecture

---

📦 Installation

Clone the repository:

git clone https://github.com/prateeksharmacoder-sys/satellite-LDRS-SEN2SR.git
cd TerraVision

Create a virtual environment:

python -m venv venv

Activate it.

Windows

venv\Scripts\activate

Linux / macOS

source venv/bin/activate

Install dependencies:

pip install -r requirements.txt

---

▶️ Running the Application

Run:

python app.py

The Gradio interface will provide a local web address.

For development, the application can also be launched with:

demo.launch()

---

📁 Project Structure

TerraVision/
│
├── app.py
├── requirements.txt
├── README.md
│
├── models/
│   └── model configuration / model assets
│
├── utils/
│   ├── preprocessing.py
│   ├── visualization.py
│   ├── geotiff.py
│   └── analysis.py
│
├── outputs/
│   └── generated products
│
└── assets/
    └── UI images / project assets

The exact structure may vary depending on the final deployment version.

---

🔬 Technical Implementation

1. Data Retrieval

TerraVision retrieves Sentinel-2 L2A imagery using a STAC-based workflow.

The application requests:

B02
B03
B04
B05
B06
B07
B08
B8A
B11
B12

---

2. Preprocessing

Sentinel-2 reflectance values are converted into normalized floating-point values before inference.

low_resolution = low_resolution / 10000

---

3. Super Resolution

The combined model is loaded and executed using SEN2SR:

super_resolution = sen2sr.predict_large(
    model=model,
    X=low_resolution,
    overlap=16
)

The model converts:

10 × 128 × 128

into approximately:

10 × 512 × 512

---

4. Spectral Products

The generated bands are used to calculate spectral indices such as:

NDVI = (B08 - B04) / (B08 + B04)
NDWI = (B03 - B08) / (B03 + B08)
NDBI = (B11 - B08) / (B11 + B08)
NBR  = (B08 - B12) / (B08 + B12)

---

5. Geospatial Export

Generated products are exported as GeoTIFF files while preserving:

• CRS
• Spatial transform
• Resolution
• Geographic bounds

The prototype output was verified at:

Resolution: 2.5 m
CRS: EPSG:32630
Output size: 512 × 512

The exact CRS changes according to the selected geographic location.

---

📥 Output Files

A typical output package contains:

terravision_layers.zip
│
├── sr_rgb.tif
├── false_color.tif
├── swir.tif
├── ndvi.tif
├── ndwi.tif
├── ndbi.tif
├── nbr.tif
└── uncertainty.tif

---

⚡ Performance

The current prototype has been successfully tested end-to-end with:

Sentinel-2 input:       10 × 128 × 128
Super-resolution:       10 × 512 × 512
Output resolution:      2.5 m
Analysis layers:        8
GeoTIFF export:         Successful
ZIP generation:         Successful

The super-resolution inference is the computationally expensive part of the pipeline.

Uncertainty estimation requires additional inference and is therefore treated as an optional computational component for deployment optimization.

---

⚠️ Limitations

TerraVision currently has several limitations.

1. Model-Based Resolution

The 2.5 m output is reconstructed by an AI model and is not equivalent to native 2.5 m satellite imagery.

2. Reconstruction Errors

Super-resolution models can introduce artifacts or reconstruct details that are not directly observed in the original imagery.

3. Cloud and Atmospheric Effects

Clouds, haze and poor-quality Sentinel-2 observations can affect the output.

4. Scientific Validation

The current prototype has been technically validated for:

• successful model inference
• correct output dimensions
• spectral layer generation
• GeoTIFF generation
• geospatial referencing

A comprehensive validation against independent high-resolution reference imagery using metrics such as PSNR, SSIM and spectral consistency remains future work.

5. Uncertainty

The current uncertainty output represents the LDSR-S2 RGB-NIR component rather than a calibrated uncertainty estimate for all 10 bands.

6. Temporal Analysis

Change detection such as dNBR requires pre-event and post-event imagery and is not part of the current single-image workflow.

---

🔮 Future Scope

Potential future improvements include:

• Multi-date satellite analysis
• Automated change detection
• dNDVI / dNBR analysis
• Cloud masking
• Larger-area processing
• Batch processing
• More advanced GIS layers
• Scientific validation using higher-resolution reference imagery
• Performance optimization
• GPU acceleration
• Caching of satellite data
• Production-scale deployment
• User accounts and project history
• Export to additional GIS formats

---

🌐 Deployment

TerraVision is designed to be deployed as a Gradio application.

The planned deployment architecture is:

User
  │
  ▼
TerraVision Web Interface
  │
  ▼
Gradio Application
  │
  ├── Satellite Data Retrieval
  │
  ├── GPU Inference
  │
  ├── Spectral Analysis
  │
  └── GeoTIFF Generation
  │
  ▼
Results + Downloads

The application can be adapted for GPU-backed deployment using Hugging Face Spaces ZeroGPU.

---

🧪 Validation Status

Component	Status
Sentinel-2 data retrieval	✅ Tested
10-band input generation	✅ Tested
LDSR-S2 + SEN2SR inference	✅ Tested
2.5 m output generation	✅ Tested
RGB generation	✅ Tested
False Color	✅ Tested
SWIR	✅ Tested
NDVI	✅ Tested
NDWI	✅ Tested
NDBI	✅ Tested
NBR	✅ Tested
LDSR-S2 uncertainty	✅ Tested
GeoTIFF export	✅ Tested
Georeferencing	✅ Verified
ZIP export	✅ Tested
Interactive web UI	✅ Prototype
Scientific benchmark validation	🔄 Future work
Production-scale deployment	🔄 Planned

---

🎯 Use Cases

TerraVision can support exploratory analysis in areas such as:

• 🌱 Vegetation monitoring
• 💧 Water-body analysis
• 🏙️ Urban expansion studies
• 🔥 Burn-area analysis
• 🌾 Agricultural monitoring
• 🛰️ Remote-sensing research
• 🗺️ GIS analysis
• 🌍 Environmental monitoring

---

📚 Acknowledgements

TerraVision builds upon open-source satellite super-resolution research and software from the European Space Agency (ESA) OpenSR project, including LDSR-S2 and SEN2SR.

The project also uses open geospatial and satellite-data technologies including:

• Sentinel-2
• Cubo
• MLSTAC
• PyTorch
• Rasterio
• Xarray
• Gradio

---

📜 Disclaimer

TerraVision is a research and demonstration project.

The generated 2.5 m imagery should not automatically be treated as equivalent to native high-resolution satellite imagery. Results may contain model reconstruction artifacts and should be independently validated before being used for critical scientific, commercial, legal, or operational decisions.

---

👨‍💻 Project

TerraVision

Sharper Earth. Brighter Decisions.

Built as a geospatial AI project for exploring the potential of satellite-image super-resolution and GIS analysis.

---

⭐ If you find TerraVision interesting

Consider giving the repository a ⭐ and exploring the implementation.

### Recommended GitHub repository files

For your current project, I would keep the repository clean like this:

```text
TerraVision/
│
├── README.md              ← this file
├── app.py                 ← main Gradio application
├── requirements.txt
├── .gitignore
│
├── utils/
│   ├── satellite.py
│   ├── analysis.py
│   ├── visualization.py
│   └── geotiff.py
│
└── assets/
    └── screenshots/

One important point: don't upload the ~1.4 GB model file directly into GitHub. Your code can download/load the model from its Hugging Face model repository at runtime.

If you want, I can also create the actual README.md file for you as a downloadable file, ready to put directly into your GitHub repository.