Replication Data for: Fast flood extent monitoring with SAR change detection using Google Earth Engine
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Fast flood extent monitoring with SAR change detection using Google Earth EngineThis dataset develops a tool for near real-time flood monitoring through a novel combining of multi-temporal and multi-source remote sensing data. We use a SAR change detection and thresholding method, and apply sensitivity analytics and thresholding calibration, using SAR-based and optical-based indices in a format that is streamlined, reproducible, and geographically agile. We leverage the massive repository of satellite imagery and planetary-scale geospatial analysis tools of GEE to devise a flood inundation extent model that is both scalable and replicable. The flood extents from the 2021 Hurricane Ida and the 2017 Hurricane Harvey were selected to test the approach. The methodology provides a fast, automatable, and geographically reliabl' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Flood extent monitoring, multi-source remote sensing data, multi-temporal Sentinel-1 radar data, optical remote sensing data, change detection and thresholding, Google Earth Engine, Hurricanes"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Fast flood extent monitoring with SAR change detection using Google Earth EngineThis dataset develops a tool for near real-time flood monitoring through a novel combining of multi-temporal and multi-source remote sensing data. We use a SAR change detection and thresholding method, and apply sensitivity analytics and thresholding calibration, using SAR-based and optical-based indices in a format that is streamlined, reproducible, and geographically agile. We leverage the massive repository of satellite imagery and planetary-scale geospatial analysis tools of GEE to devise a flood inundation extent model that is both scalable and replicable. The flood extents from the 2021 Hurricane Ida and the 2017 Hurricane Harvey were selected to test the approach. The methodology provides a fast, automatable, and geographically reliabl' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Fast flood extent monitoring with SAR change detection using Google Earth EngineThis dataset develops a tool for near real-time flood monitoring through a novel combining of multi-temporal and multi-source remote sensing data. We use a SAR change detection and thresholding method, and apply sensitivity analytics and thresholding calibration, using SAR-based and optical-based indices in a format that is streamlined, reproducible, and geographically agile. We leverage the massive repository of satellite imagery and planetary-scale geospatial analysis tools of GEE to devise a flood inundation extent model that is both scalable and replicable. The flood extents from the 2021 Hurricane Ida and the 2017 Hurricane Harvey were selected to test the approach. The methodology provides a fast, automatable, and geographically reliabl
Flood extent monitoring, multi-source remote sensing data, multi-temporal Sentinel-1 radar data, optical remote sensing data, change detection and thresholding, Google Earth Engine, Hurricanes