The challenge

Critical geospatial work is too often trapped in manual steps, brittle scripts, and disconnected tools.

Tellus focuses on the operational middle ground where GIS expertise, scientific computing, data engineering, and applied AI need to work together.

Repetitive analysis consumes expert time

Recurring GIS tasks, QA checks, reporting steps, and data preparation routines slow down teams that should be focused on interpretation.

Legacy tools are hard to maintain

FORTRAN, MATLAB, desktop scripts, and one-off models often carry valuable logic without a clear path into modern operations.

Spatial data keeps getting larger

LiDAR, DEMs, rasters, environmental records, and Earth observation products demand scalable pipelines, not manual file handling.

AI needs practical implementation

Technical teams need decision-support systems that are explainable, validated, and connected to their real workflows.

What Tellus builds

Modern systems for complex spatial and engineering data.

Geospatial Workflow Automation

Custom ArcGIS, Python, and geoprocessing tools that replace repetitive manual analysis with reliable, repeatable workflows.

ArcGIS Pro toolboxes, Python automation, spatial QA, batch processing

Spatial Data Pipelines

Processing systems for LiDAR, DEM, raster, vector, time-series, and environmental datasets that can scale beyond local desktop workflows.

Cloud storage, APIs, Earth Engine, raster processing, data validation

Legacy Code Modernization

Migration of scientific models, MATLAB routines, FORTRAN code, and fragile scripts into maintainable Python systems.

Model translation, testing, documentation, reproducible execution

AI-Assisted Decision Tools

Applied AI systems that help teams classify, summarize, prioritize, QA, and act on complex technical data.

Human-in-the-loop workflows, decision support, computer vision, review tools

How we work

Start with the workflow, then build the right system around it.

Tellus begins by understanding the analysis process, data sources, operational constraints, and decision points. The result is software that fits the team, not a generic platform pushed into service.

  1. Diagnose the workflow Map the manual steps, data dependencies, legacy logic, bottlenecks, and quality checks.
  2. Design the modernization path Choose the right mix of automation, Python tooling, cloud processing, GIS integration, and AI.
  3. Build and validate Develop maintainable tools, test outputs against existing methods, and document the system.
  4. Deliver operational capability Hand off workflows that teams can run, inspect, scale, and improve over time.
Dr. Zhifei Dong

Founder expertise

Led by a coastal engineer and geospatial data scientist who builds the tools behind the analysis.

Dr. Zhifei Dong brings deep experience across coastal engineering, geospatial data science, and GIS software development. His work spans storm surge, wave and sediment transport modeling, coastal resilience, restoration planning, LiDAR processing, machine learning, and ArcGIS Python toolbox development.

He is the first author of the Coastal Engineering Resilience Index (CERI) and an award-winning ArcGIS geoprocessing expert, with broad experience supporting technical work for state and federal agencies including USACE, EPA, FDEP, GOMA, and CPRA.

Ph.D. Ocean Engineering, University of Delaware M.S. Information and Data Science, UC Berkeley Coastal modeling, resilience, and decision-support systems Python, ArcGIS, Delft3D, SWAN, MATLAB, FORTRAN, R, AI

Use cases

Bring Tellus the workflow that is too slow, too manual, or too hard to scale.

Automating recurring spatial analysis and reporting Migrating MATLAB or FORTRAN models to Python Processing large LiDAR, DEM, raster, or point cloud datasets Connecting ArcGIS Pro workflows with cloud analytics Building Earth Engine and ArcGIS integration tools Creating AI-assisted QA, review, and prioritization systems Developing internal tools for engineering and environmental teams Turning fragmented data sources into decision-ready pipelines

Clients and partners

Trusted experience across engineering, government, geospatial, and cloud ecosystems.

ERDC I-ATLAS Woolpert Google Earth Engine

Example work

Project highlights built around real technical operations.

JALBTCX Toolbox modules

JALBTCX Toolbox

A scalable LiDAR processing toolbox for volume calculation, feature extraction, resilience quantification, and visualization across hundreds of gigabytes of data.

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LASAAP geospatial decision-support tool

LASAAP Toolbox

A sediment evaluation and management tool for ecosystem restoration in Louisiana, integrating multiple databases and regulations for data-driven decisions.

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ArcGIS Earth Engine Toolbox interface

ArcGIS Earth Engine Toolbox

An ArcGIS Pro toolbox for exploring, downloading, uploading, processing, and managing Google Earth Engine data from familiar GIS workflows.

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Python ArcGIS Pro Google Earth Engine LiDAR & Remote Sensing Cloud Computing AI & Automation

Who we serve

Built for organizations where spatial data, engineering judgment, and operational reliability all matter.

Engineering firms Government agencies Geospatial organizations Environmental consultants Research teams

Start a conversation

Have a workflow, model, dataset, or analysis process that needs to work better?

Tellus can help identify what should be automated, modernized, scaled, or connected to AI-enabled decision support.

Book a Discovery Call