{"name": "Whitebox Workflows for QGIS", "package_name": "whitebox_workflows_for_qgis", "version": "2.1.3", "experimental": false, "qgis_min": "3.28.0", "qgis_max": "4.99.0", "downloads": 3529, "uploaded_by": "jlindsay", "upload_datetime": "2026-06-14T12:11:24.767872", "changelog": "## 2.1.3 - 2026-06-14\r\n\r\n### Fixed\r\n- Fixed classifier tools showing a file-picker text field for the class label field parameter instead of a dropdown selector. Affected tools include `knn_classification`, `logistic_regression`, `random_forest_classification`, `fuzzy_knn_classification`, `nnd_classification`, `min_dist_classification`, and `parallelepiped_classification`. The parameter schema for `class_field` (and similar attribute field parameters) was incorrectly typed as a LiDAR file input, causing the QGIS plugin to render a file picker. Additionally, the parameter type-inference pre-pass was using only heuristic inference rather than explicit schema kinds, causing `training_data` vectors to be misclassified. Both issues are now resolved: schema kinds take precedence over heuristics, and attribute field parameters are correctly promoted to `QgsProcessingParameterField` dropdowns. Users can now select from available fields in the training layer without manual text entry.\r\n- Fixed `obia_pipeline_basic` and `classify_objects_random_forest`, `classify_objects_svm`, and `classify_objects_ensemble_pro` OBIA tools incorrectly treating `training` and `class_field` parameters as LiDAR inputs. These tools now correctly render `training` as a CSV/Table file picker and `class_field` as a field name text input.\r\n- Added curated parameter descriptions for `random_forest_classification_fit` and `random_forest_regression_fit` tools in the QGIS parameter metadata, providing users with clear guidance on band ordering, scaling options, and model usage.", "external_deps": null, "download_url": "https://plugins.qgis.org/plugins/whitebox_workflows_for_qgis/version/2.1.3/download/"}