Analyze
Data we collect, and data brought to us, analyzed with GIS pipelines, statistical methods, and deep-learning tools where they fit the task.
Data Analysis
The endpoint is a scientific result, not a data product: spatial analysis, statistics, and machine learning applied to research questions, delivered with code and methods documentation.
GIS workflows
ArcGIS Pro and QGIS for spatial overlays, watershed analysis, change detection, terrain analysis, vector editing, and map production.
Statistical analysis
Time-series analysis of repeat surveys, uncertainty quantification, spatial statistics. Reproducible Python and R pipelines, version-controlled, delivered with raw data, code, and methods documentation.
Deep learning
Containerized deep-learning models applied where they outperform classical methods. Build recipes are version-controlled, so a run this year and a run next year produce the same numbers.
Lidar segmentation
Models in deployment focus on forest applications: individual-tree instance segmentation from UAV / airborne lidar (SegmentAnyTree, ForAINet) and ground-based lidar / TLS / MLS (TreeLearn, FSCT, PointsToWood). Classical baselines (AMS3D, 3DFin) sit alongside the DL models for stem detection and DBH.
Imagery analysis
Object detection and segmentation in 2D imagery from any source — aerial, drone, satellite, handheld, and camera-trap stills. SAM 2 (with samgeo for georeferenced raster I/O) handles general-purpose promptable segmentation. DeepForest specializes in tree-crown detection from aerial RGB.
Remote-sensing foundation models
Pre-trained backbones for Sentinel-1/2, Landsat, NAIP, MODIS, and UAV RGB imagery — used for zero-shot scene classification, embedding-based similarity search, and transfer learning to project-specific tasks with small label budgets. Models: Clay, Prithvi-EO (IBM/NASA), Satlas (Allen AI), RemoteCLIP.
Domain-specific applied models
Animal detection in drone thermal video; seismic phase picking (SeisBench: PhaseNet, EQTransformer); LSTM rainfall-runoff prediction (NeuralHydrology); powder-XRD multi-phase identification (autoXRD).
Data labeling
Training labels for imagery and point-cloud projects are managed in the lab's hosted Label Studio instance; labeling workflows are covered in the lab documentation.
Have an analysis question?
Bring the research question — we'll scope the GIS, statistical, or deep-learning approach that fits it. Initial scoping conversations are free — see Access & Rates for how projects are priced.
Contact the Lab