War Radar: An Automated Pipeline That Uses AI to Classify Conflict and Evacuation Reports

War Radar, a new project from the Ukrainian agency SEOPort, publishes a daily world map of armed-conflict risk. The daily estimates are calculated automatically, and AI models are used only for a few narrow classification tasks. It is a practical case study in where models belong in an automated system — and where they do not.

What it shows

• Countries with available data, updated daily: each country’s current status (from armed violence to major armed conflict), ongoing events such as coups, major disasters, epidemics and internet shutdowns, and a four-level estimate of the risk of a new outbreak of armed violence or a coup within 90 days.
• Evacuations and warnings on a two-hour cycle: reports of diplomatic staff being withdrawn and new official travel warnings from several governments are checked every two hours.
• A “Possible sharp escalation in fighting” mark: when reports of fighting in a country suddenly spike, an AI model reads the headlines and judges whether they suggest a sharp escalation. The project publishes its record: in a 2022–2026 check about half of these marks coincided with a sharp rise in deaths, but the mark missed most major escalations, including the 2022 invasion of Ukraine. It does not affect the risk level.
• Five languages: English, Ukrainian, Polish, German and Spanish, with weekly summaries back to 2018.

Where AI models fit in

The risk level itself comes from a formula, not a language model: it combines the level and trend of armed violence, recent history, official travel warnings, embassy evacuations, news tone and the situation in neighboring countries, each weighted by how strongly it was linked to crises in the past. Models are used where fixed rules break down — deciding whether a news report really describes an embassy evacuation, or whether the headlines suggest a sharp escalation in fighting.

Under the hood it is a set of scheduled Python jobs that collect and process data, use SQLite, and generate a static site served by nginx. Failures trigger a Telegram alert. Conflict data arrive with a delay of about two months, so recent escalations may not yet be reflected in the risk level — a limitation the project states on the site.

Validation and limitations

The project describes the result as a rough estimate, not a forecast. The formula was fitted on 2015–2022 and tested on 2023–2026, years it had not seen. In that test, weeks rated “Low” were followed by a new crisis about 1 in 110 times, and weeks rated “Very high” about 1 in 4 times — so even at the top level, most weeks pass without a crisis. A public “Checking the estimates” page records every weekly rating from launch and compares it with what happened 90 days later, misses included.

For anyone building automation with AI agents, the design choice is the interesting part: deterministic code does the counting, models handle narrow yes/no classification, and the system publishes observed crisis frequencies for each risk level together with its known limitations.