mfaytin
/

mask2former-satellite

@@ -1,199 +1,130 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
+datasets:
+- OpenEarthMap
+language: en
 library_name: transformers
+license: mit
+metrics:
+- name: mIoU
+  type: mean_iou
+  value: 0.5202420374155045
+pipeline_tag: image-segmentation
+tags:
+- semantic-segmentation
+- earth-observation
+- satellite-imagery
+- land-cover
+- openearthmap
+- mask2former
+- remote-sensing
 ---
+# Mask2Former for Satellite Image Segmentation
+This model is a fine-tuned version of [Mask2Former](https://huggingface.co/facebook/mask2former-swin-base-ade-semantic) for semantic segmentation of satellite/aerial imagery.
+## Model Description
+- **Architecture**: Mask2Former with Swin Transformer backbone
+- **Task**: Semantic Segmentation (Land Cover Classification)
+- **Dataset**: [OpenEarthMap](https://open-earth-map.org/)
+- **Fine-tuned from**: facebook/mask2former-swin-base-ade-semantic
+## Training Results
+| Metric | Value |
+|--------|-------|
+| **Best mIoU** | 0.5202 |
+| **Validation Loss** | 44.21 |
+| **Training Epochs** | 17 |
+## Classes
+This model classifies pixels into 9 land cover categories:
+| ID | Class |
+|----|-------|
+| 0 | Background |
+| 1 | Bareland |
+| 2 | Grass |
+| 3 | Pavement |
+| 4 | Road |
+| 5 | Tree |
+| 6 | Water |
+| 7 | Cropland |
+| 8 | Building |
+## Usage
+```python
+from transformers import Mask2FormerForUniversalSegmentation, Mask2FormerImageProcessor
+from PIL import Image
+import torch
+# Load model and processor
+model = Mask2FormerForUniversalSegmentation.from_pretrained("mfaytin/mask2former-satellite")
+processor = Mask2FormerImageProcessor.from_pretrained("mfaytin/mask2former-satellite")
+# Load and preprocess image
+image = Image.open("satellite_image.tif").convert("RGB")
+inputs = processor(images=image, return_tensors="pt")
+# Run inference
+with torch.no_grad():
+    outputs = model(**inputs)
+# Post-process to get segmentation map
+segmentation = processor.post_process_semantic_segmentation(
+    outputs,
+    target_sizes=[image.size[::-1]]  # (height, width)
+)[0]
+# segmentation is a tensor of shape (H, W) with class IDs
+print(f"Segmentation shape: {segmentation.shape}")
+print(f"Unique classes: {torch.unique(segmentation).tolist()}")
+```
+## Class Labels Mapping
+```python
+CLASS_LABELS = {
+    0: "Background",
+    1: "Bareland",
+    2: "Grass",
+    3: "Pavement",
+    4: "Road",
+    5: "Tree",
+    6: "Water",
+    7: "Cropland",
+    8: "Building",
+}
+```
+## Intended Use
+This model is intended for:
+- Land cover classification from satellite/aerial imagery
+- Urban planning and environmental monitoring
+- Geographic information system (GIS) applications
+- Remote sensing research
+## Limitations
+- **Geographic Bias**: Trained primarily on imagery from specific regions in the OpenEarthMap dataset
+- **Resolution Sensitivity**: Best performance on imagery similar to training data resolution
+- **Imagery Source**: May require fine-tuning for different satellite sensors or aerial platforms
+- **Seasonal Variation**: Performance may vary across different seasons or weather conditions
+## Citation
+If you use this model, please cite the OpenEarthMap dataset:
+```bibtex
+@inproceedings{xia2023openearthmap,
+  title={OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping},
+  author={Xia, Junshi and others},
+  booktitle={WACV},
+  year={2023}
+}
+```