{
"name": "Forrest DiPaola",
"occupation": "GIS / Data Scientist",
"likes": ["hiking", "trying new food"],
}
About Me
Forrest DiPaola (CV) is versed in data science, AI, mapping, geospatial analysis, and geographic principles. He is currently a Data Scientist at Kamu Data where he works in advanced data science techniques including GIS to help Kamu create their next generation cross-organizational data exchange for enterprise data pipelines.
He is also proficient in using a variety of geospatial tools, including ArcGIS Pro, ArcMap Desktop, QGIS and the GDAL Library. Moreover, he has experience using programming languages such as Python, SQL, and R. He has a Basic operation Small Remotely Piloted Aircraft System (RPAS), Visual line-of-sight (VLOS) certificate.
His Master's thesis from the Univ. of Aberdeen, won the Rihards Johansons Prizes for Best MSC in GIS Dissertation. It focused on predicting sea level rise for Vancouver using modern GIS methods and Deep Learning techniques. With his former professors, Forrest recently wrote a paper on future sea level rise for "Coastal Studies & Society."
My Work
Co-Authored Journal Paper in "Coastal Studies & Society"
Advanced LLM Knowledge Chatbot w/ RAG chaining
Forrest recently co-authored a paper that utilized advanced data analysis and machine learning, combined with innovative LiDAR GIS techniques.
The paper, titled "Methods and insights on enabling geovisualization for coastal communities…," was published in the journal "Coastal Studies and Society."
The research focused on developing new approaches to geovisualization for coastal communities, leveraging advanced data analysis, machine learning, and LiDAR GIS techniques.
The study aimed to improve understanding and decision-making related to coastal environments
The findings contribute to the field of coastal studies by offering new insights and methods for analyzing and visualizing coastal data, with implications for coastal community resilience and sustainability.
Leveraged Advanced LLM Chaining Techniques (Python, LangChain, Vector databases, RAG)
Improved Accuracy and Domain Specificity
Ensured Data Privacy and Security
Advanced RAG System on Einstein
Forrest's Master of Science thesis at the University of Aberdeen focused on predicting sea level rise for the North Shore of Vancouver.
He created a more precise, publicly accessible interactive online map using modern GIS methods, which included:
Using 1m resolution airborne LiDAR DEM with the Bathtub Method Model with Hydrologic Connectivity to determine inundation at 1-4 meters of sea level rise.
Employing deep learning techniques such as Convolutional Neural Networks, Artificial Neural Networks, and Markov Chains to predict land use/land cover of flooded areas and future land use change with predicted sea level rise.
Award Winning Dissertation
Primary Skills
Python
Erdas Imagine
Drone operator
ArcGIS Desktop
SQL
R
QGIS
PostgreSQL
ArcGIS Pro
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