Opportunities and challenges in developing geographic information science and technology in the era of the low-altitude economy
Peer-Reviewed Publicationimage:
The rapid development and widespread application of UAVs have, to some extent, accelerated the utilization of low-altitude airspace resources. According to statistics from the Civil Aviation Administration of China (CAAC) in 2023, UAVs operating at altitudes below 120 meters accounted for 99% of all UAV flights, making them the primary technology for the development of W-class low-altitude airspace in the low-altitude economy. With the advancement of electric vertical take-off and landing (eVTOL) aircraft and flying cars, key application scenarios of the low-altitude economy now include Urban Air Mobility (UAM), low-altitude inspections and emergency response, as well as social development and comprehensive governance.
view moreCredit: Beijing Zhongke Journal Publising Co. Ltd.
Journal of Geo-information Science recently published an online article on research led by professor Xiaohan Liao, associate professor Yaohuan Huang(State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences), and researcher Xia Liu (Working Committee for UAV Applications and Regulation, China Association for Geographic Information Industry). Their study systematically reviews the role of geographic information science and technology in the development of the low-altitude economy, an emerging sector driven by UAV advancements and increasing airspace utilization demands.
As a representative of new-quality productivity, the low-altitude economy is gradually emerging as a new engine for economic growth in China. China’s low-altitude economy is projected to grow into a trillion-yuan industry by 2025, significantly impacting urban transportation, logistics, and environmental monitoring. This low-altitude economy is based on the development and utilization of low-altitude airspace resources. Geographic information science and technology are crucial in key areas of the low-altitude economy, including the refined utilization of airspace resources, the construction of low altitude flying environments, the design, construction, and operation of the new air-route infrastructure, as well as the safe and efficient operation and regulatory oversight of drones. Consequently, the geographic information industry will greatly benefit from development opportunities such as the integration and innovation of emerging scientific and technological advancements, growing market demand, policy support, industrial guidance, and industrial upgrading and transformation.
Despite these Opportunities, the study addresses the Challenges that geographic information science and technology must overcome to meet the development needs of the low-altitude economy. Top challenges include spatiotemporal dimensional expansion, 3-D map and location-based services, high-frequency & rapid data-acquisition systems, all-time and all-domain capabilities, and ubiquitous AI technologies. While the low-altitude economy offers vast potential for geographic information applications, strengthening the integration of geographic information science and technology with low-altitude economic activities will promote the field and deliver essential scientific support for the sustainable management of airspace resources.
Geographic Information Science and Technology have established a mature system and advanced technical capabilities for acquiring, managing, analyzing, and applying geospatial information. The development of the low-altitude economy, which is based on the utilization of low-altitude airspace resources, urgently requires the support of Geographic Information Science and Technology, presenting new opportunities for its advancement. Geographic Information Science and Technology is expected to achieve significant breakthroughs in key aspects of the low-altitude economy, including the refined utilization of airspace resources and low-altitude environment construction, the planning, development, and operation of new air traffic infrastructure, as well as the safe and efficient operation and regulatory oversight of UAVs.
Credit
Beijing Zhongke Journal Publising Co. Ltd
For more details, please refer to the original article:
Opportunities and Challenges in Developing Geographic Information Science and Technology in the Era of the Low-Altitude Economy.
https://www.sciengine.com/JGIS/doi/10.12082/dqxxkx.2025.250028
（If you want to see the English version of the full text, please click on the “iFLYTEK Translation” in the article page.）
Article Title
Opportunities and Challenges in Developing Geographic Information Science and Technology in the Era of the Low-Altitude Economy
Proposes the idea and technical roadmap for the evolution from Geographic Information Systems (GIS) to Geographic Intelligent Agents
Beijing Zhongke Journal Publising Co. Ltd.
image:
The development of GIS and artificial intelligence has progressed through three main stages: the early combination of GIS and automation (1960–1980), the initial integration of GIS and machine learning (1990–2010), and the era of deep learning and big data-driven intelligent GIS (2010–2020).
view moreCredit: Beijing Zhongke Journal Publising Co. Ltd.
The Journal of Geo-information Science published an online article on research led by Bin Luo ,Wenhao Liu and Jin Wu (State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences), recently. This research addresses the limitation of traditional Geographic Information Systems (GIS), which cannot achieve bidirectional interaction between physical and informational spaces in rapidly changing and complex three-dimensional geographic environments. The team innovatively proposed the "Geographic Intelligent Agent" framework, which integrates embodied intelligence, self-supervised learning, and multimodal large models. Through the collaboration of three core modules—multimodal perception, intelligent hub, and action manipulation—GIS evolves from an information-processing tool to an autonomous spatial intelligence system, significantly enhancing its decision-making capabilities in complex geographic environments.
Based on the "Geographic Intelligent Agent" framework, the research team developed the virtual digital human "EarthSage" as a prototype, demonstrating the practical effectiveness of the geographic intelligent agent. With natural language commands, users can automatically call relevant data, perform autonomous intelligent geospatial analysis, and generate standardized geographic data, greatly lowering the professional threshold. The introduction of the "Geographic Intelligent Agent" marks a key step in the transformation of GIS from a static information-processing tool to a system with autonomous learning, real-time adaptation, and dynamic decision-making capabilities, paving the way for true geographic-scale spatial intelligence.
For more details, please refer to the original article:
From Geographic Information System to Geographic Intelligent Agent.
https://www.sciengine.com/JGIS/doi/10.12082/dqxxkx.2025.240658
（If you want to see the English version of the full text, please click on the “iFLYTEK Translation” in the article page.）
Article Title
From Geographic Information System to Geographic Agent
The overall architecture of the Geographic Intelligent Agent includes three core modules: multimodal perception, intelligent hub, and action manipulation, providing real-time interaction channels between the physical world and the digital world.
Inspired by neurobiology and the structural design of the human brain, the cognitive map model is collaboratively constructed by the knowledge graph model (left brain) and the multimodal large model (right brain). The knowledge graph model provides precise representation of structured spatial relationships and logical reasoning within the cognitive map, while the multimodal large model processes unstructured multimodal data through deep learning, offering dynamic environmental predictions and global semantic enhancements. Through this organization, the cognitive map constructs a dynamic understanding of spatial and environmental attributes, providing efficient structured support for organizing spatial knowledge and environmental properties.
"EarthSage" adopts a chatbot system architecture, enabling efficient interaction between users and the system through natural language processing and a GIS-based query response system
A large language model (LLM) and a cognitive map generation model (GeoGPT) are used to construct the intelligent core of "EarthSage," connecting the multimodal perception module, temporal processing module, memory storage module, planning and decision-making module, and action manipulation module, forming the architecture of the geographic intelligent agent brain.
Credit
Beijing Zhongke Journal Publising Co. Ltd.
No comments:
Post a Comment