Name: Kentucky Flooding, 2022, Irving County, SALaD
Display Field: id
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Date of Image: 7/23/2022, 8/03/2022</SPAN></P><P><SPAN>Summary:</SPAN></P><P><SPAN>The NASA GSFC landslides team mapped potential damage using the change detection-based Semi-Automatic Landslide Detection (SALaD-CD) system (https://doi.org/10.1002/gdj3.145). SALaD-CD uses object-oriented change detection and machine learning to map landslides, but the model was utilized here as a test to see how well it performed identifying potential damage post-flooding. The potential damage areas were derived from using pre (07/23/2022) and post (08/03/22) PlanetScope imagery. This data should be used in conjunction with ancillary datasets to help prioritize areas to assess for potential damage.</SPAN></P><P><SPAN>NOTE: This is a rapid response product. We have manually removed false positives that misidentified clouds as change, but otherwise we have not done any manual corrections for this product to be research grade.</SPAN></P><P><SPAN>Suggested Use:</SPAN></P><P><SPAN>This data should be used in conjunction with ancillary datasets to help guide how to prioritize areas to assess for potential damage. This is not a research grade product.</SPAN></P><P><SPAN>Satellite/Sensor:</SPAN></P><P><SPAN>PlanetScope</SPAN></P><P><SPAN>Resolution:</SPAN></P><P><SPAN>3 meters</SPAN></P></DIV></DIV></DIV>
Copyright Text: NASA Landslides Team, Planet Team (2017). Planet Application Program Interface: In Space for Life on Earth. San Francisco, CA. https://api.planet.com
Name: Kentucky Flooding, 2022, Breathitt County, SALaD
Display Field: id
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Date of Image: 7/23/2022, 8/03/2022</SPAN></P><P><SPAN>Summary:</SPAN></P><P><SPAN>The potential damage areas were derived from using pre (07/23/2022) and post (08/03/22) PlanetScope imageryThe NASA GSFC landslides team mapped potential damage using the change detection-based Semi-Automatic Landslide Detection (SALaD-CD) system (https://doi.org/10.1002/gdj3.145). SALaD-CD uses object-oriented change detection and machine learning to map landslides, but the model was utilized here as a test to see how well it performed identifying potential damage post-flooding. . This data should be used in conjunction with ancillary datasets to help prioritize areas to assess for potential damage.</SPAN></P><P><SPAN>NOTE: This is a rapid response product. We have manually removed false positives that misidentified clouds as change, but otherwise we have not done any manual corrections for this product to be research grade.</SPAN></P><P><SPAN>Suggested Use:</SPAN></P><P><SPAN>This data should be used in conjunction with ancillary datasets to help guide how to prioritize areas to assess for potential damage. This is not a research grade product.</SPAN></P><P><SPAN>Satellite/Sensor:</SPAN></P><P><SPAN>PlanetScope</SPAN></P><P><SPAN>Resolution:</SPAN></P><P><SPAN>3 meters</SPAN></P></DIV></DIV></DIV>
Copyright Text: NASA Landslides Team, Planet Team (2017). Planet Application Program Interface: In Space for Life on Earth. San Francisco, CA. https://api.planet.com