CV

Experience, education, skills, and publications.

Download PDF

Summary

Geospatial data scientist and remote sensing specialist with over ten years of industry experience, from field campaigns and imagery analytics in precision agriculture to machine learning for clients across insurance, energy, agriculture and environmental monitoring. I take projects end to end: framing the problem with clients, combining satellite, aerial, field and signals data into workflows that answer it, and delivering documented code and clear results. I’m most motivated by work where open, scalable earth observation tools help solve real-world problems.

Experience

Write-ups of selected projects are on the Projects page.

Owner, GL Spatial

Alberta, Canada · Mar 2021 – present

  • Independent remote sensing and geospatial consulting, delivering custom machine learning workflows with documented code and sample datasets.
  • Recent work: detecting thermal anomalies over geothermal areas from multi-sensor satellite data (MODIS, Landsat, Sentinel).
  • Earlier work: deep learning detection of electrical substations in satellite imagery, and hyperspectral proofs of concept from crop classification to oil spill and roof material mapping.

Senior Data Scientist, Sparkgeo

Prince George, BC (Remote) · Jul 2021 – Aug 2026

  • Ran data science projects end to end, from client discovery calls through to delivered proofs of concept, across optical, SAR and hyperspectral imagery, mobility data and other signals data.
  • Built machine learning models for imagery and signals data, including foundation-model embeddings for Arctic sea ice mapping, object detection and segmentation in satellite and aerial imagery, and place-of-interest status models.
  • Built data pipelines and multi-source analyses, including scheduled Airflow and Spark pipelines, automated quality checks of vendor mobility datasets, and multi-hazard risk assessment for US properties.
  • Mentored junior data scientists and interns, and advised company leadership.

Farmers Edge

Lethbridge, AB · Jul 2016 – Jul 2021

Remote Sensing Manager · Feb 2020 – Jul 2021

  • Led planning and development of imagery analytics for the company’s SaaS precision agriculture platform.
  • Managed and mentored a team of remote sensing scientists (hiring, training, performance reviews), working with development teams in an agile environment.

Remote Sensing Scientist → Senior Remote Sensing Scientist · Jul 2016 – Feb 2020 (promoted Jul 2018)

  • Led development of agricultural imagery analytics, including an automated crop health change alerting system and its hail damage extension.
  • Led field research campaigns for model calibration and validation, from planning and staffing through to data processing.

Casual Instructor, Precision Agriculture, Lethbridge College

Lethbridge, AB · Aug – Dec 2019

  • Developed and taught lectures, GIS labs, and assessments for a class of 38 students.

Earlier roles

  • Geospatial Analyst, Integrated Sustainability Consultants, Calgary (2015). UAV photogrammetry, GIS mapping and Python image processing for oil and gas water management projects.
  • Research Assistant, Alberta Terrestrial Imaging Centre, Lethbridge (2013). Field spectroscopy, crop measurements and UAV imaging for a food security project.
  • Hyperspectral Research Assistant, Canada Centre for Mapping and Earth Observation, Ottawa (2012). Hyperspectral image pre-processing and lab spectroscopy for mineral detection, mine reclamation and oil sands projects.
  • Remote Sensing Technician, Alberta Terrestrial Imaging Centre, Lethbridge (2011). Wetland mapping from satellite imagery, land cover classification and field data collection.

Education

M.Sc., Geography, University of Lethbridge · 2013 – 2016 · GPA 4.0
Thesis: The impact of spatial resolution on riparian leaf area index modelling using remote sensing.

B.Sc., Geography (GIS concentration), University of Lethbridge · 2009 – 2013 · GPA 3.98
Graduated with Great Distinction.

Publications

  • Logie, G. S. J., & Coburn, C. A. (2018). An investigation of the spectral and radiometric characteristics of low-cost digital cameras for use in UAV remote sensing. International Journal of Remote Sensing, 39(15–16), 4891–4909. doi:10.1080/01431161.2018.1488297
  • Coburn, C. A., Smith, A. M., Logie, G. S., & Kennedy, P. (2018). Radiometric and spectral comparison of inexpensive camera systems used for remote sensing. International Journal of Remote Sensing, 39(15–16), 4869–4890. doi:10.1080/01431161.2018.1466085
  • Coburn, C. A., & Logie, G. S. J. (2017). Temporal dynamics of sand dune bidirectional reflectance characteristics for absolute radiometric calibration of optical remote sensing data. Journal of Applied Remote Sensing, 12(1), 012006. doi:10.1117/1.JRS.12.012006
  • Flanagan, L. B., Orchard, T. E., Logie, G. S. J., Coburn, C. A., & Rood, S. B. (2017). Water use in a riparian cottonwood ecosystem: Eddy covariance measurements and scaling along a river corridor. Agricultural and Forest Meteorology, 232, 332–348. doi:10.1016/j.agrformet.2016.08.024

Conference presentations

  • Logie, G. (2025). Monitoring water levels in Canadian lakes and reservoirs using the Surface Water and Ocean Topography (SWOT) mission [Poster]. Canadian Symposium on Remote Sensing (CSRS) 2025. Poster PDF
  • Logie, G. (2022). Mapping fire burn severity and extent in British Columbia watersheds [Presentation]. Canadian Symposium on Remote Sensing (CSRS) 2022. Project page
  • Logie, G. (2022). A tool for mapping fire burn severity and extent in watersheds for flood risk assessment [Presentation]. FOSS4G 2022, Florence, Italy. Talk page · Project page

Skills

  • Languages: Python, SQL; familiarity with JavaScript, R, MATLAB, IDL.
  • Machine learning: PyTorch, PyTorch Lightning, TensorFlow/Keras, scikit-learn, XGBoost, Hugging Face Transformers, Ultralytics (YOLO). Object detection, semantic segmentation, vision transformers, geospatial foundation models and embeddings.
  • Geospatial: GDAL, Rasterio, GeoPandas, Shapely, H3, TorchGeo, Xarray; COG, Zarr, GeoParquet, STAC; QGIS, ArcGIS Pro, Google Earth Engine, Microsoft Planetary Computer, ENVI, ESA SNAP.
  • Data and cloud: pandas, Polars, DuckDB, PostgreSQL/PostGIS, Snowflake, Apache Spark, Apache Iceberg, Apache Airflow, AWS (S3, Batch, Lambda, SageMaker), Databricks, Docker, MLflow.
  • Visualization and reporting: Matplotlib, Seaborn, Plotly, Quarto.
  • AI-assisted development: Claude Code, GitHub Copilot, Gemini; local open-weight models (Llama, Qwen).
  • Remote sensing: multispectral, hyperspectral, thermal, SAR and lidar data from satellite, airborne and UAV platforms; atmospheric correction, sensor calibration, time series analysis, spectral unmixing.
  • Field: campaign design and coordination, field spectroscopy, RTK GPS surveying, crop biophysical measurements.

Banner image: Short wave infrared composite image of the Agan River Wetlands in Siberia, Russia, which have been polluted by oil and gas extraction. Captured by Sentinel-2 on May 21, 2024. Image source