Model Card — SegFormer-B0 Kathmandu Valley Satellite Segmentation

Model Description

This model is a fine-tuned SegFormer-B0 for semantic segmentation of satellite imagery over Kathmandu Valley, Nepal. It classifies each pixel into one of 7 land-use categories: Background, Residential Area, Road, River, Forest, and Unused Land. The model is intended for urban planning, GIS analysis, and geospatial research applications.

• Developed by: praniil
• Model type: Semantic Segmentation (SegFormer-B0)
• Language(s) (NLP): N/A (Computer Vision)
• License: MIT
• Finetuned from model: nvidia/mit-b0 (SegFormer-B0 pretrained on ImageNet)

Model Sources

• Repository: https://github.com/praniil/satellite-image-segmentation
• HuggingFace Hub: Pranilllllll/segformer-satellite-segementation

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Uses

Direct Use

This model can be used out-of-the-box for satellite image segmentation over Kathmandu Valley or similar urban/semi-urban landscapes. It accepts a 512×512 RGB satellite image and outputs a per-pixel land-use classification mask.

import torch
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from transformers import SegformerForSemanticSegmentation, SegformerFeatureExtractor

device = "cuda" if torch.cuda.is_available() else "cpu"

HF_REPO = "Pranilllllll/segformer-satellite-segementation"
model = SegformerForSemanticSegmentation.from_pretrained(HF_REPO).to(device)
processor = SegformerFeatureExtractor.from_pretrained(HF_REPO)
model.eval()

image = Image.open("path_to_your_satellite_image.png").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
pixel_values = inputs["pixel_values"].to(device)

with torch.no_grad():
 outputs = model(pixel_values=pixel_values)
 logits = outputs.logits # [1, 7, H, W]

pred_mask = torch.argmax(logits, dim=1).squeeze().cpu().numpy()

colors = np.array([
 [0, 0, 0], # Background
 [128, 0, 0], # Residential Area
 [0, 128, 0], # Road
 [0, 0, 128], # River
 [0, 128, 128], # Forest
 [128, 128, 0], # Unused Land
 [128, 0, 128], # (reserved)
], dtype=np.uint8)

seg_image = colors[pred_mask]
plt.imsave("prediction.png", seg_image)
print("Inference complete. Prediction saved as prediction.png")

Downstream Use

This model can be plugged into larger GIS pipelines for:

• Automated land-use/land-cover (LULC) mapping
• Urban sprawl analysis
• River and forest change detection
• Input feature generation for spatial planning models

Out-of-Scope Use

• Not suitable for segmenting non-satellite imagery (street photos, drone footage with different resolution/angle).
• Performance may degrade on satellite imagery from regions with significantly different land cover patterns than Kathmandu Valley.
• Not suitable for fine-grained object detection within classes (e.g., identifying individual buildings).

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Bias, Risks, and Limitations

• Geographic bias: Trained exclusively on Kathmandu Valley tiles; may not generalize to other geographies.
• Class imbalance: Despite weighted loss, rare classes (Road, River) may have lower per-class IoU.
• Resolution dependency: Expects 512×512 input tiles; other resolutions require resizing and may affect accuracy.
• Annotation noise: Manual annotations via CVAT may have some boundary ambiguity between classes.

Recommendations

Validate predictions on your specific region before using results for critical planning decisions. Cross-checking against GIS datasets (e.g., OpenStreetMap) is recommended.

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How to Get Started with the Model

Install dependencies:

pip install torch transformers Pillow matplotlib

Then use the inference script in the Direct Use section above.

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Training Details

Training Data

A custom dataset was built from satellite imagery of Kathmandu Valley, Nepal, divided into a grid of tiles.

• Total images: ~400 tiles
• Resolution: 512 × 512 pixels
• Annotation tool: CVAT
• Task: Multi-class semantic segmentation

Annotation Classes

Training Procedure

Preprocessing

• Images resized to 512 × 512
• Standard ImageNet normalization via SegformerFeatureExtractor

Data Augmentation

Applied using albumentations:

• Horizontal and vertical flips
• Random 90-degree rotations
• Resize to 512 × 512

Training Hyperparameters

Class Imbalance Handling

Inverse frequency class weights were computed from the training set and applied to the cross-entropy loss, ensuring rare classes (Road, River) contribute proportionally during training.

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Evaluation

Metrics

• Mean IoU (mIoU): Primary metric — overlap between predicted and ground truth masks averaged across all classes.
• Per-class IoU: Segmentation accuracy per land-use category.
• Qualitative inspection: Visual comparison of predicted vs. ground truth masks.

Results

Cross-validation results are reported as mean ± standard deviation of mIoU across 3 folds. Training curves (loss, mIoU, gradient norm) are available in the eval_plots/ directory.

The stable gradient norm across training confirms the MiT encoder converged effectively without vanishing gradient issues.

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Model Architecture

• Backbone: SegFormer-B0 (nvidia/mit-b0)
• Encoder: MiT (Mix Transformer) — hierarchical global context without positional encoding
• Decoder: Lightweight MLP head — per-pixel class probability predictions
• Output: 7-class segmentation mask over a 512×512 spatial grid

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Environmental Impact

• Hardware Type: CUDA-enabled GPU
• Cloud Provider: Not applicable (local training)
• Compute Region: Nepal
• Carbon Emitted: Not measured

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Citation

@misc{praniil2024kathmandu-segmentation,
 author = {praniil},
 title = {Kathmandu Valley Satellite Image Segmentation with SegFormer-B0},
 year = {2024},
 publisher = {GitHub},
 howpublished = {\url{https://github.com/praniil/satellite-image-segmentation}},
}

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Model Card Authors

praniil

Model Card Contact

Open an issue at https://github.com/praniil/satellite-image-segmentation/issues