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arXiv Machine Learning By Pann Thinzar Seint, Subas Chhatkuli, Bryan Atwood

A Two-Stage Cascade for Near-Real-Time Forest Anomaly Detection from Sentinel-1 SAR Time Series

Read the original on arXiv Machine Learning →

The paper presents a two‑stage cascade for near‑real‑time detection of forest anomalies using Sentinel‑1 SAR time series. The first stage applies an adaptive robust‑statistics z‑score test on co‑registered VH backscatter against a same‑season historical baseline, while the second stage uses a convolutional autoencoder’s structural similarity metric to confirm disturbances. Alerts are issued only when both stages agree, with confidence scores and risk tiers based on repeat‑occurrence history, and the system outputs area estimates compatible with MRV workflows.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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