Geo-Data Automation for the Israeli Air Force
A mission-critical Python automation that transformed recurring Israeli Air Force geo-spatial reporting—from 12+ hours of manual work to seconds.
Overview
During my service with the Israeli Air Force, automated of a sensitive geo-spatial reporting workflow. Analysts previously spent 12–15 hours copying coordinates, metadata, and operational labels between Excel and Word templates. I engineered a Python-based converter that ingests the raw Excel workbook, applies validation and transformation rules, and exports a mission-ready Word document in seconds.
Highlights
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Designed a resilient parser with
openpyxlthat normalized inconsistent sheet formats and safeguarded against empty or malformed cells. -
Generated standardized Word intelligence
dossiers using
python-docx, automatically embedding annotated maps, headers, and operational summaries. - Implemented a Pytest regression harness replicating historical datasets to guarantee that every code change preserved the integrity of the delivered reports.
Impact
The automation reduced delivery time per report from half a day to seconds, saving an estimated 250 labor hours in the first six months. Operators reallocated that time toward higher-value mission analysis while trusting that the generated documents were complete, accurate, and audit-ready.