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# RockGlacier_Project
## Project Overview
This project focuses on the automated detection and segmentation of rock glaciers using deep learning techniques. By leveraging multi-modal remote sensing data—including Sentinel-2 optical imagery, Digital Elevation Models (DEM), Land Surface Temperature (LST), and surface velocity data—we aim to accurately map rock glaciers to better understand their distribution, dynamics, and response to climate change.
## Repository Structure
- `raw_data/`: Contains the original, unprocessed data files from various sources.
  - `shapefiles/`: Rock glacier polygons in vector format (e.g., .shp).
  - `sentinel2/`: Optical satellite imagery from Sentinel-2.
  - `dem/`: Digital Elevation Model data.
  - `lst/`: Land Surface Temperature data.
  - `velocity/`: Surface velocity maps.
- `features/`: Stores intermediate extracted features derived from the raw data sources.
- `masks/`: Contains binary masks generated from the shapefiles, where `0` indicates background and `1` indicates a rock glacier.
- `stacks/`: Holds stacked feature arrays combining multiple data modalities (Sentinel-2, DEM, LST, velocity) into single multi-band files for model input.
- `dataset/`: Contains the final extracted patches organized for model training, validation, and testing.
  - `train/`: Data used to train the model, containing `images/` and corresponding `masks/`.
  - `val/`: Data used to validate the model during training and tune hyperparameters.
  - `test/`: Unseen data used to evaluate the final model's generalization performance.
- `models/`: Contains the PyTorch/TensorFlow implementations of various deep learning architectures (e.g., U-Net, Swin-Unet, SegFormer).
- `outputs/`: Stores the artifacts generated during model training and evaluation.
  - `checkpoints/`: Saved model weights from training.
  - `logs/`: Training metrics, loss logs, and TensorBoard data.
  - `predictions/`: Output segmentation masks generated by the models on the test set.

## Dataset Creation Pipeline

The dataset is created through a systematic processing pipeline from raw data to machine-learning-ready patches:

Rock Glacier Polygons (.shp)
↓
Rasterization
↓
Binary Mask (0=Background, 1=Rock Glacier)

Sentinel-2 + DEM + LST + Velocity
↓
Feature Extraction
↓
Feature Stack

Feature Stack + Mask
↓
Patch Extraction
↓
Train / Val / Test Dataset

Train Dataset
↓
U-Net / Swin-Unet / SegFormer

## Google Earth Engine Setup & Feature Extraction

To generate the multi-band feature stacks for the dataset, we rely on Google Earth Engine (GEE). The extraction pipeline requires a local GEE Python environment.

### 1. Environment Setup
A dedicated Conda environment is used to run the extraction scripts without dependency conflicts:
```bash
conda create -y -n rockglacier python=3.11
conda activate rockglacier
pip install earthengine-api geemap geopandas
```

### 2. Authentication
Because the script runs locally, you must authenticate your Google Account via the terminal (this is a one-time process):
```bash
earthengine authenticate
```
This command will open a web browser. Select the Google account registered for Earth Engine, grant permissions, and copy the provided Authorization Code back into your terminal.

### 3. Feature Extraction (Task 02)
Once authenticated, the feature extraction pipeline will:
- Read the generated `.geojson` polygon bounds.
- Query Sentinel-2 Surface Reflectance (Summer 2023) and Copernicus DEM.
- Calculate indices (NDVI, NDWI, NDSI, Slope, Aspect).
- Stack them into a 12-band GeoTIFF at exactly `10m/px` resolution with a `200m` buffer.
- Download them directly to the `features/` directory to perfectly align with the `masks/`.

See `task_02.md` for full technical specifications.

## Future Workflow
- **Data Preprocessing Pipeline:** Implement scripts to automate the extraction, rasterization, and stacking of features from `raw_data` into `features` and `stacks`.
- **Patch Generation:** Develop a sliding window approach to extract appropriately sized patches from the feature stacks and masks, saving them into the respective `dataset` splits.
- **Model Implementation:** Complete the architecture definitions in the `models/` directory for `unet.py`, `swin_unet.py`, and `segformer.py`.
- **Training & Evaluation:** Implement `train.py` and `evaluate.py` to streamline model training, hyperparameter tuning, and comprehensive evaluation on the test set.
- **Inference & Visualization:** Set up automated prediction on new regions using the best trained model and visualize the resulting predictions from `outputs/predictions`.

	# RockGlacier_Project

	## Project Overview
	This project focuses on the automated detection and segmentation of rock glaciers using deep learning techniques. By leveraging multi-modal remote sensing data—including Sentinel-2 optical imagery, Digital Elevation Models (DEM), Land Surface Temperature (LST), and surface velocity data—we aim to accurately map rock glaciers to better understand their distribution, dynamics, and response to climate change.

	## Repository Structure

	- `raw_data/`: Contains the original, unprocessed data files from various sources.
	- `shapefiles/`: Rock glacier polygons in vector format (e.g., .shp).
	- `sentinel2/`: Optical satellite imagery from Sentinel-2.
	- `dem/`: Digital Elevation Model data.
	- `lst/`: Land Surface Temperature data.
	- `velocity/`: Surface velocity maps.
	- `features/`: Stores intermediate extracted features derived from the raw data sources.
	- `masks/`: Contains binary masks generated from the shapefiles, where `0` indicates background and `1` indicates a rock glacier.
	- `stacks/`: Holds stacked feature arrays combining multiple data modalities (Sentinel-2, DEM, LST, velocity) into single multi-band files for model input.
	- `dataset/`: Contains the final extracted patches organized for model training, validation, and testing.
	- `train/`: Data used to train the model, containing `images/` and corresponding `masks/`.
	- `val/`: Data used to validate the model during training and tune hyperparameters.
	- `test/`: Unseen data used to evaluate the final model's generalization performance.
	- `models/`: Contains the PyTorch/TensorFlow implementations of various deep learning architectures (e.g., U-Net, Swin-Unet, SegFormer).
	- `outputs/`: Stores the artifacts generated during model training and evaluation.
	- `checkpoints/`: Saved model weights from training.
	- `logs/`: Training metrics, loss logs, and TensorBoard data.
	- `predictions/`: Output segmentation masks generated by the models on the test set.

	## Dataset Creation Pipeline

	The dataset is created through a systematic processing pipeline from raw data to machine-learning-ready patches:

	Rock Glacier Polygons (.shp)
	↓
	Rasterization
	↓
	Binary Mask (0=Background, 1=Rock Glacier)

	Sentinel-2 + DEM + LST + Velocity
	↓
	Feature Extraction
	↓
	Feature Stack

	Feature Stack + Mask
	↓
	Patch Extraction
	↓
	Train / Val / Test Dataset

	Train Dataset
	↓
	U-Net / Swin-Unet / SegFormer

	## Google Earth Engine Setup & Feature Extraction

	To generate the multi-band feature stacks for the dataset, we rely on Google Earth Engine (GEE). The extraction pipeline requires a local GEE Python environment.

	### 1. Environment Setup
	A dedicated Conda environment is used to run the extraction scripts without dependency conflicts:
	```bash
	conda create -y -n rockglacier python=3.11
	conda activate rockglacier
	pip install earthengine-api geemap geopandas
	```

	### 2. Authentication
	Because the script runs locally, you must authenticate your Google Account via the terminal (this is a one-time process):
	```bash
	earthengine authenticate
	```
	This command will open a web browser. Select the Google account registered for Earth Engine, grant permissions, and copy the provided Authorization Code back into your terminal.

	### 3. Feature Extraction (Task 02)
	Once authenticated, the feature extraction pipeline will:
	- Read the generated `.geojson` polygon bounds.
	- Query Sentinel-2 Surface Reflectance (Summer 2023) and Copernicus DEM.
	- Calculate indices (NDVI, NDWI, NDSI, Slope, Aspect).
	- Stack them into a 12-band GeoTIFF at exactly `10m/px` resolution with a `200m` buffer.
	- Download them directly to the `features/` directory to perfectly align with the `masks/`.

	See `task_02.md` for full technical specifications.

	## Future Workflow
	- Data Preprocessing Pipeline: Implement scripts to automate the extraction, rasterization, and stacking of features from `raw_data` into `features` and `stacks`.
	- Patch Generation: Develop a sliding window approach to extract appropriately sized patches from the feature stacks and masks, saving them into the respective `dataset` splits.
	- Model Implementation: Complete the architecture definitions in the `models/` directory for `unet.py`, `swin_unet.py`, and `segformer.py`.
	- Training & Evaluation: Implement `train.py` and `evaluate.py` to streamline model training, hyperparameter tuning, and comprehensive evaluation on the test set.
	- Inference & Visualization: Set up automated prediction on new regions using the best trained model and visualize the resulting predictions from `outputs/predictions`.