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datasets/damage-probability-ian.data.mdx

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 <Prose>
 The contiguous United States (CONUS) Disturbance Watcher system was built based on time-series analysis and machine learning techniques. The system consisted of two components. The first component, the retrospective disturbance analysis, extracted disturbance predictors from historical open access disturbance products and satellite datasets and built a series of machine learning models respectively for different lag stages. The second component, namely the near real-time monitoring, recursively updates per-pixel time-series models and outputted current vectors of disturbance predictor using Stochastic Continuous Change Detection (S-CCD) algorithm, and then applied the offline machine learning models from the first component to map disturbance probabilities and patches. The system could be updated regularly in an interval of one week, or updated as an user-defined interval, such as one day that was used for Hurricane Ian monitoring. The per-day updating interval provided the quickest mapping response and the most extensive damage region as some would be recovered after days, while the highest computational resources were needed.   
 
-**Temporal Extent:** January 1, 2022 - October 3, 2022 
+**-Temporal Extent:** January 1, 2022 - October 3, 2022 
 
-**Temporal Resolution:** Daily 
+**-Temporal Resolution:** Daily 
 
-**Spatial Extent:** Continental United States
+**-Spatial Extent:** Continental United States
 
-**Spatial Resolution:** 30 m x 30 m 
+**-Spatial Resolution:** 30 m x 30 m 
 
-**Data Units:** Disturbance probability from 0 to 1 
+**-Data Units:** Disturbance probability from 0 to 1 
 
-**Data Type:** (Research, Regulatory, or Operational)
+**-Data Type:** (Research, Regulatory, or Operational)
 
-**Data Latency:** Daily 
+**-Data Latency:** Daily 
 
 
 **Scientific Details:**