Qingdao operates an advanced 3D city map for smart urban management

https://en.people.cn/n3/2025/0530/c98649-20322033.html

A platform on real-scene 3D modeling of the city of Qingdao was launched in March 2021 under the leadership of the Qingdao Institute of Survey and Mapping

Qingdao’s varied topography – marked by hilly terrain and dramatic elevation changes – necessitated the use of oblique aerial imaging to capture raw imagery and build an accurate 3D model. The project team deployed manned fixed-wing aircraft equipped with 150-megapixel, five-lens oblique aerial cameras. The aerial survey covered the entire urban area, achieving a ground resolution of 15 centimeters and maintaining more than 70 percent image overlap to maximize accuracy.

In March 2022, following expert review, the project was officially launched for citywide application. Today, the platform covers Qingdao’s entire land area – 11,000 square kilometers – as well as 800 kilometers of coastline, 49 bays, and seven inhabited islands.

The 3D simulation platform has been shared with over 60 municipal departments. It supports more than 100 key functions, including disaster prevention and mitigation, urban planning, social governance, and urban renewal. The platform also underpins over 70 digital government service applications and records nearly 100 million uses annually. As an example, at the bureau’s headquarters, staff members examined two versions of a digital model for a former mining site in Qingdao’s West Coast New Area. The comparison revealed tangible signs of ecological restoration – more vegetation and a gentler slope. Qingdao is home to 898 legacy mine sites. In the past, inspecting these sites required a full month of on-the-ground efforts. Now, with the help of the 3D model, the same work takes just five days.

Since 2023, the city has carried out annual temporal updates to the city-scale 3D simulation platform, enabling it to track urban changes with precision and support data-driven lysis and evidence-based planning.

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https://en.people.cn/n3/2026/0324/c90000-20439477.html

At the Qingdao Humanoid Robot Data Training Center in Laoshan District of Qingdao, humanoid robots are trained for jobs such as intelligent industrial manufacturing, smart home, and commercial services. Data collectors here control robots to complete specific tasks like logistics sorting, supermarket restocking, kitchen operations, and component assembly. Through thousands of repetitions and trials, massive amounts of motion data are generated, endowing robots with a smarter “intelligent brain,” and helping humanoid robots enter all walks of life to serve thousands of households.

https://www.cas.cn/syky/202602/t20260226_5102870.shtml

https://doi.org/10.1186/s40168-026-02339-3

A research team at the CAS Qingdao Institute of Bioenergy and Bioprocess Technology has developed RamEx, an integrated analysis framework for Ramanome big data. This platform, tailored to the characteristics of Raman spectroscopy data, establishes a one-stop workflow from data reading and standardized preprocessing to downstream data mining, centered on automated quality control algorithms and efficient parallel computing processes. It also demonstrates a systematic analysis of microbial metabolomical heterogeneity and metabolic pattern differentiation at the single-cell level.

Raman genomics deep analysis can track the dynamic changes in the composition of macromolecules such as lipids, proteins, and nucleic acids in different cells, thus revealing the differentiation and succession patterns of microbial metabolic states at the population scale with single-cell precision. This provides new research ideas and technical pathways for understanding the functional organization and environmental adaptation mechanisms of complex communities.

http://english.cas.cn/newsroom/research-news/202602/t20260224_1151116.shtml

https://link.springer.com/article/10.1186/s40168-026-02339-3

Scientists from the CAS Qingdao Institute of Bioenergy and Bioprocess Technology have developed a novel computational tool, RamEx, designed to resolve the computational bottleneck in high-throughput microbial Ramanomics.

RamEx streamlines the full Ramanomic analysis pipeline, from data preprocessing and automated quality control to advanced data mining. An Iterative Convolutional Outlier Detection (ICOD) algorithm tackles spectral noise in an unsupervised manner to dynamically identify and eliminate spectral artifacts, ensuring high-quality input for downstream analysis.

The platform’s performance was validated using diverse datasets, including pathogenic bacteria, probiotics, and yeast fermentation systems. Notably, RamEx successfully captured phenotypic heterogeneity in genetically identical yeast cells by detecting subtle metabolic fluctuations and tracking the dynamic accumulation of intracellular macromolecules, including lipids, proteins, and nucleic acids.

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