3DCNN+ Dust Detector
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About
The 3DCNN+ Dust Detector is a highly optimized 3-D Convolutional Neural Network pipeline designed for near real-time detection of dust storms from MODIS satellite imagery. It leverages spatial-spectral patterns from 38 spectral channels to achieve 91.1% pixel-level accuracy. Key optimizations, including memory-mapped storage, precomputed patch indices, large-batch training, torch.compile, and mixed-precision arithmetic, result in a remarkable 21-fold training speedup, reducing training time from 12 hours to under 35 minutes. This makes full-granule inference possible in less than two seconds on an A100 GPU. The system supports public-health alerts and operational forecasting by providing rapid, reliable dust storm monitoring, overcoming the challenges of traditional remote-sensing pipelines with high data volumes and deep-learning latency.
Platform
Features
• code available on github
• near real-time inference
• fast gpu utilization
• mixed-precision training
• memory-mapped i/o
• 21x training speedup
• 91.1% pixel-level accuracy
• 3d convolutional neural network pipeline
FAQs
Can the pipeline run on commodity hardware?
Yes. Bulk data resides on disk (memory-mapped), small fraction loads into RAM. Mixed-precision training reduces GPU memory, enabling the model to run on a single modern GPU (e.g., RTX 3080) without sacrificing accuracy.
How does the weighted loss affect dust-storm maps?
Weighted MSE penalizes mis-classifications in strong dust reflectance regions more, focusing the optimizer on hazardous pixels. This results in cleaner, more reliable dust masks aligning with ground observations.
Pricing Plans
Open-Source
Free Plan• Source code access
• Dust storm detection
• 91.1% pixel-level accuracy
• 21x faster training
• Near real-time inference
Job Opportunities
There are currently no job postings for this AI tool.
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