T/XZBX 0194-2026 Active Group standards

T/XZBX 0194-2026 Unmanned aerial vehicle aerial survey data accuracy intelligent verification technical specification

Publish Date: 2026-03-16 Implement Date: 2026-04-01 For services related to genuine standard inquiry, procurement, translation, and other related services in China, please Contact Us

Price: $ 20.00

Out of stock

Basic Information

Standard Code: T/XZBX 0194-2026

Standard Type: Group standards

Standard Status: Active

is_force_gb: no

CCS Name: -

ICS Name: The application of information technology in natural sciences

Publish Date: 2026-03-16

Implement Date: 2026-04-01

Scope

In recent years, unmanned aerial surveying has been widely applied in scenarios such as natural resource investigation, engineering measurement and monitoring, emergency disaster assessment, traffic and municipal inspection, and fine management of mines and water resources, due to its advantages of mobility, cost-effectiveness, and short data acquisition cycle. At the same time, aerial surveying tasks exhibit characteristics of multi-platform, multi-sensor, multi-batch, cross-regional, and high-frequency. The data production chain is complex, involving flight planning, field implementation, image control setting and measurement, spatial three-dimensional adjustment, point cloud/image processing, result production, and delivery to storage. Any deviation in any link may lead to results that do not meet the expected accuracy requirements. Traditional accuracy verification often relies on manual spot checks, offline calculations, and empirical interpretation, which suffer from low efficiency, insufficient reproducibility, and difficulty in timely detecting systematic errors and hidden anomalies, making it difficult to adapt to the needs of large-scale production, rapid delivery, and quality traceability management. Intelligent verification technology, based on methods such as quality rule libraries, statistical testing, spatial consistency analysis, and anomaly detection, combined with task metadata, sensor parameters, processing logs, and feature characteristics of result elements, enables automated evaluation of aerial surveying data accuracy, problem localization, and risk warning. It can significantly improve verification efficiency and consistency, reduce human subjective differences, and support batch production and full-process quality management. To standardize the scope of intelligent verification for aerial surveying data accuracy, technical processes, indicator systems, data requirements, algorithm and threshold management, result determination, and output f