Segformer B0 Finetuned Deprem Satellite
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Segformer B0 Finetuned Deprem Satellite
Developed by sawthiha
A semantic segmentation model fine-tuned on satellite image datasets based on NVIDIA SegFormer-B0 architecture, specifically designed for earthquake-related scenario analysis
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Release Time : 1/1/2024
Model Overview
This model is an optimized deep learning model for satellite image semantic segmentation tasks, particularly suitable for earthquake disaster assessment scenarios. It can accurately identify and segment different land cover categories in satellite images.
Model Features
High-Precision Segmentation
Achieves 98.49% mean Intersection over Union (mIoU) and 99.33% overall accuracy on the evaluation dataset
Efficient Inference
Processes 10.988 samples per second during evaluation, suitable for real-time applications
Domain Adaptation
Specifically fine-tuned on the deprem_satellite_semantic_whu_dataset, optimized for earthquake scenario analysis
Model Capabilities
Satellite Image Analysis
Semantic Segmentation
Disaster Assessment
Land Cover Identification
Use Cases
Disaster Response
Earthquake Damage Assessment
Analyze damage to buildings and infrastructure after earthquakes through satellite images
Can accurately segment damaged areas to support rescue decision-making
Geographic Information Systems
Land Use Classification
Automatically classify and label different land types in satellite images
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