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Raster foundations, forest layers and documented handoff.

Seven clearly scoped modules for existing and upstream UAV/GIS workflows.

01

Process existing geodata

Inputs
Orthomosaic, CHM, DSM/DTM, AOI, reference data
Processing
Review, clipping, normalization and validation
Outputs
Normalized raster/vector package
Partner value
Direct start with existing project assets
02

Optional raster foundations from UAV imagery

Inputs
JPG/TIFF, EXIF/GPS, MRK/RTK/NAV/OBS/OBK, mission structure
Processing
Data and metadata review; raster derivation where technically suitable
Outputs
Orthomosaic, DSM/DTM/CHM or COG where feasible
Partner value
One workflow even without finished raster products
03

Tree and crown layers

Inputs
Normalised CHM and/or RGB orthomosaic
Processing
Tree-point detection and crown delineation
Outputs
tree_points.gpkg and crown_candidates.gpkg
Partner value
Review-ready layers for QGIS and downstream work
04

QGIS-ready delivery packages

Inputs
Raster, vector and agreed output schema
Processing
Naming, layer structure and project assembly
Outputs
GPKG, COG, CSV, QGZ and QA summary
Partner value
Deliverables ready for client projects
05

QA, manifest and Evidence Pack

Inputs
Inputs, parameters, versions and processing steps
Processing
Checksums, lineage, QA and handoff documentation
Outputs
Manifest or complete Evidence Pack
Partner value
Traceable internal review and client documentation
06

Standardise partner workflows

Inputs
Recurring datasets and handoff rules
Processing
Output schemas, prioritisation and handoff definition
Outputs
Standard packages and S3/API-adjacent delivery
Partner value
Scalable production under your brand
07

Method stack for treetops and crown delineation

Inputs
Orthomosaic, CHM/elevation model, LiDAR/point cloud, control points or ground truth
Processing
Multiple AI-based, elevation-model-based and deterministic method paths can be combined and checked for plausibility depending on the dataset.
Outputs
Treetops, tree candidates and crown delineations as review-ready layers
Partner value
Dataset-specific method selection instead of a single-model black box

Dataset-specific processing

Not one model. A method stack.

Treetops, tree candidates and crown delineations are generated, compared and selected through multiple method paths depending on input data, quality and output scope — instead of applying one model blindly to every project.

Different data sources provide different signals: RGB/RGBA orthomosaics, CHMs or elevation models, LiDAR/point clouds, existing control points or ground-truth data. digitalforestry.ai does not treat these as a rigid single-model workflow, but as inputs for method paths that can be combined, compared and documented with QA notes depending on the project goal.

AI-based tree detectionCHM-based treetop detectionRGB/RGBA-based crown cuesLiDAR/elevation-model structure extractionGeometric crown modelsDeterministic segmentation methodsEnsemble and comparison logicQA and review rules
METHOD ROUTER Focus a signal or path

Input signals

Method paths

Review-ready outputs

Method & MRV add-ons

Additional modules for calibration, ground truthing and audit-oriented project records.

These modules extend forest layers with reproducible reference foundations, campaign planning, field-data handoffs, calibration metrics and structured evidence packages for later professional review.

i

Audit-oriented MRV preparation and ISO 14064-adjacent workflow support — not certification, official verification or guaranteed regulatory acceptance.

01

Reference DGM / DTM Package

An official reference DTM as a reproducible elevation foundation: sourcing, reprojection, raster harmonisation, mosaicking, QA/QC, checksums and an audit trail for a static reference surface from official base geodata.

Typical outputs
  • reference_dgm.cog.tif
  • source_manifest.csv
  • processing_log
  • method_report.json
  • qa_qc_report.json
  • checksums_manifest.csv
02

Calibration & Ground-Truthing Plan

A campaign-specific plan covering scope, strata, plot design, plot list, field SOP, roles, QA/QC, deviation handling and the Evidence Pack structure.

Typical outputs
  • calibration_plan.pdf
  • plots.csv
  • plots.geojson
  • field_sop.pdf
  • data_templates.zip
  • evidence_index.csv
03

Plot Design & Sampling Package

Stratified, reproducible plot generation from digital individual-tree data, including exclusion zones, minimum distances, scoring, parameters and plot maps.

Typical outputs
  • plots.csv
  • plots.geojson
  • plot_maps.pdf
  • sampling_parameters.json
  • sampling_report.pdf
04

Field Data Handoff Package

Templates and a handoff structure for DBH and height measurements, equipment logs, photo logs, track logs and deviation records.

Typical outputs
  • trees_measured.csv
  • equipment_log.csv
  • photo_log.csv
  • deviations.csv
  • field_checklist.pdf
05

Calibration Report Package

A calibration report with plot-level comparison, bias, MAE/RMSE, uncertainty, calibration parameters and documented limitations.

Typical outputs
  • calibration_report.pdf
  • metrics_tables.csv
  • uncertainty_summary.pdf
  • calibration_parameters.json
06

Audit Pack / Evidence Index

An audit-oriented project record with chain of custody, checksums, QA/QC reports, processing logs, method reports, deviations and handoff documentation.

Typical outputs
  • evidence_index.csv
  • chain_of_custody.md
  • checksums_manifest.csv
  • qa_qc_reports/
  • method_reports/
  • handoff_readme.md

Processing workflow

From data intake to delivery.

Six concise stages, including optional raster generation and documented handoff.

01

INPUT

Data intake

Raster, vector, imagery, sidecar and AOI assets are received in a structured package.

02

RASTER

Optional: raster foundation

UAV imagery is reviewed for suitable orthomosaic, elevation-model and COG outputs.

03

NORMALIZE

Data normalization & validation

CRS, NoData, units, resolution and file structure are normalised and validated.

04

PROCESS

AI/geospatial processing

Tree points, crown polygons and structure layers are processed with defined parameters.

05

EVIDENCE

QA, manifest & evidence pack

Processing steps, checksums, parameters and outputs are documented for your project context.

06

DELIVER

Partner-branded delivery

QGIS-ready package using the agreed output schema and handoff process.

Premium processing evidence

Deliver more than layers. Deliver traceability.

The optional evidence pack combines input references, checksums, processing steps, parameters, versions, outputs, QA notes and handoff documentation into a traceable project record.

01 Manifest

Technical run and output artifact.

→
02 Evidence Pack

For partners who need to document results for clients, internal QA or later review stages.

Add-on from €750 per projectPremium add-on
Discuss evidence pack
Chain of Evidence Explorer Evidence Pack
Evidence stage · 01

Reference input assets

Project assets, AOI, sidecars and handoff structure are linked to the processing run.

Included artifacts

✓input_assets.json
✓project_summary.md
Statusdocumented
ChecksumsQA packageReproducibilityClient handoff

QGIS · GPKG · COG · QGZ · JSON

Prepared for review, QA and professional delivery.

You remain in control of interpretation, approval and client delivery. We provide the documented processing layer.

QA

Interpretation, professional approval and client communication remain entirely within your process.

delivery / explorer
  • digitalforestry_delivery/
  • forest_layers.gpkg
  • canopy_height_model.cog.tif
  • tree_points.csv
  • crown_candidates.gpkg
  • qgis_project.qgz
  • processing_manifest.json
  • qa_summary.md

Start with real data

Start with real data.

Send a bounded dataset. We will assess which raster foundations, forest layers and documentation packages can support your partner offer.

Test your data Discuss evidence pack