Convert CloudCompare ASCII exports to CSV files for the lidar change-detection exercise in Geospatial Field Methods at Washington University in St. Louis.
No local Python installation is needed. Connect to the default CPU runtime and
run the notebook's upload, conversion, and download cells in order. Upload your
m3c2.asc and c2c.asc exports, then download and extract
exercise06_qgis_csvs.zip. Import the CSVs into QGIS as described in the handout.
The notebook contains the conversion helper; students do not need to upload a Python script. It renames scalar-field columns and preserves every data row and numerical string, including missing values. It does not calculate point-cloud distances, adjust coordinates, apply a detection mask, or estimate survey accuracy. Uploaded files go to the Colab runtime; Google Drive access is not requested. The notebook contains no completed exercise results or lidar data.
Export the M3C2 output cloud as m3c2.asc and the June cloud carrying
C2C absolute distances as c2c.asc. Choose ASCII cloud with a column-name header and coordinates plus scalar fields.
The converter accepts comma, space, tab, or semicolon separators; field names
with spaces or underscores; and an optional point-count header. That count is
validated and is not treated as a data row. Missing values remain unchanged.
Preserve the original survey coordinates when saving.
If Save does nothing on macOS, open Display > Display settings, disable Use native load/save dialogs, click OK, then retry Save. See the CloudCompare support thread.
With Python 3.9 or later, the helper can also run locally with no extra libraries:
python3 normalize_export.py m3c2.asc m3c2.csv
python3 normalize_export.py c2c.asc c2c.csvThe notebook embeds the same helper. Regenerate it from the included script:
python3 build_colab_notebook.pyThe helper refuses to overwrite an existing CSV. The notebook uses a separate output folder for each conversion run.