Meteorological Satellite Report Uncertainty and Negation Benchmark Dataset

#Change Establishment Judgment #Uncertainty Detection #Change Type Classification #Evidence Span Extraction #Structured Information Generation #Meteorological Report Analysis #Satellite Change Monitoring #Weather Information Extraction #Model Error Diagnosis

3650
0.4GB
JSON
CATL
MOBIUSI INC

Updated:2026-11-04

AI Analysis & Value Prop

This benchmark focuses on negation, modality, and uncertainty in web-based meteorological satellite reports, including statements such as no significant change observed, possible intensification, gradual weakening, and no expansion in extent yet. Each record provides structured annotations for whether a change is established, the confidence state, candidate change types, and supporting evidence spans from the source text. It supports meteorological remote-sensing text analysis, model training, and targeted evaluation of common errors in change understanding and structured generation.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
report_text	string	The raw text of the web-based meteorological satellite report to be analyzed.
evidence_spans	array	Source-text spans supporting the judgment of whether a change is established, its confidence state, or its change type, with position and expression attributes.
confidence_state	string	Describes the certainty of the report's change judgment, distinguishing explicit conclusions, possible judgments, and cases where no judgment can be made.
change_established	boolean	Indicates whether the report explicitly states that the target change has occurred; negated statements such as no significant change observed are generally labeled false.
candidate_change_types	array	One or more change types identified in the report text; no significant change is also recorded as a candidate type.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What does the dataset contain?: The dataset is based on web-based meteorological satellite reports. It provides structured labels for whether a change is established, the confidence state, candidate change types, and source-text evidence spans, with a focus on negation, possibility, and trend expressions.

Which training and evaluation tasks does it support?: It supports change establishment judgment, uncertainty detection, change type classification, evidence extraction, and structured information generation. It can also help diagnose errors involving statements such as no significant change observed or possible intensification.

What are the dataset size and annotation specifications?: The field design provides both judgment labels and source-text evidence, supporting result verification and error analysis. The supplied information does not specify the sample count or coverage period, so check the product details or dataset documentation for exact specifications before acquisition.

How can I obtain the data and verify its license?: Check the dataset product page for available access methods, file formats, and license terms. Before use, confirm that the permitted scope covers your research, training, or commercial deployment needs.

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Cite this Work

@dataset{Mobiusi2026,
  title={Meteorological Satellite Report Uncertainty and Negation Benchmark Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/74a8bcaf3423782600d3358999b1652d},
  urldate={2026-10-27},
  keywords={meteorological satellite dataset,satellite report text,change detection benchmark,negation detection,uncertainty detection,remote sensing text,structured data generation,evidence span extraction},
  version={1.0}
}

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