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---
license: apache-2.0
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- siglip2
- SAT
- Landforms
---
# **SAT-Landforms-Classifier**
> **SAT-Landforms-Classifier** is an image classification vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for a single-label classification task. It is designed to classify satellite images into different landform categories using the **SiglipForImageClassification** architecture.
```py
Accuracy: 0.9863
F1 Score: 0.9858
Classification Report:
precision recall f1-score support
Annual Crop 0.9866 0.9810 0.9838 3000
Forest 0.9927 0.9957 0.9942 3000
Herbaceous Vegetation 0.9697 0.9800 0.9748 3000
Highway 0.9826 0.9928 0.9877 2500
Industrial 0.9964 0.9916 0.9940 2500
Pasture 0.9882 0.9610 0.9744 2000
Permanent Crop 0.9690 0.9760 0.9725 2500
Residential 0.9940 0.9970 0.9955 3000
River 0.9864 0.9872 0.9868 2500
Sea Lake 0.9963 0.9923 0.9943 3000
accuracy 0.9863 27000
macro avg 0.9862 0.9855 0.9858 27000
weighted avg 0.9863 0.9863 0.9863 27000
```
The model categorizes images into ten classes:
- **Class 0:** "Annual Crop"
- **Class 1:** "Forest"
- **Class 2:** "Herbaceous Vegetation"
- **Class 3:** "Highway"
- **Class 4:** "Industrial"
- **Class 5:** "Pasture"
- **Class 6:** "Permanent Crop"
- **Class 7:** "Residential"
- **Class 8:** "River"
- **Class 9:** "Sea Lake"
# **Run with Transformers🤗**
```python
!pip install -q transformers torch pillow gradio
```
```python
import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/SAT-Landforms-Classifier"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def landform_classification(image):
"""Predicts landform category for a satellite image."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
labels = {
"0": "Annual Crop", "1": "Forest", "2": "Herbaceous Vegetation", "3": "Highway", "4": "Industrial",
"5": "Pasture", "6": "Permanent Crop", "7": "Residential", "8": "River", "9": "Sea Lake"
}
predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
return predictions
# Create Gradio interface
iface = gr.Interface(
fn=landform_classification,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Prediction Scores"),
title="SAT Landforms Classification",
description="Upload a satellite image to classify its landform type."
)
# Launch the app
if __name__ == "__main__":
iface.launch()
```
# **Intended Use:**
The **SAT-Landforms-Classifier** model is designed to classify satellite images into various landform types. Potential use cases include:
- **Land Use Monitoring:** Identifying different land use patterns from satellite imagery.
- **Environmental Studies:** Supporting researchers in tracking changes in vegetation and water bodies.
- **Urban Planning:** Assisting planners in analyzing residential, industrial, and infrastructure distributions.
- **Agricultural Analysis:** Helping assess crop distribution and pastureland areas.
- **Disaster Management:** Providing insights into land coverage for emergency response and recovery planning. |