Survey CSV & Excel Cleaner (PENZD)
Consolidate up to 100 independent survey data files (CSV, TXT, Excel). Re-map coordinates dynamically to correct orientations, perform bulk elevation Shifts, standardize description codes, and check point locations on an interactive 2D coordinate canvas.
Drag & Drop Survey Files
Support up to 100 coordinates files simultaneously. Accepts .csv, .txt, .dat, and Excel .xlsx spreadsheets.
Survey CSV Cleaner (PENZD) Documentation
Technical specifications, mathematical formulas, and civil guidelines
1. Functional Purpose
Raw total station or GPS survey files come in mismatched, poorly structured columns with duplicate point numbers, text characters in coordinate columns, and invalid elevation values. Importing these raw files directly into Civil 3D causes survey database errors, missing breaklines, and triangulation (TIN Surface) anomalies. The **Survey CSV Cleaner** parses these raw text and CSV files, maps columns automatically, validates elevation limits, eliminates duplicate point numbers, and exports a standardized PENZD file.
It maps description codes (e.g., standardizing codes for centerline, edge of road, shoulder) and converts arbitrary separator structures into clean, comma-delimited import sheets.
2. Practical Workflow Instructions
To clean and prepare your survey listings:
- Upload Raw Data: Drag or upload your raw total station coordinate file (.csv or .txt).
- Map Coordinates: The cleaner auto-detects columns. If auto-detection is incorrect, manually map columns for: Point Number, Easting, Northing, Elevation, and Description (P-E-N-Z-D).
- Filter Settings: Set coordinate boundaries, check for duplicated point IDs, and toggle description mapping overrides.
- Process & Download: Click `Clean Data`. Inspect the processed data log for filtered outliers and download the formatted CSV file, ready for immediate surface generation in AutoCAD Civil 3D.
3. Mathematical & Civil Engineering Basis
The cleaning engine performs statistical checks to identify data-entry anomalies. Outliers are detected using spatial distance deviation checking:
Where E_i, N_i are coordinates, and E_avg, N_avg represent the mean spatial coordinates. If d_i exceeds a threshold multiple of the standard deviation (Z-score outlier detection), a warning is raised.
It also checks elevation levels against normal geographic limits:
Any row with null coordinates, alphabetic letters in number fields, or zero values is flagged and excluded to prevent surface interpolation spikes.