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ARXIV:2602.12117 · METEOROLOGICAL AI · SUBMITTED 19 MAR · 21:31 UTC · FRESHNESS STALE
ARXIV:2602.12117METEOROLOGICAL AISUBMITTED 19 MAR · 21:31 UTCFRESHNESS STALEarXiv
Develop a lightweight, high-performance AI tool for tropical cyclone monitoring on edge devices.
Opportunity summary
Pain Develop a lightweight, high-performance AI tool for tropical cyclone monitoring on edge devices.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
Develop a lightweight, high-performance AI tool for tropical cyclone monitoring on edge devices. Timely monitoring of tropical cyclones is crucial for reducing loss of life and property, yet it is hindered by the computational…
Tropical cyclones (TC) are among the most destructive natural disasters, causing catastrophic damage to coastal regions through extreme winds, heavy rainfall, and storm surges. Timely monitoring of tropical cyclones is crucial for reducing loss…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. For Maximum Sustained Wind (MSW) prediction, experiments demonstrate that the KAN-FIF framework achieves a $94.8\%$ reduction in parameters (0.99MB vs 19MB) and $68.7\%$ faster…
Meteorological AI moved forward this cycle; last verified April 2026. Public score 8.0/10.
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Develop a lightweight, high-performance AI tool for tropical cyclone monitoring on edge devices.
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Paper Pack
10.48550/arXiv.2602.12117Develop a lightweight, high-performance AI tool for tropical cyclone monitoring on edge devices.
Abstract
Tropical cyclones (TC) are among the most destructive natural disasters, causing catastrophic damage to coastal regions through extreme winds, heavy rainfall, and storm surges. Timely monitoring of tropical cyclones is crucial for reducing loss of life and property, yet it is hindered by the computational inefficiency and high parameter counts of existing methods on resource-constrained edge devices. Current physics-guided models suffer from linear feature interactions that fail to capture high-order polynomial relationships between TC attributes, leading to inflated model sizes and hardware incompatibility. To overcome these challenges, this study introduces the Kolmogorov-Arnold Network-based Feature Interaction Framework (KAN-FIF), a lightweight multimodal architecture that integrates MLP and CNN layers with spline-parameterized KAN layers. For Maximum Sustained Wind (MSW) prediction, experiments demonstrate that the KAN-FIF framework achieves a $94.8\%$ reduction in parameters (0.99MB vs 19MB) and $68.7\%$ faster inference per sample (2.3ms vs 7.35ms) compared to baseline model Phy-CoCo, while maintaining superior accuracy with $32.5\%$ lower MAE. The offline deployment experiment of the FY-4 series meteorological satellite processor on the Qingyun-1000 development board achieved a 14.41ms per-sample inference latency with the KAN-FIF framework, demonstrating promising feasibility for operational TC monitoring and extending deployability to edge-device AI applications. The code is released at https://github.com/Jinglin-Zhang/KAN-FIF.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 33% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 8.0
PROBLEM
Develop a lightweight, high-performance AI tool for tropical cyclone monitoring on edge devices. Timely monitoring of tropical cyclones is crucial for reducing loss of life and property, yet it is hindered by the computational inefficiency and high parameter counts of existing m...
METHOD
Tropical cyclones (TC) are among the most destructive natural disasters, causing catastrophic damage to coastal regions through extreme winds, heavy rainfall, and storm surges. Timely monitoring of tropical cyclones is crucial for reducing loss of life and property, yet it is hi...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. For Maximum Sustained Wind (MSW) prediction, experiments demonstrate that the KAN-FIF framework achieves a $94.8\%$ reduction in parameters (0.99MB vs 19MB) and $68.7\%$ faster inference per sample (2.3ms...
WHY NOW
Meteorological AI moved forward this cycle; last verified April 2026. Public score 8.0/10.
achieves a $94.8\%$ reduction in parameters (0.99MB vs 19MB)
Explicitly stated in the abstract with specific numeric comparison (0.99MB vs 19MB).
partial
$68.7\%$ faster inference per sample (2.3ms vs 7.35ms)
Explicitly stated in the abstract with specific numeric comparison (2.3ms vs 7.35ms).
partial
maintaining superior accuracy with $32.5\%$ lower MAE
Explicitly stated in the abstract with specific numeric improvement.
partial
achieved a 14.41ms per-sample inference latency with the KAN-FIF framework
Explicitly stated in the abstract with specific numeric result from deployment experiment.
partial
Current physics-guided models suffer from linear feature interactions that fail to capture high-order polynomial relationships between TC attributes
Directly stated in the abstract as a limitation of existing methods.
partial
demonstrating promising feasibility for operational TC monitoring and extending deployability to edge-device AI applications
Directly stated in the abstract as a conclusion from the deployment experiment.
partial
Real-world satellite integration may face unforeseen compatibility issues
Explicitly mentioned in the analysis excerpt as a caveat, though not in the abstract.
partial
integrates MLP and CNN layers with spline-parameterized KAN layers
Directly stated in the abstract describing the method architecture.
partial
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Concepts
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Develop a lightweight, high-performance AI tool for tropical cyclone monitoring on edge devices.
Segment
Meteorological AI
Adoption evidence
No public code link in the paper record yet
Commercial read
8.0/10 public viability
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reason
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proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Evidence coverage
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stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
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passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
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Evidence
0 references, 0 sources, 33% evidence coverage.
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Integration burden
missing
Current read
No public implementation surface observed.
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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