Daniel Hedges Email & Phone Number

Seattle, Washington, US

Seattle, Washington, US

Developing GIS solutions that unlock efficiencies and drive informed decision-making. Projects developed in Python and implemented in client-facing GIS software.Projects include:- Deep learning feature extraction- Advanced interpolation methodology design- Creation of physics-based geometry projection tools

Bend, OR, US

Curriculum design and teaching of Geography 267: Geodatabase Design

Redlands, CA, US

• Design and optimize user-facing workflows in Python for creating and analyzing 3D geospatial data for cities and landscapes.
• Develop methods for automated feature extraction (using various segmentation methods, including Mask R-CNN within TensorFlow) from remotely-sensed data to inform 3D modeling of urban environments.
• Research use-cases and create demos for new and innovative utilization of 3D GIS analysis.
• Create and manage authoritative 3D content for consumption on ArcGIS Online

Amsterdam, North Holland, NL

• Developed manual and automated processes to meet complex data management needs with tight turnaround requirements.
• Evaluated large-scale spatial datasets for quality and continuity.
• Created reports explaining technical findings in laypersons terms to inform decision-making processes.

• Convert, compile, and prep large areas of remote sensing data in order to create a complete and congruent dataset ready for analysis.
• Develop batch routines for large‐scale GIS processing and data management.
• Create and implement a procedure for the quality control of LiDAR-derived hydrology datasets created for the Lake Tahoe basin watershed area.

Raleigh, NC, US

• Performed QA/QC procedures for transmission line LiDAR survey models, imagery, and CAD design sheets for major transmission operating companies across the United States.
• Tracked and reported progress along the data production process to ensure timely, high quality client deliverables.

• Develop and document Standard Operating Procedures and Specifications for the collection and classification of high-resolution aerial and terrestrial LiDAR data.
• Develop new procedures and algorithms for optimized efficiency and accuracy in production-scale LiDAR point cloud processing, as well as create new data products for electrical utility analysis and management.
• Manage multiple vendors, coordinate multiple steps in the data process flow, and control quality of data at various levels along the production process.
• Utilize standard procedures to classify raw LiDAR data collected from various scanner types and across a wide range of landscapes to produce actionable, accurate data models.

• Specialized in the interpretation of high-resolution imagery and LiDAR to identify a broad range of features across a variety of landscapes.
• Applied Standard Operating Procedures as part of a 14-person QA/QC team to create over 10,000 edits to spatial databases in ArcGIS.
• Converted and compiled various spatial (vector, raster, and LiDAR data sets) and tabular data into ArcGIS compatible formats.
• Documented spatial data sets using FGDC metadata standards.