Public evaluation

Conflict Zone Resolution

The swarm is tested by providing coordinates within a known conflict zone and a request from a civilian entity.

Evaluation type

task based

Challenge

Geo-Compliance Satellite Swarm

Difficulty

Advanced

Rigor

Unspecified

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Evaluation overview

How the linked challenge is judged: tasks, benchmarks, and criteria count.

Tasks

Benchmarks

Criteria

Task templates

Inputs and expected outputs.

Task 1

Conflict Zone Resolution

Determine if a request for imagery at specific coordinates should be denied based on current geo-political status.

Input format

Latitude, Longitude, Requesting Entity Type

Output format

Approval Status, Risk Level, Agent Consensus log

Linked challenge

Geo-Compliance Satellite Swarm

Inspired by Planet Labs' recent decision to withhold satellite imagery of conflict regions at the request of the US government, you will design a multi-agent 'Compliance Swarm'. Using CrewAI, you will define three distinct roles: a Geo-Strategist, a Legal Compliance Officer, and an Image Analyst. The swarm will automate the process of reviewing satellite image requests against dynamic international conflict zone databases and government directives. The system will use GPT-5.4 Pro for high-level synthesis and strategic reasoning, while Claude Sonnet 4.6.6 handles the detailed regulatory text analysis. You will host the specialized image classification models on the Hugging Face Inference API and use Text Generation Inference (TGI) for high-speed local processing of the agentic communication. The agents must collaborate to decide if an image of a specific coordinate can be released, ensuring that all military and law enforcement anti-drone operations.

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