DeepForest · Agentic Research Directory

Evidence & verification

Source level

Repository-documented
Grounded in a maintainer repository/README. Availability, stars and forks do not establish research benefit or correctness.

Outcome verification

Not empirically evaluated here
A documented resource or intended workflow, not a demonstrated academic outcome. No empirical benefit is established by its inclusion.

Sources checked

2026-10-15 (UTC)

Primary-source feature/documentation review only.[7] Performance, uptake and promotional claims are not treated as validated research gains.

Source-checked does not mean independently reproduced. See the Method page for how these labels are assigned.

Sources (1)

Research applicability

Open-source Python package training and predicting individual tree crowns from airborne RGB imagery, with a prebuilt NEON-trained model.[7]

Getting started

Begin from the weecology/DeepForest repository; install the Python package and use or retrain the prebuilt model with local annotated imagery.

Maintenance

Maintenance status not independently audited.

License

Not verified; consult upstream terms and dependency/data/model licences before adoption; no independent license audit performed.

Workflow & human oversight

Resource-level classification is intentionally unspecified; instantiated workflows may differ. Humans define objectives, control data access, and check outputs.

Workflow level is not an evidence score; higher is not better evidence.

Approach

Training and prediction pipelines detect individual tree crowns; the prebuilt model can be extended by annotating and training on local imagery.[7]

Limitations

Detection accuracy varies with imagery and forest structure; the prebuilt model requires local annotation and retraining. Documentation review only - no software execution or independent performance validation.

Reproducibility

Stored primary-source extract research-wide/candidate-evidence-076.md, accessed 2026-10-15, read for this review. No software, model or claimed outcome reproduced.

Adoption requirements

Georeferenced aerial imagery; Python; annotations for local adaptation.

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