Repository Analysis

opengeos/geoai

GeoAI: Artificial Intelligence for Geospatial Data

41.7 Strong AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of opengeos/geoai, a Python project with 3,178 GitHub stars. SynthScan v2.0 examined 126,135 lines of code across 308 source files, recording 3319 pattern matches distributed across 19 syntactic categories. The overall adjusted score of 41.7 places this repository in the Strong AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

41.7
Adjusted Score
41.7
Raw Score
100%
Time Factor
2026-07-13
Last Push
3.2K
Stars
Python
Language
126.1K
Lines of Code
308
Files
3.3K
Pattern Hits
2026-07-14
Scan Date
0.54
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

This chart maps the temporal evolution of the adjusted synthetic code score across successive scan runs. An upward trajectory indicates ongoing incorporation of AI-generated code or expanding LLM-assisted scaffolding; a stable or declining trajectory may reflect active human refactoring, code removal, or the adoption of stricter authorship policies. The dashed secondary line (right axis) independently tracks total raw pattern hit count, which can diverge from the normalised score when codebase size changes significantly between scans.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 8HIGH 158MEDIUM 610LOW 2543

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 3319 distinct pattern matches across 19 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Decorative Section Separators502 hits · 1688 pts
SeverityFileLineSnippetContext
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py640# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py642# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py1120# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py1122# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py34# ---------------------------------------------------------------------------COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py36# ---------------------------------------------------------------------------COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py55# ---------------------------------------------------------------------------COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py57# ---------------------------------------------------------------------------COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py116# ---------------------------------------------------------------------------COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py118# ---------------------------------------------------------------------------COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py160# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py162# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py203# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py205# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py319# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py321# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py374# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py376# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py412# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py414# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py475# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py477# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py767# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py769# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py850# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py852# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py901# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py903# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py969# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py971# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py1025# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py1027# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py1040# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/geoai_cli.py1042# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py87# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py89# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py407# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py409# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py441# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py443# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py470# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py472# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py503# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py505# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py28# ---------------------------------------------------------------------------COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py30# ---------------------------------------------------------------------------COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py185# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py187# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py299# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py301# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py368# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py370# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_full_e2e.py89# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_full_e2e.py91# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_full_e2e.py174# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_full_e2e.py176# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_full_e2e.py341# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_full_e2e.py343# ═══════════════════════════════════════════════════════════════════════════COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_full_e2e.py23# ---------------------------------------------------------------------------COMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_full_e2e.py25# ---------------------------------------------------------------------------COMMENT
442 more matches not shown…
Hyper-Verbose Identifiers1110 hits · 1123 pts
SeverityFileLineSnippetContext
LOWagent-harness/cli_anything/geoai/geoai_cli.py634def segment_list_architectures():CODE
LOWagent-harness/cli_anything/geoai/core/vector.py88def _get_vector_info_geopandas(path: str) -> Dict[str, Any]:CODE
LOWagent-harness/cli_anything/geoai/core/segment.py148def run_semantic_segmentation(CODE
LOWagent-harness/cli_anything/geoai/core/raster.py183def _get_raster_info_rasterio(path: str) -> Dict[str, Any]:CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py95 def test_create_project_defaults(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py104 def test_create_project_custom(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py397 def test_rasterize_vector_missing_file(self, sample_raster, tmp_dir):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py434 def test_list_sources_contains_naip(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py464 def test_run_sam_missing_file(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py493 def test_run_detection_missing_file(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py119 def test_open_project_missing_file(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py124 def test_open_project_invalid_json(self, tmp_dir):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py165 def test_add_file_auto_detect(self, sample_raster):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py206 def test_session_get_project_no_project(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py212 def test_session_snapshot_and_undo(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py272 def test_session_max_undo_limit(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py285 def test_session_save_and_load(self, tmp_dir):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py314 def test_get_raster_info_missing_file(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py329 def test_get_raster_stats_bad_band(self, sample_raster):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py382 def test_get_vector_info_missing_file(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_core.py519 def test_detect_changes_missing_file(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py101 def test_raster_info_real_file(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py182 def test_project_create_add_save_reload(self, sample_raster, tmp_dir):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py290 def test_cli_raster_info_json(self, sample_raster):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py297 def test_cli_raster_stats_json(self, sample_raster):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py305 def test_cli_project_new_json(self, tmp_dir):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py316 def test_cli_system_info_json(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py324 def test_cli_session_status_json(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py331 def test_cli_segment_list_models(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py368 def test_raster_analysis_pipeline(self, sample_raster, tmp_dir):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py399 def test_project_management_workflow(self, tmp_dir):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py423 def test_data_discovery_workflow(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py112 def test_raster_stats_all_bands(self):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py129 def test_raster_tile_and_count(self, sample_raster, tmp_dir):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py142 def test_raster_vectorize_real(self, tmp_dir):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py203 def test_project_session_undo_redo(self, sample_raster):CODE
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py222 def test_project_info_after_multiple_files(self, sample_raster, tmp_dir):CODE
LOWgeoai/foundation_models.py592def get_foundation_model_info(name: str) -> Dict[str, Any]:CODE
LOWgeoai/foundation_models.py620def check_terratorch_available() -> bool:CODE
LOWgeoai/change_detection.py1020 def create_comprehensive_report(CODE
LOWgeoai/change_detection.py26def _normalize_change_confidence(CODE
LOWgeoai/change_detection.py112 def _get_change_confidence_threshold(self) -> float:CODE
LOWgeoai/change_detection.py184 def set_mask_generator_params(CODE
LOWgeoai/change_detection.py1261 def _save_instance_segmentation_masks(CODE
LOWgeoai/change_detection.py1356 def _save_instance_scores_mask(CODE
LOWgeoai/change_detection.py1496 def _extract_detailed_results(CODE
LOWgeoai/download.py244def get_overture_latest_release(patch=True) -> str:CODE
LOWgeoai/download.py300def download_overture_buildings(CODE
LOWgeoai/rfdetr.py117def _is_rfdetr_segmentation_variant(model_variant: str) -> bool:CODE
LOWgeoai/rfdetr.py122def _normalize_detection_masks(CODE
LOWgeoai/rfdetr.py144def _mask_to_georeferenced_geometry(CODE
LOWgeoai/onnx.py1072def onnx_semantic_segmentation(CODE
LOWgeoai/onnx.py1131def onnx_image_classification(CODE
LOWgeoai/timm_regress.py81def _prepare_normalization_stats(CODE
LOWgeoai/timm_regress.py110def _infer_preprocessing_params(CODE
LOWgeoai/timm_regress.py1101def plot_regression_comparison(CODE
LOWgeoai/timm_regress.py1664def predict_with_timm_regressor(*args, **kwargs):CODE
LOWgeoai/timm_regress.py1672def get_timm_regression_model(*args, **kwargs):CODE
LOWgeoai/timm_segment.py629def train_timm_segmentation_model(CODE
LOWgeoai/timm_segment.py835def timm_semantic_segmentation(CODE
1050 more matches not shown…
Docstring Block Structure141 hits · 705 pts
SeverityFileLineSnippetContext
HIGHagent-harness/cli_anything/geoai/core/vector.py12Get metadata for a vector file. Args: path: Path to the vector file. Returns: Dict with featurSTRING
HIGHagent-harness/cli_anything/geoai/core/vector.py43Convert a vector file to a raster using a template for resolution/extent. Args: path: Input vector file patSTRING
HIGHagent-harness/cli_anything/geoai/core/classify.py22Train an image classification model. Args: train_dir: Training data directory (subdirectories are class namSTRING
HIGHagent-harness/cli_anything/geoai/core/classify.py94Classify an image with a trained model. Args: image_path: Path to the input image. model_path: PathSTRING
HIGHagent-harness/cli_anything/geoai/core/session.py171Persist the current project to disk. Args: path: Output path. Defaults to project_path. ReSTRING
HIGHagent-harness/cli_anything/geoai/core/segment.py50Run SAM (Segment Anything Model) on a raster. Args: raster: Input raster file path. output: Output STRING
HIGHagent-harness/cli_anything/geoai/core/segment.py101Run GroundedSAM text-prompted segmentation. Args: raster: Input raster file path. output: Output maSTRING
HIGHagent-harness/cli_anything/geoai/core/segment.py157Run semantic segmentation with a trained model. Args: raster: Input raster file path. model_path: PSTRING
HIGHagent-harness/cli_anything/geoai/core/segment.py230Train a semantic segmentation model. Args: image_root: Directory with training images. label_root: STRING
HIGHagent-harness/cli_anything/geoai/core/raster.py12Get metadata for a raster file. Args: path: Path to the raster file. Returns: Dict with CRS, sSTRING
HIGHagent-harness/cli_anything/geoai/core/raster.py38Compute statistics for a raster band. Args: path: Path to the raster file. band: Band number (1-indSTRING
HIGHagent-harness/cli_anything/geoai/core/raster.py89Split a raster into tiles. Args: path: Input raster path. output_dir: Output directory for tiles. STRING
HIGHagent-harness/cli_anything/geoai/core/raster.py146Convert a raster to vector polygons. Args: path: Input raster path. output: Output vector file pathSTRING
HIGHagent-harness/cli_anything/geoai/core/detect.py34Run object detection on a raster image. Args: raster: Input raster file path. model_path: Path to tSTRING
HIGHagent-harness/cli_anything/geoai/core/detect.py119Train an object detection model. Args: images_dir: Directory with training images. labels_dir: DireSTRING
HIGHagent-harness/cli_anything/geoai/core/change.py21Run change detection between two temporal images. Args: image1: Path to the earlier image. image2: STRING
HIGHagent-harness/cli_anything/geoai/core/data.py151Parse a comma-separated bounding box string. Args: bbox_str: Bounding box as "minx,miny,maxx,maxy". ReSTRING
HIGHagent-harness/cli_anything/geoai/core/project.py43Load a project from a JSON file. Args: path: Path to the project JSON file. Returns: Project dSTRING
HIGHagent-harness/cli_anything/geoai/core/project.py144Add a file reference to the project. Args: project: Project dict. path: Path to the file. fSTRING
HIGHagent-harness/cli_anything/geoai/core/project.py187Remove a file entry by index. Args: project: Project dict. index: Index in the files list. RetSTRING
HIGHagent-harness/cli_anything/geoai/core/project.py225Add a processing result to the project. Args: project: Project dict. result_type: Type of result (sSTRING
HIGHagent-harness/cli_anything/geoai/core/project.py268Detect file type from extension. Args: path: File path. Returns: File type string. RaisesSTRING
HIGHgeoai/dinov3_finetune.py67Return the intermediate layer indices for a given ViT depth. Args: num_blocks: Total number of transformer STRING
HIGHgeoai/ftw.py62Download the Fields of The World (FTW) dataset for specified countries. Downloads Sentinel-2 imagery and instance sSTRING
HIGHgeoai/ftw.py164Prepare FTW data for training with geoai's instance segmentation pipeline. Rescales Sentinel-2 reflectance images fSTRING
HIGHgeoai/foundation_models.py500List available remote sensing foundation models. Args: category: Filter by model category. One of ``"visionSTRING
HIGHgeoai/foundation_models.py593Get detailed metadata for a specific foundation model. Args: name: Registry key for the model (e.g. ``"pritSTRING
HIGHgeoai/foundation_models.py642Load a foundation model backbone via TerraTorch. Downloads pre-trained weights from HuggingFace and returns a `STRING
HIGHgeoai/water.py161Segment water bodies from satellite or aerial imagery using OmniWaterMask. Uses a sensor-agnostic deep learning modSTRING
HIGHgeoai/change_detection.py1883Read and align two GeoTIFF images by their spatial overlap. Args: image1_path (str): Path to the fiSTRING
HIGHgeoai/classify.py359 Classify a geospatial image using a trained semantic segmentation model. The version has tile edge artifactSTRING
HIGHgeoai/classify.py600 Classify a geospatial image using a trained semantic segmentation model. This function handles the full image STRING
HIGHgeoai/classify.py845 Classify multiple geospatial images using a trained semantic segmentation model. This function accepts either STRING
HIGHgeoai/download.py36Download NAIP imagery from Planetary Computer based on a bounding box. This function searches for NAIP (National AgSTRING
HIGHgeoai/download.py245 Retrieves the value of the 'latest' key from the Overture Maps release JSON file. Args: patch (bool): STRING
HIGHgeoai/download.py329Fetches overture data and returns it as a GeoDataFrame. Args: overture_type (str): The type of overture datSTRING
HIGHgeoai/download.py486 Downloads a STAC item from Microsoft Planetary Computer with specified bands. This function fetches a STAC iteSTRING
HIGHgeoai/download.py735 Retrieves and displays the list of available collections from Planetary Computer. This function connects to thSTRING
HIGHgeoai/download.py866 Search for STAC items in the Planetary Computer catalog. This function queries the Planetary Computer STAC APISTRING
HIGHgeoai/download.py955 Download assets from STAC items retrieved from the Planetary Computer. This function downloads specified assetSTRING
HIGHgeoai/download.py1195 Visualize a STAC item from the Planetary Computer on an interactive map. This function allows users to displaySTRING
HIGHgeoai/download.py1290 Visualize multiple STAC items from the Planetary Computer on an interactive map. This function allows users toSTRING
HIGHgeoai/rfdetr.py208Get the RF-DETR model class for a given variant name. Args: model_variant: Name of the model variant (e.g.,STRING
HIGHgeoai/onnx.py117Export a PyTorch / Hugging Face model to ONNX format. Args: model_name_or_path: Hugging Face model name or STRING
HIGHgeoai/canopy.py1075Predict canopy height from a GeoTIFF image. Processes the input image in tiles of the specified size, runs STRING
HIGHgeoai/caption.py87Load the nested aerial_features.json and flatten to a list of feature keys. Results are cached after the first succSTRING
HIGHgeoai/caption.py173Load a PIL image from various sources. Supports loading from local file paths, HTTP(S) URLs, or returns the imaSTRING
HIGHgeoai/clip_classify.py141Classify each polygon feature using zero-shot CLIP inference. For every polygon in *vector_data*, extracts the STRING
HIGHgeoai/clip_classify.py430Classify vector polygon features using zero-shot CLIP inference. Convenience function that creates a :class:`CLIPVeSTRING
HIGHgeoai/networks.py40Simplify a road or street network with ``neatnet``. This function wraps :func:`neatnet.neatify` to convert transporSTRING
HIGHgeoai/moondream.py92Load the Moondream model. Args: revision: Model revision/checkpoint. compile_model: WheSTRING
HIGHgeoai/moondream.py236Load a GeoTIFF file and return a PIL Image with metadata. Args: source: Path to GeoTIFF file. STRING
HIGHgeoai/embeddings.py278Import and return the torchgeo dataset class by registry name. Args: name: Registry name (key from EMBEDDINSTRING
HIGHgeoai/embeddings.py320Load an embedding dataset by name. This is a unified factory function that instantiates the correct torchgeo emSTRING
HIGHgeoai/embeddings.py388Get detailed information about an embedding dataset. Args: name: Registry name of the dataset. ReturnsSTRING
HIGHgeoai/embeddings.py564Visualize high-dimensional embeddings in 2D using dimensionality reduction. Supports PCA, t-SNE, and UMAP for projeSTRING
HIGHgeoai/embeddings.py786Cluster embedding vectors using unsupervised methods. Args: embeddings: Array of shape ``(N, D)``. STRING
HIGHgeoai/embeddings.py1213Download Google/AlphaEarth satellite embeddings for a region. Downloads pre-computed 64-D satellite embedding vectoSTRING
HIGHgeoai/pipeline.py262Expand a single item into multiple items by globbing. Args: item: Dict with ``input_dir`` or ``inpuSTRING
HIGHgeoai/pipeline.py687Deserialize a step from a config dict. Args: d: Dict with ``type`` key and step parameters. Returns: STRING
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Excessive Try-Catch Wrapping470 hits · 429 pts
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LOWagent-harness/cli_anything/geoai/geoai_cli.py1110 except Exception as e:CODE
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LOWgeoai/map_widgets.py191 except Exception as e:CODE
LOWgeoai/map_widgets.py258 except Exception:CODE
LOWgeoai/map_widgets.py618 except Exception as e:CODE
LOWgeoai/map_widgets.py657 except Exception as e:CODE
MEDIUMgeoai/map_widgets.py148def handle_map_interaction(**kwargs):CODE
LOWgeoai/download.py135 except Exception as e:CODE
LOWgeoai/download.py428 except Exception as e:CODE
LOWgeoai/download.py471 except Exception as e:CODE
LOWgeoai/download.py720 except Exception as e:CODE
LOWgeoai/download.py760 except Exception as e:CODE
LOWgeoai/download.py766 except Exception as e:CODE
LOWgeoai/download.py900 except Exception as e:CODE
LOWgeoai/download.py939 except Exception as e:CODE
LOWgeoai/download.py1046 except Exception as e:CODE
LOWgeoai/download.py1096 except Exception as e:CODE
LOWgeoai/rfdetr.py958 except Exception as e:CODE
LOWgeoai/rfdetr.py1024 except Exception:CODE
LOWgeoai/onnx.py206 except Exception as exc:CODE
LOWgeoai/timm_regress.py121 except Exception:CODE
LOWgeoai/timm_regress.py182 except Exception as e:CODE
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LOWgeoai/timm_segment.py940 except Exception as e:CODE
LOWgeoai/timm_segment.py1237 except Exception as e:CODE
LOWgeoai/dinov3.py118 except Exception as e:CODE
LOWgeoai/dinov3.py157 except Exception as e:CODE
LOWgeoai/dinov3.py202 except Exception as e:CODE
LOWgeoai/caption.py71 except Exception as e:CODE
LOWgeoai/clip_classify.py333 except Exception:CODE
LOWgeoai/moondream.py182 except Exception as e:CODE
LOWgeoai/moondream.py297 except Exception as e:CODE
LOWgeoai/embeddings.py1422 except Exception as e:CODE
LOWgeoai/cli.py66 except Exception:CODE
LOWgeoai/cli.py78 except Exception as e:CODE
LOWgeoai/cli.py119 except Exception as e:CODE
LOWgeoai/cli.py170 except Exception as e:CODE
LOWgeoai/cli.py182 except Exception as e:CODE
LOWgeoai/cli.py215 except Exception as e:CODE
LOWgeoai/pipeline.py846 except Exception as e:CODE
LOWgeoai/pipeline.py950 except Exception as e:CODE
LOWgeoai/pipeline.py1029 except Exception as e:CODE
LOWgeoai/tessera.py639 except Exception:CODE
LOWgeoai/train.py1833 except Exception as e:CODE
LOWgeoai/train.py3014 except Exception as e:CODE
LOWgeoai/train.py3102 except Exception as e:CODE
LOWgeoai/train.py3651 except Exception as e:CODE
LOWgeoai/train.py3671 except Exception:CODE
LOWgeoai/train.py4333 except Exception as e:CODE
LOWgeoai/train.py4435 except Exception as e:CODE
LOWgeoai/train.py4515 except Exception as e:CODE
LOWgeoai/train.py4560 except Exception:CODE
LOWgeoai/train.py4762 except Exception as e:CODE
LOWgeoai/train.py5506 except Exception as e:CODE
LOWgeoai/train.py5659 except Exception as e:CODE
LOWgeoai/train.py5738 except Exception as e:CODE
LOWgeoai/train.py6088 except Exception as e:CODE
LOWgeoai/object_detect.py1498 except Exception as e:CODE
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Unused Imports318 hits · 256 pts
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LOWagent-harness/cli_anything/geoai/core/vector.py8CODE
LOWagent-harness/cli_anything/geoai/core/segment.py8CODE
LOWagent-harness/cli_anything/geoai/core/raster.py8CODE
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LOWagent-harness/cli_anything/geoai/utils/geoai_backend.py10CODE
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LOWgeoai/onnx.py90CODE
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LOWgeoai/geoai.py13CODE
LOWgeoai/geoai.py13CODE
LOWgeoai/geoai.py14CODE
LOWgeoai/geoai.py14CODE
LOWgeoai/geoai.py14CODE
LOWgeoai/geoai.py14CODE
LOWgeoai/geoai.py14CODE
LOWgeoai/geoai.py14CODE
LOWgeoai/geoai.py14CODE
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LOWgeoai/geoai.py14CODE
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LOWgeoai/geoai.py14CODE
LOWgeoai/geoai.py14CODE
LOWgeoai/geoai.py28CODE
LOWgeoai/geoai.py29CODE
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LOWgeoai/geoai.py32CODE
LOWgeoai/geoai.py32CODE
LOWgeoai/geoai.py32CODE
LOWgeoai/geoai.py32CODE
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LOWgeoai/geoai.py32CODE
LOWgeoai/geoai.py32CODE
LOWgeoai/geoai.py32CODE
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LOWgeoai/geoai.py32CODE
LOWgeoai/geoai.py32CODE
LOWgeoai/geoai.py32CODE
LOWgeoai/geoai.py51CODE
LOWgeoai/geoai.py52CODE
LOWgeoai/geoai.py53CODE
LOWgeoai/timm_regress.py18CODE
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LOWgeoai/dinov3.py28CODE
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Self-Referential Comments83 hits · 238 pts
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MEDIUMagent-harness/cli_anything/geoai/tests/test_core.py345 # Create a simple classified rasterCOMMENT
MEDIUMagent-harness/cli_anything/geoai/tests/test_full_e2e.py226 # Create a second rasterCOMMENT
MEDIUMgeoai/map_widgets.py248 # Create the mapCOMMENT
MEDIUMgeoai/classify.py137 # Create a wrapper class for albumentations to work with TorchGeo formatCOMMENT
MEDIUMgeoai/classify.py472 # Create a window that matches the original boundsCOMMENT
MEDIUMgeoai/classify.py773 # Create a mask that gives higher weight to central regionsCOMMENT
MEDIUMgeoai/classify.py870 # Import required librariesCOMMENT
MEDIUMgeoai/download.py59 # Create a geometry from the bounding boxCOMMENT
MEDIUMgeoai/download.py703 # Create a dictionary mapping band indices to band namesCOMMENT
MEDIUMgeoai/download.py922 # Create the search objectCOMMENT
MEDIUMgeoai/download.py1"""This module provides functions to download data, including NAIP imagery and building data from Overture Maps."""STRING
MEDIUMgeoai/dinov3.py106 # Define the repository ID and model filenameCOMMENT
MEDIUMgeoai/train.py695 # Create a dummy target with minimal required fieldsCOMMENT
MEDIUMgeoai/train.py1580 # Create a full dataset to determine image IDs, then splitCOMMENT
MEDIUMgeoai/train.py1786 # Initialize training variablesCOMMENT
MEDIUMgeoai/train.py4485 # Initialize tracking variablesCOMMENT
MEDIUMgeoai/train.py5362 # Create a simple affine transform (identity transform for pixel coordinates)COMMENT
MEDIUMgeoai/segment.py1"""This module provides functionality for segmenting high-resolution satellite imagery using vision-language models."""STRING
MEDIUMgeoai/segment.py273 # Create an empty maskCOMMENT
MEDIUMgeoai/sam.py340 # Create an output image with the same size as the input imageCOMMENT
MEDIUMgeoai/sam.py820 # Create an empty image to store the mask overlaysCOMMENT
MEDIUMgeoai/extract.py129 # Define the bounds of the datasetCOMMENT
MEDIUMgeoai/extract.py353 # Define the repository ID and model filenameCOMMENT
MEDIUMgeoai/extract.py597 # Create a slightly smaller bounding box to exclude edge regionsCOMMENT
MEDIUMgeoai/extract.py741 # Create a mask for this objectCOMMENT
MEDIUMgeoai/extract.py1418 # Create a rotated rectangle using OpenCV method (more robust than Shapely methods)COMMENT
MEDIUMgeoai/extract.py1482 # Create a copy to avoid modifying the originalCOMMENT
MEDIUMgeoai/extract.py1626 # Create a scaled transform for the resampled imageCOMMENT
MEDIUMgeoai/extract.py1634 # Create a scaled transformCOMMENT
MEDIUMgeoai/extract.py1802 # Create a simpler visualization focused just on a subset of objectsCOMMENT
MEDIUMgeoai/extract.py1816 # Define a window around this objectCOMMENT
MEDIUMgeoai/extract.py2708 # Create a ResNet50 backbone with modified input channelsCOMMENT
MEDIUMgeoai/extract.py2923 # Create a custom Sentinel-2 dataset classCOMMENT
MEDIUMgeoai/extract.py1"""This module provides a dataset class for object extraction from raster data"""STRING
MEDIUMgeoai/landcover_train.py1110 # Create the loss function with proper ignore_index supportCOMMENT
MEDIUMgeoai/hf.py137 # Create an empty mask to hold all the labelsCOMMENT
MEDIUMgeoai/hf.py141 # Create a dictionary to map labels to values and store scoresCOMMENT
MEDIUMgeoai/hf.py190 # Create a CSV colormap file with scores includedCOMMENT
MEDIUMgeoai/hf.py300 # Create a PIL Image from the numpy arrayCOMMENT
MEDIUMgeoai/hf.py329 # Create a mask raster with unique IDs for each maskCOMMENT
MEDIUMgeoai/hf.py374 # Create a record for the CSV - without geometry calculationCOMMENT
MEDIUMgeoai/hf.py437 # Create a record for the CSV - without geometry calculationCOMMENT
MEDIUMgeoai/agents/geo_agents.py605 # Create a callback handler that updates the status widgetCOMMENT
MEDIUMgeoai/agents/geo_agents.py1225 # Create a callback handler that updates the status widgetCOMMENT
MEDIUMgeoai/utils/vector.py313 # Create the plotCOMMENT
MEDIUMgeoai/utils/vector.py391 # Create a polygon from the boundsCOMMENT
MEDIUMgeoai/utils/vector.py394 # Create a GeoJSON featureCOMMENT
MEDIUMgeoai/utils/vector.py418 # Create the GeoJSON collectionCOMMENT
MEDIUMgeoai/utils/vector.py936 # Create a list of Shapely Polygon objects based on the provided coordinatesCOMMENT
MEDIUMgeoai/utils/vector.py939 # Create a GeoDataFrame with the Shapely Polygon objectsCOMMENT
MEDIUMgeoai/utils/download.py126 # Define the repository ID and model filenameCOMMENT
MEDIUMgeoai/utils/conversion.py65 # Create the transform matrixCOMMENT
MEDIUMgeoai/utils/conversion.py74 # Create a single time value or range if neededCOMMENT
MEDIUMgeoai/utils/visualization.py1256 # Create a polygon from the bounds (already in geo-coordinates)COMMENT
MEDIUMgeoai/utils/visualization.py1265 # Create a GeoJSON featureCOMMENT
MEDIUMgeoai/utils/geometry.py259 # Create a rectangular hull in rotated spaceCOMMENT
MEDIUMgeoai/utils/geometry.py445 # Define a custom function to calculate median intensityCOMMENT
MEDIUMgeoai/utils/geometry.py1527 # Create a simplified version to check number of verticesCOMMENT
MEDIUMgeoai/utils/geometry.py1604 # Create a new geometry with orthogonalized ringsCOMMENT
MEDIUMgeoai/utils/geometry.py1637 # Create the final GeoJSON structureCOMMENT
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Deep Nesting236 hits · 173 pts
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LOWagent-harness/cli_anything/geoai/geoai_cli.py78CODE
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LOWagent-harness/cli_anything/geoai/core/raster.py215CODE
LOWagent-harness/cli_anything/geoai/core/project.py267CODE
LOWgeoai/dinov3_finetune.py129CODE
LOWgeoai/dinov3_finetune.py851CODE
LOWgeoai/change_detection.py400CODE
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LOWgeoai/onnx.py104CODE
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LOWgeoai/tessera.py334CODE
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AI Structural Patterns131 hits · 112 pts
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LOWagent-harness/cli_anything/geoai/core/classify.py11CODE
LOWagent-harness/cli_anything/geoai/core/segment.py217CODE
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LOWgeoai/train.py5985CODE
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Redundant / Tautological Comments79 hits · 106 pts
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LOWgeoai/ftw.py138 # Check if the zip contained a single top-level directoryCOMMENT
LOWgeoai/classify.py884 # Check if any images were foundCOMMENT
LOWgeoai/download.py586 # Check if file exists and skip if overwrite is FalseCOMMENT
LOWgeoai/download.py624 # Check if merged file exists and skip if overwrite is FalseCOMMENT
LOWgeoai/timm_segment.py91 # Check if using a pure timm model from HF HubCOMMENT
LOWgeoai/dinov3.py282 # Check if it's a GeoTIFF fileCOMMENT
LOWgeoai/moondream.py171 # Set model to evaluation modeCOMMENT
LOWgeoai/moondream.py341 # Check if it's a GeoTIFFCOMMENT
LOWgeoai/train.py1808 # Check if it's a full checkpoint or just model weightsCOMMENT
LOWgeoai/train.py2039 # Check if window is validCOMMENT
LOWgeoai/train.py2287 # Check if window is validCOMMENT
LOWgeoai/train.py4337 # Check if all images have the same sizeCOMMENT
LOWgeoai/train.py5624 # Check if input directory existsCOMMENT
LOWgeoai/train.py6163 # Check if data directory contains imagesCOMMENT
LOWgeoai/recognize.py313 # Check if any subdirectory of *root* contains matching imagesCOMMENT
LOWgeoai/object_detect.py307 # Check if COCO JSON already existsCOMMENT
LOWgeoai/prithvi.py878 # Check if we need to pad to num_framesCOMMENT
LOWgeoai/prithvi.py930 # Check if input dimensions match model expectationsCOMMENT
LOWgeoai/landcover_utils.py301 # Check if tile has featuresCOMMENT
LOWgeoai/timm_train.py62 # Check if model existsCOMMENT
LOWgeoai/extract.py333 # Set model to evaluation modeCOMMENT
LOWgeoai/extract.py1575 # Check if raster file existsCOMMENT
LOWgeoai/extract.py1588 # Check if confidence column exists in the GeoDataFrameCOMMENT
LOWgeoai/extract.py2818 # Check if this is likely a Sentinel-2 productCOMMENT
LOWgeoai/auto.py155 # Check if this is a model that needs the full processor (text + image)COMMENT
LOWgeoai/auto.py269 # Check if GeoTIFFCOMMENT
LOWgeoai/auto.py752 # Check if URLCOMMENT
LOWgeoai/auto.py793 # Check if we need tiled processing (not for classification tasks)COMMENT
LOWgeoai/agents/catalog_tools.py163 # Check if boxes do NOT intersect, then negateCOMMENT
LOWgeoai/agents/geo_agents.py892 # Check if this is a search_items resultCOMMENT
LOWgeoai/utils/vector.py224 # Check if attribute existsCOMMENT
LOWgeoai/utils/vector.py308 # Check if attribute existsCOMMENT
LOWgeoai/utils/models.py29 # Check if file existsCOMMENT
LOWgeoai/utils/models.py40 # Check if it's a state_dict or a complete modelCOMMENT
LOWgeoai/utils/models.py121 # Check if it might be a UNetCOMMENT
LOWgeoai/utils/device.py40 # Print output in real-timeCOMMENT
LOWgeoai/utils/conversion.py202 input_is_3d = coords.ndim == 3 # Check if the input is a 3D arrayCODE
LOWgeoai/utils/visualization.py469 # Check if input is a GeoDataFrameCOMMENT
LOWgeoai/utils/visualization.py686 # Check if input is a GeoDataFrameCOMMENT
LOWgeoai/utils/geometry.py121 # Check if we need to add a right-angle vertexCOMMENT
LOWgeoai/utils/geometry.py59 # Check if we're dealing with a GeoDataFrameCOMMENT
LOWgeoai/utils/geometry.py224 # Check if building is close to orthogonalCOMMENT
LOWgeoai/utils/geometry.py1047 # Check if merging would result in 3 or 4 segments (potentially triangular)COMMENT
LOWgeoai/utils/geometry.py1143 # Check if lines are parallel or collinear (denominator close to zero)COMMENT
LOWgeoai/utils/geometry.py1154 # Check if the intersection point is within a reasonable distanceCOMMENT
LOWgeoai/utils/geometry.py1157 # Check if point (x,y) is near the line segment from (x1,y1) to (x2,y2)COMMENT
LOWgeoai/utils/geometry.py1170 # Check if the projection is near the segment, not just the infinite lineCOMMENT
LOWgeoai/utils/geometry.py1179 # Check if intersection is reasonably close to both line segmentsCOMMENT
LOWgeoai/utils/geometry.py1214 # Check if the intersection point is too far from either line segmentCOMMENT
LOWgeoai/utils/geometry.py1284 # Check if the intersection point is too far from either line segmentCOMMENT
LOWgeoai/utils/geometry.py1525 # Check if shape is triangular and if we want to avoid triangular shapesCOMMENT
LOWgeoai/utils/geometry.py1619 # Add the feature to the listCOMMENT
LOWgeoai/utils/geometry.py1728 # Check if the input data is emptyCOMMENT
LOWgeoai/utils/raster.py491 # Check if the window is validCOMMENT
LOWgeoai/utils/raster.py585 # Check if CRS is geographic (area in sq degrees, not sq meters)COMMENT
LOWgeoai/utils/raster.py804 # Set CRS to the CRS of the first GeoDataFrameCOMMENT
LOWgeoai/utils/raster.py876 # Check if vector data is emptyCOMMENT
LOWgeoai/utils/raster.py893 # Check if we have all required parametersCOMMENT
LOWgeoai/utils/training.py230 # Check if image is large enough for tilingCOMMENT
LOWgeoai/utils/training.py2445 # Check if class_value_field existsCOMMENT
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Modern Structural Boilerplate94 hits · 94 pts
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LOWagent-harness/cli_anything/geoai/core/session.py40 def set_project(self, project: Dict[str, Any], path: Optional[str] = None) -> None:CODE
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LOWgeoai/segment.py25__all__ = [CODE
LOWgeoai/segment.py32logger = logging.getLogger(__name__)CODE
LOWgeoai/sam.py24logger = logging.getLogger(__name__)CODE
LOWgeoai/sam.py510 def set_image(self, image: Union[str, np.ndarray], **kwargs: Any) -> None:CODE
LOWgeoai/prithvi.py23logger = logging.getLogger(__name__)CODE
LOWgeoai/prithvi.py25__all__ = [CODE
LOWgeoai/landcover_utils.py24logger = logging.getLogger(__name__)CODE
LOWgeoai/landcover_utils.py35__all__ = [CODE
LOWgeoai/timm_train.py29logger = logging.getLogger(__name__)CODE
LOWgeoai/extract.py28logger = logging.getLogger(__name__)CODE
LOWgeoai/extract.py37__all__ = [CODE
LOWgeoai/landcover_train.py28logger = logging.getLogger(__name__)CODE
LOWgeoai/landcover_train.py2083__all__ = [CODE
LOWgeoai/hf.py8logger = logging.getLogger(__name__)CODE
LOWgeoai/hf.py17__all__ = [CODE
LOWgeoai/auto.py59logger = logging.getLogger(__name__)CODE
LOWgeoai/auto.py61__all__ = [CODE
LOWgeoai/segmentation.py7logger = logging.getLogger(__name__)CODE
LOWgeoai/tools/cloudmask.py16logger = logging.getLogger(__name__)CODE
LOWgeoai/tools/__init__.py9__all__ = []CODE
LOWgeoai/tools/sr.py19logger = logging.getLogger(__name__)CODE
LOWgeoai/tools/multiclean.py28logger = logging.getLogger(__name__)CODE
LOWgeoai/tools/timeseries.py21logger = logging.getLogger(__name__)CODE
LOWgeoai/agents/geo_agents.py10logger = logging.getLogger(__name__)CODE
LOWgeoai/agents/map_tools.py216 def set_pitch(self, pitch: float) -> None:CODE
34 more matches not shown…
Hallucination Indicators8 hits · 80 pts
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CRITICALgeoai/extract.py925 torch.utils.data._utils.collate.default_collate(CODE
CRITICALgeoai/extract.py935 return torch.utils.data._utils.collate.default_collate(batch)CODE
CRITICALgeoai/extract.py1182 torch.utils.data._utils.collate.default_collate(CODE
CRITICALgeoai/extract.py1192 return torch.utils.data._utils.collate.default_collate(batch)CODE
CRITICALgeoai/extract.py2057 torch.utils.data._utils.collate.default_collate(CODE
CRITICALgeoai/extract.py2067 return torch.utils.data._utils.collate.default_collate(batch)CODE
CRITICALgeoai/extract.py3072 torch.utils.data._utils.collate.default_collate(CODE
CRITICALgeoai/extract.py3082 return torch.utils.data._utils.collate.default_collate(batch)CODE
Cross-File Repetition15 hits · 75 pts
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HIGHgeoai/dinov3.py0download the object detection model from hugging face. args: model_path: path to the model file. repo_id: hugging face rSTRING
HIGHgeoai/extract.py0download the object detection model from hugging face. args: model_path: path to the model file. repo_id: hugging face rSTRING
HIGHgeoai/utils/download.py0download the object detection model from hugging face. args: model_path: path to the model file. repo_id: hugging face rSTRING
HIGHqgis_plugin/geoai/core/python_manager.py0log a message to the qgis message log. args: message: the message to log. level: the log level (default: qgis.info).STRING
HIGHqgis_plugin/geoai/core/venv_manager.py0log a message to the qgis message log. args: message: the message to log. level: the log level (default: qgis.info).STRING
HIGHqgis_plugin/geoai/dialogs/water_segmentation.py0log a message to the qgis message log. args: message: the message to log. level: the log level (default: qgis.info).STRING
HIGHqgis_plugin/geoai/dialogs/instance_segmentation.py0capture stdout and emit lines to a callback in real-time.STRING
HIGHqgis_plugin/geoai/dialogs/water_segmentation.py0capture stdout and emit lines to a callback in real-time.STRING
HIGHqgis_plugin/geoai/dialogs/segmentation.py0capture stdout and emit lines to a callback in real-time.STRING
HIGHqgis_plugin/geoai/dialogs/instance_segmentation.py0initialize the segmentation dock widget. args: iface: qgis interface instance. parent: parent widget.STRING
HIGHqgis_plugin/geoai/dialogs/water_segmentation.py0initialize the segmentation dock widget. args: iface: qgis interface instance. parent: parent widget.STRING
HIGHqgis_plugin/geoai/dialogs/segmentation.py0initialize the segmentation dock widget. args: iface: qgis interface instance. parent: parent widget.STRING
HIGHtests/test_inference.py0test that __all__ contains the expected public api names.STRING
HIGHtests/test_rfdetr.py0test that __all__ contains the expected public api names.STRING
HIGHtests/test_prithvi.py0test that __all__ contains the expected public api names.STRING
Verbosity Indicators42 hits · 65 pts
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LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py374 # Step 1: Get raster infoCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py380 # Step 2: Get band statisticsCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py387 # Step 3: Create project and add the fileCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py407 # Step 1: Create projectCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py414 # Step 2: Verify project file is valid JSONCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py429 # Step 1: List data sourcesCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py435 # Step 2: List segmentation modelsCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py441 # Step 3: List detection modelsCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py447 # Step 4: List change methodsCOMMENT
LOWgeoai/utils/geometry.py110 # Step 3: Rotate to align with axes, regularize, then rotate backCOMMENT
LOWgeoai/utils/geometry.py113 # Step 4: Rectify coordinates to enforce right anglesCOMMENT
LOWgeoai/utils/geometry.py80 # Step 1: Simplify to remove noise and small verticesCOMMENT
LOWgeoai/utils/geometry.py92 # Step 2: Get the dominant angle to rotate buildingCOMMENT
LOWgeoai/utils/raster.py1738 # Step 1: Build VRTCOMMENT
LOWgeoai/utils/raster.py1742 # Step 2: Translate VRT to output GeoTIFFCOMMENT
LOWgeoai/utils/raster.py1765 # Step 3: Clean up VRTCOMMENT
LOWqgis_plugin/geoai/core/venv_manager.py4067 # Step 1: Download Python standalone (0-10%)COMMENT
LOWqgis_plugin/geoai/core/venv_manager.py4162 # Step 2: Create venv (13-18%)COMMENT
LOWqgis_plugin/geoai/core/venv_manager.py4175 # Step 3: Install dependencies (18-95%)COMMENT
LOWqgis_plugin/geoai/core/venv_manager.py4193 # Step 4: Verify (95-100%)COMMENT
LOWscripts/train_s2_water.py504 # Step 1: Download dataset if not presentSTRING
LOWscripts/train_s2_water.py509 # Step 2: Push dataset to HuggingFace HubSTRING
LOWscripts/train_s2_water.py516 # Step 3: Tile scenes into fixed-size tilesSTRING
LOWscripts/train_s2_water.py525 # Step 4: Train modelSTRING
LOWscripts/train_s2_water.py543 # Step 5: EvaluateSTRING
LOWscripts/train_s2_water.py553 # Step 6: Push model to HuggingFace HubSTRING
LOWscripts/train_nwpu_detection.py242 # Step 1: Download and prepare datasetCOMMENT
LOWscripts/train_nwpu_detection.py245 # Step 2: Train modelCOMMENT
LOWscripts/train_nwpu_detection.py255 # Step 3: Evaluate (optional)COMMENT
LOWscripts/train_nwpu_detection.py259 # Step 4: Push to Hub (optional)COMMENT
LOWscripts/train_whu_building.py395 # Step 1: Prepare datasetCOMMENT
LOWscripts/train_whu_building.py403 # Step 2: Train modelCOMMENT
LOWscripts/train_whu_building.py421 # Step 3: EvaluateCOMMENT
LOWscripts/train_whu_building.py431 # Step 4: Push to HuggingFace HubCOMMENT
LOWscripts/object_detection_nwpu.py30# Step 1: Download NWPU-VHR-10 datasetCOMMENT
LOWscripts/object_detection_nwpu.py46# Step 2: Prepare dataset (train/val split)COMMENT
LOWscripts/object_detection_nwpu.py61# Step 3: Visualize sample annotationsCOMMENT
LOWscripts/object_detection_nwpu.py110# Step 4: Train multi-class detection modelCOMMENT
LOWscripts/object_detection_nwpu.py134# Step 5: Plot training metricsCOMMENT
LOWscripts/object_detection_nwpu.py173# Step 6: Evaluate model with COCO metricsCOMMENT
LOWscripts/object_detection_nwpu.py189# Step 7: Run inference on a validation imageCOMMENT
LOWscripts/object_detection_nwpu.py221# Step 8: Visualize detectionsCOMMENT
Structural Annotation Overuse42 hits · 64 pts
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LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py374 # Step 1: Get raster infoCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py380 # Step 2: Get band statisticsCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py387 # Step 3: Create project and add the fileCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py407 # Step 1: Create projectCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py414 # Step 2: Verify project file is valid JSONCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py429 # Step 1: List data sourcesCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py435 # Step 2: List segmentation modelsCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py441 # Step 3: List detection modelsCOMMENT
LOWagent-harness/cli_anything/geoai/tests/test_full_e2e.py447 # Step 4: List change methodsCOMMENT
LOWgeoai/utils/geometry.py110 # Step 3: Rotate to align with axes, regularize, then rotate backCOMMENT
LOWgeoai/utils/geometry.py113 # Step 4: Rectify coordinates to enforce right anglesCOMMENT
LOWgeoai/utils/geometry.py80 # Step 1: Simplify to remove noise and small verticesCOMMENT
LOWgeoai/utils/geometry.py92 # Step 2: Get the dominant angle to rotate buildingCOMMENT
LOWgeoai/utils/raster.py1738 # Step 1: Build VRTCOMMENT
LOWgeoai/utils/raster.py1742 # Step 2: Translate VRT to output GeoTIFFCOMMENT
LOWgeoai/utils/raster.py1765 # Step 3: Clean up VRTCOMMENT
LOWqgis_plugin/geoai/core/venv_manager.py4067 # Step 1: Download Python standalone (0-10%)COMMENT
LOWqgis_plugin/geoai/core/venv_manager.py4162 # Step 2: Create venv (13-18%)COMMENT
LOWqgis_plugin/geoai/core/venv_manager.py4175 # Step 3: Install dependencies (18-95%)COMMENT
LOWqgis_plugin/geoai/core/venv_manager.py4193 # Step 4: Verify (95-100%)COMMENT
LOWscripts/train_s2_water.py504 # Step 1: Download dataset if not presentSTRING
LOWscripts/train_s2_water.py509 # Step 2: Push dataset to HuggingFace HubSTRING
LOWscripts/train_s2_water.py516 # Step 3: Tile scenes into fixed-size tilesSTRING
LOWscripts/train_s2_water.py525 # Step 4: Train modelSTRING
LOWscripts/train_s2_water.py543 # Step 5: EvaluateSTRING
LOWscripts/train_s2_water.py553 # Step 6: Push model to HuggingFace HubSTRING
LOWscripts/train_nwpu_detection.py242 # Step 1: Download and prepare datasetCOMMENT
LOWscripts/train_nwpu_detection.py245 # Step 2: Train modelCOMMENT
LOWscripts/train_nwpu_detection.py255 # Step 3: Evaluate (optional)COMMENT
LOWscripts/train_nwpu_detection.py259 # Step 4: Push to Hub (optional)COMMENT
LOWscripts/train_whu_building.py395 # Step 1: Prepare datasetCOMMENT
LOWscripts/train_whu_building.py403 # Step 2: Train modelCOMMENT
LOWscripts/train_whu_building.py421 # Step 3: EvaluateCOMMENT
LOWscripts/train_whu_building.py431 # Step 4: Push to HuggingFace HubCOMMENT
LOWscripts/object_detection_nwpu.py30# Step 1: Download NWPU-VHR-10 datasetCOMMENT
LOWscripts/object_detection_nwpu.py46# Step 2: Prepare dataset (train/val split)COMMENT
LOWscripts/object_detection_nwpu.py61# Step 3: Visualize sample annotationsCOMMENT
LOWscripts/object_detection_nwpu.py110# Step 4: Train multi-class detection modelCOMMENT
LOWscripts/object_detection_nwpu.py134# Step 5: Plot training metricsCOMMENT
LOWscripts/object_detection_nwpu.py173# Step 6: Evaluate model with COCO metricsCOMMENT
LOWscripts/object_detection_nwpu.py189# Step 7: Run inference on a validation imageCOMMENT
LOWscripts/object_detection_nwpu.py221# Step 8: Visualize detectionsCOMMENT
Over-Commented Block37 hits · 20 pts
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LOWgeoai/sam.py741 # point_coords,COMMENT
LOWtests/test_embeddings.py1# #!/usr/bin/env pythonCOMMENT
LOWtests/test_embeddings.py21# self.assertIsInstance(df, pd.DataFrame)COMMENT
LOWtests/test_embeddings.py41# def test_list_embedding_datasets_filter_pixel(self):COMMENT
LOWtests/test_embeddings.py61# self.assertIn("class_name", info)COMMENT
LOWtests/test_embeddings.py81# "earth_embeddings",COMMENT
LOWtests/test_embeddings.py101# "temporal_extent",COMMENT
LOWtests/test_embeddings.py121COMMENT
LOWtests/test_embeddings.py141COMMENT
LOWtests/test_embeddings.py161# """Test that loading pixel dataset without paths raises ValueError."""COMMENT
LOWtests/test_embeddings.py181# """Set up test data."""COMMENT
LOWtests/test_embeddings.py201# import matplotlibCOMMENT
LOWtests/test_embeddings.py221# matplotlib.use("Agg")COMMENT
LOWtests/test_embeddings.py241# matplotlib.use("Agg")COMMENT
LOWtests/test_embeddings.py261# import matplotlib.pyplot as pltCOMMENT
LOWtests/test_embeddings.py281# def test_plot_embedding_raster(self):COMMENT
LOWtests/test_embeddings.py301# """Set up test data."""COMMENT
LOWtests/test_embeddings.py321COMMENT
LOWtests/test_embeddings.py341# # Cosine similarity with itself should be ~1.0COMMENT
LOWtests/test_embeddings.py361# from geoai.embeddings import embedding_similarityCOMMENT
LOWtests/test_embeddings.py381COMMENT
LOWtests/test_embeddings.py401# from geoai.embeddings import compare_embeddingsCOMMENT
LOWtests/test_embeddings.py421COMMENT
LOWtests/test_embeddings.py441# """Set up test data."""COMMENT
LOWtests/test_embeddings.py461# from geoai.embeddings import train_embedding_classifierCOMMENT
LOWtests/test_embeddings.py481# """Test training a Random Forest classifier."""COMMENT
LOWtests/test_embeddings.py501# self.train_labels,COMMENT
LOWtests/test_embeddings.py521# verbose=False,COMMENT
LOWtests/test_embeddings.py541# def test_save_geotiff(self):COMMENT
LOWtests/test_embeddings.py561# self.assertEqual(src.width, 64)COMMENT
LOWtests/test_embeddings.py581# with rasterio.open(output_path) as src:COMMENT
LOWtests/test_embeddings.py601# def test_extract_from_mock_dataset(self):COMMENT
LOWtests/test_embeddings.py621# data = extract_patch_embeddings(ds, max_samples=5)COMMENT
LOWtests/test_embeddings.py641# ds = MockDataset()COMMENT
LOWtests/test_embeddings.py661# data = extract_patch_embeddings(ds, max_samples=10)COMMENT
LOWtests/test_embeddings.py681# self.assertTrue(hasattr(geoai, "cluster_embeddings"))COMMENT
LOW.github/workflows/draft-pdf.yml21 # This is the output path where Pandoc will write the compiledCOMMENT
AI Slop Vocabulary4 hits · 12 pts
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MEDIUMgeoai/change_detection.py1032 # Create comprehensive visualizationCOMMENT
MEDIUMgeoai/change_detection.py689 # Create comprehensive visualizationCOMMENT
MEDIUMgeoai/change_detection.py1023 """Create a comprehensive visualization report from detailed results."""STRING
MEDIUMgeoai/extract.py1418 # Create a rotated rectangle using OpenCV method (more robust than Shapely methods)COMMENT
Modern AI Meta-Vocabulary2 hits · 8 pts
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MEDIUMgeoai/auto.py738 # Handle zero-shot object detection separatelyCOMMENT
MEDIUMqgis_plugin/geoai/core/venv_manager.py4006# Full orchestrationCOMMENT
Cross-Language Confusion2 hits · 6 pts
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HIGHscripts/train_ptv3_dales.py601 " cd Pointcept/libs/pointops && python setup.py install && cd ../..\n"CODE
HIGHscripts/train_ptv3_dales.py12 cd Pointcept/libs/pointops && python setup.py install && cd ../..STRING
Example Usage Blocks3 hits · 4 pts
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LOWgeoai/segmentation.py392# Example usageCOMMENT
LOWdocs/examples/pipelines/building_extraction.yaml7# Usage:COMMENT
LOWdocs/examples/pipelines/semantic_segmentation.yaml7# Usage:COMMENT