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--- |
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license: apache-2.0 |
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datasets: |
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- jonathan-roberts1/GID |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- Gaofen-Image-Dataset |
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- Land-Cover-Classification |
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- Remote-Sensing-Images |
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--- |
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# **GiD-Land-Cover-Classification** |
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> **GiD-Land-Cover-Classification** is a multi-class image classification model based on `google/siglip2-base-patch16-224`, trained to detect **land cover types** in geographical or environmental imagery. This model can be used for **urban planning**, **agriculture monitoring**, and **environmental analysis**. |
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```py |
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Classification Report: |
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precision recall f1-score support |
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arbor woodland 0.8868 0.9130 0.8997 2000 |
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artificial grassland 0.9173 0.9425 0.9297 2000 |
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dry cropland 0.9320 0.9395 0.9358 2000 |
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garden plot 0.8639 0.8380 0.8508 2000 |
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industrial land 0.8967 0.8940 0.8953 2000 |
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irrigated land 0.8817 0.7865 0.8314 2000 |
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lake 0.7597 0.8045 0.7814 2000 |
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natural grassland 0.9770 0.9750 0.9760 2000 |
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paddy field 0.9305 0.9580 0.9441 2000 |
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pond 0.7646 0.7405 0.7523 2000 |
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river 0.8124 0.7945 0.8033 2000 |
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rural residential 0.8875 0.8325 0.8591 2000 |
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shrub land 0.8936 0.9195 0.9064 2000 |
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traffic land 0.9577 0.9510 0.9543 2000 |
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urban residential 0.7821 0.8470 0.8133 2000 |
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accuracy 0.8757 30000 |
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macro avg 0.8762 0.8757 0.8755 30000 |
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weighted avg 0.8762 0.8757 0.8755 30000 |
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``` |
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--- |
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## **Label Classes** |
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The model distinguishes between the following land cover types: |
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``` |
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0: arbor woodland |
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1: artificial grassland |
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2: dry cropland |
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3: garden plot |
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4: industrial land |
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5: irrigated land |
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6: lake |
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7: natural grassland |
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8: paddy field |
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9: pond |
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10: river |
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11: rural residential |
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12: shrub land |
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13: traffic land |
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14: urban residential |
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``` |
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--- |
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## **Installation** |
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```bash |
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pip install transformers torch pillow gradio |
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``` |
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--- |
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## **Example Inference Code** |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/GiD-Land-Cover-Classification" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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# ID to label mapping |
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id2label = { |
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"0": "arbor woodland", |
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"1": "artificial grassland", |
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"2": "dry cropland", |
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"3": "garden plot", |
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"4": "industrial land", |
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"5": "irrigated land", |
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"6": "lake", |
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"7": "natural grassland", |
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"8": "paddy field", |
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"9": "pond", |
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"10": "river", |
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"11": "rural residential", |
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"12": "shrub land", |
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"13": "traffic land", |
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"14": "urban residential" |
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} |
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def detect_land_cover(image): |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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prediction = {id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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return prediction |
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# Gradio Interface |
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iface = gr.Interface( |
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fn=detect_land_cover, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=5, label="Land Cover Type"), |
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title="GiD-Land-Cover-Classification", |
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description="Upload an image to classify its land cover type: arbor woodland, dry cropland, lake, river, traffic land, etc." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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--- |
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## **Applications** |
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* **Urban Development Planning** |
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* **Agricultural Monitoring** |
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* **Land Use and Land Cover (LULC) Mapping** |
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* **Disaster Management and Flood Risk Analysis** |
