Datasets:

cayerbe
/

geiant-benchmark

language:
  - en
license: apache-2.0
tags:
  - geospatial
  - agent-benchmark
  - jurisdictional-routing
  - geometry-validation
  - delegation-chain
  - gdpr
  - eu-ai-act
  - h3
  - gns-protocol
pretty_name: GEIANT Geospatial Agent Benchmark
size_categories:
  - n<1K
task_categories:
  - text-classification
  - question-answering

GEIANT Geospatial Agent Benchmark

The first benchmark dataset for geospatial AI agent orchestration.

Built on the GNS Protocol — the decentralized identity system that proves humanity through Proof-of-Trajectory.

Overview

Every AI orchestrator (LangChain, CrewAI, AutoGPT) routes tasks based on capability and availability. None of them understand where the task originates, what regulatory framework governs that location, or whether the geometry the agent produced is actually valid.

GEIANT fixes this. This benchmark tests three capabilities no other orchestrator has:

Capability	What it tests
Jurisdictional Routing	H3 cell → country → regulatory framework → agent selection
Geometry Mutation Integrity	Multi-step geometry workflows with injected corruption
Delegation Chain Validation	Human→agent authorization cert validity

Dataset Statistics

Total records: 40

By Family

Family	Count
`jurisdictional_routing`	14
`geometry_mutation`	11
`delegation_chain`	15

By Difficulty

Difficulty	Count
`easy`	16
`medium`	13
`hard`	4
`adversarial`	7

By Expected Outcome

Outcome	Count
`route_success`	15
`reject_delegation`	10
`reject_geometry`	7
`reject_no_ant`	4
`reject_tier`	2
`reject_no_jurisdiction`	1
`flag_boundary_crossing`	1

Schema

Each record is a DatasetRecord with the following fields:

{
  id: string;                    // UUID
  family: DatasetFamily;         // which benchmark
  description: string;           // human-readable scenario description
  input: object;                 // the task/cert/geometry submitted
  expected_outcome: string;      // what GEIANT should do
  ground_truth: {
    expected_ant_handle?: string;
    expected_country?: string;
    expected_frameworks?: string[];
    geometry_valid?: boolean;
    delegation_valid?: boolean;
    explanation: string;         // WHY this is the correct answer
  };
  difficulty: string;            // easy | medium | hard | adversarial
  tags: string[];
}

Regulatory Frameworks Covered

Framework	Jurisdiction	Max Autonomy Tier
GDPR	EU	trusted
EU AI Act	EU	trusted
eIDAS2	EU	certified
FINMA	Switzerland	certified
Swiss DPA	Switzerland	certified
UK GDPR	United Kingdom	trusted
US EO 14110	United States	sovereign
CCPA	California, USA	sovereign
LGPD	Brazil	trusted
PDPA-SG	Singapore	trusted
Italian Civil Code	Italy	trusted

Usage

from datasets import load_dataset

ds = load_dataset("GNS-Foundation/geiant-geospatial-agent-benchmark")

# Filter by family
routing = ds.filter(lambda x: x["family"] == "jurisdictional_routing")

# Filter by difficulty
adversarial = ds.filter(lambda x: x["difficulty"] == "adversarial")

# Get all rejection scenarios
rejections = ds.filter(lambda x: x["expected_outcome"].startswith("reject_"))

Geospatial Moat

This dataset uses H3 hexagonal hierarchical spatial indexing (Uber H3) at resolution 5–9. Each agent is assigned a territory as a set of H3 cells. Routing validates that the task origin cell is contained within the agent's territory — not just lat/lng bounding boxes.

The H3 cells in this dataset are generated from real coordinates:

import h3
rome_cell = h3.latlng_to_cell(41.902, 12.496, 7)
# → '871e805003fffff'

Citation

@dataset{geiant_benchmark_2026,
  author    = {Ayerbe, Camilo},
  title     = {GEIANT Geospatial Agent Benchmark},
  year      = {2026},
  version   = {0.1.0},
  publisher = {GNS Foundation / ULISSY s.r.l.},
  url       = {https://huggingface.co/datasets/GNS-Foundation/geiant-geospatial-agent-benchmark}
}

License

Apache 2.0 — free for research and commercial use.

Built with GEIANT — Geo-Identity Agent Navigation & Tasking. Part of the GNS Protocol ecosystem.