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Fine-tuned Prithvi EO 2.0 (IBM/NASA Geospatial Foundation Model) for pixel-level wildfire burn scar severity classification using Sentinel-2 multi-temporal satellite imagery.
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Description de la source
Fine-tuned Prithvi EO 2.0 (IBM/NASA Geospatial Foundation Model) for pixel-level wildfire burn scar severity classification using Sentinel-2 multi-temporal satellite imagery.
Author: Tushar Thokdar
This model performs on multi-temporal Sentinel-2 satellite imagery to classify burn severity into 5 classes. It builds on the NASA-IBM Prithvi EO 2.0 Vision Transformer backbone with a UperNet decoder, fine-tuned using a novel and a training strategy.
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1 sourceVérifié 27 sept.
Artefacts du modèle
1 artefactExtraits de sources
2 extraits| Component | Details |
|---|---|
| Backbone | prithvi_eo_v2_tiny_tl (ViT-based Masked Autoencoder) |
| Decoder | UperNet (Unified Perceptual Parsing Network) |
| Parameters | ~25.1M |
| Pre-training | IBM/NASA global satellite imagery |
| Fine-tuning | Burn scar severity from Sentinel-2 |
(Batch, Time=3, Channels=6, Height=224, Width=224)| Class ID | Label | Color |
|---|---|---|
| 0 | Unburned | Green |
| 1 | Low Severity | Yellow |
| 2 | Moderate-Low | Orange |
| 3 | Moderate-High | Red-Orange |
| 4 | High Severity | Dark Red |
| Metric | Baseline (Pretrained) | Fine-Tuned | Improvement |
|---|---|---|---|
| Accuracy | 35.86% | 69.93% | +34.07% |
| Macro F1 | 0.1160 | 0.6218 | +50.58% |
| Weighted F1 | 0.2035 | 0.7015 | +49.80% |
| Burned F1 | 0.0133 | 0.5553 | +54.20% |
| Class | Baseline | Fine-Tuned |
|---|---|---|
| Unburned | 0.5667 | 0.8187 |
| Low Severity | 0.0000 | 0.5336 |
| Moderate-Low | 0.0000 | 0.4804 |
| Moderate-High | 0.0133 | 0.4029 |
| High Severity | 0.0509 | 0.7055 |
| Parameter | Value |
|---|---|
| Optimizer | AdamW |
| Learning Rate | 1e-4 |
| Weight Decay | 0.01 |
| LR Scheduler | Cosine Annealing (T_max=30, eta_min=1e-6) |
| Batch Size | 8 |
| Max Epochs | 50 (early stopped at 38) |
| Early Stopping | Patience=12 on val_loss |
| Precision | 16-bit mixed (AMP) |
| Gradient Clipping | 1.0 |
Hybrid loss combining class-weighted Cross-Entropy and Dice Loss:
Loss = CrossEntropy(class_weights) + 0.8 * DiceLoss(classes=[1,2,3,4])
Class weights: [0.7406, 1.3147, 1.0963, 1.2333, 0.8575]
Standard approaches use 2-frame stacks (pre + post). This model uses a 3-frame stack that includes an explicit spectral difference:
Delta = Clip(Post-fire - Pre-fire, -1.0, 1.0)
Input = Stack([Pre-fire, Post-fire, Delta]) # Shape: (3, 6, 224, 224)
This provides the model with direct change magnitude information, significantly improving boundary detection.
import torch
import numpy as np
from huggingface_hub import hf_hub_download
# Download checkpoint
ckpt_path = hf_hub_download(
repo_id="Tushar365/prithvi-burn-scar-model",
filename="prithvi-delta-best.ckpt",
)
# Model class must be available for load_from_checkpoint
from model import PrithviFireSegmentation
CLASS_WEIGHTS = torch.tensor([0.7406347, 1.3146889, 1.0963433, 1.233306, 0.8574721])
model = PrithviFireSegmentation.load_from_checkpoint(
ckpt_path, class_weights=CLASS_WEIGHTS
)
model.eval()
model.cuda()
# Load a sample chip (3, 6, 224, 224)
image = np.load("chip_000000.npy")
input_tensor = torch.from_numpy(image).float().unsqueeze(0).cuda()
with torch.no_grad():
logits = model(input_tensor)
prediction = torch.argmax(logits, dim=1).squeeze(0).cpu().numpy()
# prediction shape: (224, 224) with values 0-4
torch>=2.1.0
pytorch-lightning==2.1.0
terratorch
segmentation-models-pytorch
numpy==1.26.4
| File | Description |
|---|---|
prithvi-delta-best.ckpt | Fine-tuned model checkpoint (~100MB) |
model.py | PrithviFireSegmentation class definition (required for loading) |
README.md | This model card |
@misc{thokdar2024prithviburnscars,
title={Fine-tuning Prithvi EO 2.0 for Burn Scar Severity Detection with Delta Channel Algorithm},
author={Tushar Thokdar},
year={2024},
note={End-to-end wildfire damage assessment pipeline using NASA-IBM foundation model}
}
--- license: apache-2.0 library_name: pytorch pipeline_tag: image-segmentation tags: - earth-observation - segmentation - prithvi - wildfire - burn-scar - sentinel-2 - terratorch - vision-transformer - pytorch - pytorch-lightning - remote-sensing - geospatial datasets: - Tushar365/prithvi-burn-scar-dataset metrics: - f1 - accuracy model-index: - name: Prithvi EO 2.0 Burn Scar Severity results: - task: type: image-segmentation name: Semantic Segmentation dataset: name: Prithvi Burn Scar Severity Dataset type: Tushar365/prithvi-burn-scar-dataset metrics: - name: Macro F1 type: f1 value: 0.6218 - name: Accuracy type: accuracy value: 0.6993 - name: Weighted F1 type: f1 value: 0.7015 --- # Prithvi EO 2.0 - Burn Scar Severity Detection Fine-tuned **Prithvi EO 2.0** (IBM/NASA Geospatial Foundation Model) for pixel-level wildfire burn scar severity classification using Sentinel-2 multi-temporal satellite imagery. **Author:** Tushar Thokdar ## Model Description This model performs **semantic segmentation** on multi-temporal Sentinel-2 satellite imagery to classify burn severity into 5 classes. It builds on the NASA-IBM Prithvi EO 2.0 Vision Transformer backbone with a UperNet decoder, fine-tuned using a novel **Delta Channel Algorithm** and a **Freeze-then-Unfreeze** training strategy. ### Architecture | Component | Details | |:----------|:--------| | Backbone | `prithvi_eo_v2_tiny_tl` (ViT-based Masked Autoencoder) | | Decoder | UperNet (Unified Perceptual Parsing Network) | | Parameters | ~25.1M | | Pre-training | IBM/NASA global satellite imagery | | Fine-tuning | Burn scar severity from Sentinel-2 | ### Input Specification - **Shape:** `(Batch, Time=3, Channels=6, Height=224, Width=224)` - **Temporal frames:** Pre-fire, Post-fire, Delta (Post - Pre, clipped to [-1, 1]) - **Spectral bands:** B2 (Blue), B3 (Green), B4 (Red), B8A (NIR), B11 (SWIR1), B12 (SWIR2) - **Normalization:** Surface reflectance values in [0, 1] ### Output Classes | Class ID | Label | Color | |:---------|:------|:------| | 0 | Unburned | Green | | 1 | Low Severity | Yellow | | 2 | Moderate-Low | Orange | | 3 | Moderate-High | Red-Orange | | 4 | High Severity | Dark Red | ## Performance | Metric | Baseline (Pretrained) | Fine-Tuned | Improvement | |:-------|:----------------------|:-------...
Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Library pytorch · Repo Tushar365/prithvi-burn-scar-model