| | --- |
| | base_model: |
| | - ibm-esa-geospatial/TerraMind-1.0-base |
| | pipeline_tag: image-classification |
| | tags: |
| | - methane |
| | - detection |
| | - geospatial |
| | - terramind |
| | --- |
| | |
| | # FAST-EO Use Case 2 - Methane Detection |
| |
|
| | This repository contains data and code to reproduce experiments for fine-tuning **TerraMind-Base** to detect methane-related signatures in multispectral imagery. It includes multiple experiment variants and their corresponding datasets. |
| |
|
| | The file `Methane_benchmark_patches_summary_v3.xlsx` provides per-patch descriptions and defines the **fold splits** used to ensure non-overlapping partitions. Runner scripts use this Excel file to build train/val/test splits, typically reserving one fold for testing. |
| |
|
| | All scripts provide usage instructions via `--help` (or `-h`). |
| |
|
| | ## Requirements |
| |
|
| | - Install the TerraMind/Terratorch stack used by the project before running experiments. |
| | - Ensure your environment has the required dependencies for data loading and training. |
| |
|
| | ## Experiments |
| |
|
| | ### Experiment 1: Fine-tuning on Methane Benchmark Dataset (MBD) |
| |
|
| | Fine-tune TerraMind-Base on the Methane Benchmark Dataset. The normalized dataset is provided in: |
| |
|
| | - `MBD_nan_S2_zscore/` |
| |
|
| | Training code is located in: |
| |
|
| | - `classification/` (includes dataset and dataloader classes) |
| |
|
| | ### Experiment 2: Fine-tuning on MBD with text captions |
| |
|
| | This experiment modifies the TerraMind-Base model to concatenate text-caption embeddings with visual embeddings. |
| |
|
| | - Caption embeddings are computed with `all-MiniLM-L6-v2`. |
| | - Code and resources are in `classification_with_text/`. |
| | - Original captions: `classification_with_text/MBD_text/` |
| | - Precomputed embeddings: `combined_caption_embeddings.csv` |
| |
|
| | ### Experiment 3: Sentinel-2 with simulated atmospheric conditions |
| |
|
| | Evaluate generalization on Sentinel-2 data with simulated atmospheric conditions in both: |
| | - Top-of-Atmosphere (TOA) |
| | - Bottom-of-Atmosphere (BOA) |
| |
|
| | The model can be trained on this simulated data or used only for inference to test cross-domain robustness. |
| |
|
| | ### Experiment 4: Intuition-1 with simulated atmospheric conditions |
| |
|
| | Analogous to Experiment 3, but using Intuition-1 imagery with simulated atmospheric conditions (TOA and BOA). This experiment tests robustness under domain shift. |
| |
|
| | ### Experiment 5: Urban dataset without methane (false-positive stress test) |
| |
|
| | A control dataset containing only urban imagery without methane is used to check whether models learn methane-specific cues rather than urban signatures. The goal is to quantify false positives. |
| |
|
| | Scripts for loading and running inference on this dataset are provided in the repository. |
| |
|
| | ## Notes |
| |
|
| | - Use `--help` on each runner/training script to see available options. |
| | - Keep fold definitions consistent with `Methane_benchmark_patches_summary_v3.xlsx` to ensure comparable results across experiments. |
| |
|
