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- Andrew Saah

I can help you with:

  •     Designing robust GIS-integrated workflows and methodologies
  •     Spatial data discovery, acquisition, cleaning and visualization
  •     All web-mapping options available in ArcGIS Online
  •     Spatial analyses and app creation with Google Earth Engine and ArcGIS Pro
  •     3D with LiDAR, including data processing, and analyses
  •     Incorporating Machine Learning/Deep Learning frameworks in ArcGIS Pro
  •     Introductory fire/fuel modeling knowledge

Andrew Saah is a Master of Forestry Student in the Yale School of the Environment whose prior research and work experience broadly encompassed GIS and remote sensing applications in natural resource management. Specifically, he has an extensive background in working with various forest-based LiDAR data collected a comprehensive range of sensors spanning from phone based, to space based, and everything in between. He is mainly experienced in ArcGIS Pro and ArcGIS online web mapping, ArcPy, Cloud Compare, and Google Earth Engine as well as open-source GIS packages in Python like Geo-Pandas, open3d, etc. He also has a background training and using pre trained Machine Learning and Deep Learning models for 3D object detection, land classification and 3D point segmentation.   

Some of his past research has focused on landcover mapping using satellite imagery, forest structural modeling with airborne and space-based LiDAR data products, climate risk projections, and landscape scale fire risk modeling as well as fuel modeling. Beyond natural resource management, he has a strong foundation in both vector and raster data products for epidemiological studies, urban planning, humanitarian aid, and climate change risk projections according to differing RCP pathways. 

Andrew Saah is available at multiple locations.

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