The numbers behind the concept are precise. Vicinagearth Space is defined as extending from 1,000 meters below the surface—the maximum depth to which sunlight penetrates water—up to 10,000 meters above the ground, the cruising altitude of civil aviation routes. Within that envelope, the core operational zone runs from 100 meters below the surface to 1,000 meters above it. This is the layer where low-altitude three-dimensional transportation, marine ranching, underwater monitoring, and disaster rescue operations increasingly overlap, and where, according to Li, risk categories have multiplied as economies grow, exploration intensifies, and the natural environment deteriorates. The framework therefore covers low-altitude safety, underwater safety, and cross-domain safety, alongside environmental safety and the safety of artificial intelligence technologies themselves, including large language models and large vision models.

What makes VS unusual is its deliberately interdisciplinary breadth. Li describes it as a frontier field that draws on aerospace, oceanography, mechatronics, mathematics, physics, acoustics, optics, biology, life sciences, information science, computer science, control science, cybersecurity, energy, new materials, and artificial intelligence. The technical challenges that follow from this fusion are formidable: identifying remote objects and detecting ever-weaker signals, fusing multi-modal information efficiently, and coordinating formations of unmanned aerial vehicles, unmanned ground vehicles, and unmanned underwater vehicles so they can operate as integrated teams. Representative applications include aerial three-dimensional traffic management, extreme disaster monitoring and warning, autonomous search and rescue, marine ranching, and port safety monitoring.

Low-altitude safety is perhaps the most visible pillar. As airspace opens up, three-dimensional vehicles—drones, autonomous aircraft clusters, and eventually flying cars—will be monitored and controlled in real time along virtual routes, with airspace resources managed at fine granularity. The framework envisions aerial traffic control systems that optimize guidance, strengthen operational monitoring, and support tasks ranging from express delivery and geographic surveying to agriculture, emergency rescue, energy monitoring, and passenger-carrying autonomous aerial vehicles. Li notes that unmanned aerial vehicles already serve agriculture, industrial inspection, logistics, environmental protection, and firefighting, and that by connecting cities with rural and mountainous regions, they are expected to promote balanced regional development while injecting new business models into the emerging low-altitude economy.

The technical machinery required for such traffic is considerable. The editorial details how multi-UAV systems—swarms of homogeneous or heterogeneous aircraft collaborating through dynamic networking—can be organized under centralized, distributed, or hybrid architectures. Centralized designs are simple to manage but vulnerable to single points of failure and communication bottlenecks; distributed designs scale robustly but demand complex coordination mechanisms; hybrid architectures divide a swarm into sub-groups, each with its own central node, balancing adaptability with control. Communication is identified as a dominant consumer of resources, since weak signals and high interference force costly measures such as increased transmission power, relays, cooperative communication, and careful spectrum scheduling. Dynamic topology switching, which adjusts connectivity based on link quality, is proposed as a remedy that selects better paths and balances load across the fleet.

Decision-making in these swarms draws on four classes of information: sensor data from cameras, radars, and laser scanners; communication data exchanged among vehicles; status reports from a UAV management layer; and external sources such as maps, weather, and human instructions. Machine learning, reinforcement learning, and planning algorithms then convert that information into coordinated behavior, with fault-tolerant mechanisms allowing the swarm to reassign tasks and replan paths when individual units malfunction. Perhaps most strikingly, Li highlights the integration of large language models and large vision models into UAV control, allowing natural-language commands to be translated into actionable tasks and enabling real-time decisions based on environmental data and mission objectives—a development he describes as a cutting-edge fusion of AI with robotic systems that could reduce the cognitive load on human operators.

Beneath the waves, the framework turns to unmanned underwater vehicles, which come in three main flavors: tethered remotely operated vehicles controlled from surface vessels, self-propelled autonomous underwater vehicles, and hybrid vehicles that can switch between both modes. UUVs already inspect subsea pipelines with sonar and cameras, map the seafloor with magnetometers and sub-bottom profilers in the search for mineral deposits, and document underwater archaeological ruins with high-resolution imaging and LiDAR without disturbing the sites. Their advantages over manned craft are practical: they can remain submerged for extended periods, deploy quickly, eliminate risks to human crews, and avoid the cost of life-support systems. A key enabling technology is autonomous docking, which allows AUVs to return to underwater base stations to recharge and exchange data without a mothership, using optical, acoustic, or electromagnetic guidance—each with distinct strengths in range, precision, and robustness.

Cross-domain safety ties the aerial and underwater worlds together. Li envisions a future society whose operations are inherently cross-domain, three-dimensional, and coordinated, with low-altitude aircraft, ground vehicles, surface ships, and submersibles jointly integrating water, land, and airspace resources for ecological monitoring, disaster assessment, and search and rescue. Amphibious systems illustrate the concept: water-land robots that traverse rough terrain and open water alike, and air-water vehicles that combine the capabilities of a drone and an unmanned surface vessel, offering rapid deployment and reduced human risk for maritime surveillance and disaster response. Near-ground safety—building codes, pedestrian protection, traffic management, and emergency planning in urban environments—completes the picture, extending the framework down to the street level.

The editorial is candid about the obstacles ahead. Detection in the low-altitude domain remains a patchwork of radar, photoelectric, and acoustic techniques, each degraded differently by weather, terrain, and electromagnetic interference; rainstorms and complex terrain trouble all methods, fog and darkness make infrared sensors essential, and acoustic detection suffers from noise and ground reflection. Li argues that the future lies in fusing multi-sensor, multi-modal information into all-weather, all-time perception systems. Underwater, the priorities are large-field-of-view, high-frequency optical guidance and medium-to-long-range acoustic guidance for autonomous recovery of vehicles. Cross-domain data collected by heterogeneous platforms varies in resolution and quality, demanding new joint earth-observation and collaborative sensing research. Beyond technology, the framework calls for policy measures—standards, subsidies, tax incentives, and continuous evaluation—alongside attention to cybersecurity, privacy, and the environmental footprint of the systems themselves. Future work, Li concludes, should focus on predictive safety analytics, decentralized autonomous response mechanisms, ethical safeguards in heavily monitored urban airspace, and international cooperation to standardize protocols, so that managing the neighborhood of Earth becomes a collective rather than fragmented endeavor.

Subject of Research: A proposed interdisciplinary safety framework for low-altitude, underwater, and cross-domain operations in near-Earth space

Article Title: Vicinagearth

Article References: Li, X. (2024). Vicinagearth. Vicinagearth, 1(1), Article 1. https://doi.org/10.1007/s44336-024-00005-6

Image Credits: AI Generated

DOI: 10.1007/s44336-024-00005-6

Keywords: Vicinagearth Safety, low-altitude airspace, underwater vehicles, drones, UAV swarms, AUV docking, cross-domain safety, artificial intelligence, large language models, disaster rescue, marine ranching, unmanned systems