Using 3D models in ArcGIS Pro to visualize buildings susceptible to sea level rise.

To identify Miami Beach buildings vulnerable to sea level rise and estimate the year of potential inundation, I employed various data sources available through Esri, along with their ArcGIS Pro software. Utilizing a 2D layer of building footprints in Miami Beach, Florida provided by Esri, I used their extrusion tools to accurately represent building heights. Building height data from a separate database was integrated into the attribute table using the Join Field geoprocessing tool.

Next, a 3D multipatch layer, supplied by Esri, was utilized to enhance the accuracy of the 3D model. This multipatch layer combines data from sources such as drone imagery, aerial imagery, and building footprints, adding roof pitch and shapes for a more detailed representation.

Once the 3D model was established, I incorporated sea level rise data from ArcGIS Living Atlas, sourced from NOAA's Digital Coast. Choosing the Intermediate-High scenario, I created three vector layers indicating possible sea level rise for 2030, 2050, and 2090. Using the Select By Location tool in ArcGIS Pro, I identified buildings projected to be affected by sea level rise in each respective year.

The Calculate Field tool was then employed to label the selected buildings with the corresponding inundation year (2030, 2050, or 2090). Visualizing the data in the symbology tab, I assigned distinct colors to signify the anticipated year of impact: yellow for 2030, orange for 2050, and red for 2090. Buildings likely unaffected were marked in green.

The map, highlighting buildings affected by sea level rise over time, was exported to ArcGIS Online. Using ArcGIS Instant Apps templates, I created an interactive map to assist decision-makers and communities in analyzing and planning for the long-term impacts of sea level rise on Miami Beach. This tool provides valuable insights to mitigate potential impacts and plan for changes proactively.

Next
Next

GeoMundo: Automated detection of deforestation from satellite images