Building height is a key indicator of urban vertical structure, yet most existing methods still estimate one representative height for each building. Although adequate for many applications, this representation cannot localize height variation along the building’s façade edges in stepped or other multi-height structures, limiting its use in street-scale analyses that depend on local building geometry, such as shadow, noise, and visibility modeling. To represent such localized variation, this study proposes a multi-view computational framework to estimate façade-level building heights, based on panoramic street-view imagery (SVI) and building-footprint GIS data. The framework combines visibility-aware SVI-façade matching, projection-guided pixel-to-façade modeling, and view-quality-aware multi-view fusion using pointwise weighted median aggregation to capture façade-level height variations. Evaluations in Rotterdam and Amsterdam showed that the proposed framework outperformed both evaluated baselines while additionally preserving façade-level height variation. On the common detected building sets, it achieved mean absolute errors (MAEs) of 1.13 m and 3.50 m, respectively, reducing errors by 22.1% and 27.4% relative to Pano2Geo and by 74.6% and 79.4% relative to SIHE. It also achieved the highest proportions of estimates with absolute errors below 1, 2, and 3 m in both sets. At the façade level, MAEs were 1.09 m in Rotterdam and 3.68 m in Amsterdam against the official LoD 1.3 reference. The proposed method retained the lowest building-level MAE across all occlusion groups in both areas, although high-rise buildings with slanted roofs remained challenging. These results indicate that the proposed framework can recover localized street-facing height variation from multi-view SVI and building footprints while retaining accurate building-level estimates after aggregation. The resulting street-facing height information provides a geometric basis for detailed urban digital twins and could support street-scale analyses of shadow and solar exposure, noise propagation, and visibility or street enclosure.
Polar environmental changes affect the surface energy balance, ocean circulation, and global sea level. Constrained by extreme environmental conditions and logistical challenges, traditional in situ observations exhibit limitations in spatial coverage and temporal continuity. Remote sensing supports polar environmental research by providing repeated observations over large areas. Over the past four decades, China’s polar remote sensing research has transitioned from early reliance on foreign satellite datasets to the establishment of an independent and increasingly comprehensive observation system. This paper traces the development of China’s polar remote sensing technologies, reviews the initial establishment of research frameworks, and summarizes the evolution and applications of domestically developed satellite systems, crewed airborne systems, and unmanned aerial vehicle platforms. Recent integrated observation campaigns based on domestic icebreakers are further examined, focusing on the combination of satellite, airborne, ice-based, and underwater measurements for sea-ice mapping and remote sensing validation. This review also identifies limitations in observational coverage, measurement accuracy, and multi-platform data integration, and discusses future directions in dedicated polar satellite observations, long-endurance autonomous airborne platforms, multi-source retrieval, data assimilation, and operational services.
As networked Earth observation systems, satellite remote sensing constellations serve as the core support for global aerospace information acquisition. However, the traditional open-loop, feed-forward remote sensing paradigm dominated by the “planned observation to scene interpretation” pipeline struggles to cope with the complete sampling of non-stationary events, spatio-temporal reasoning of complex physical processes, and autonomous platform coordination. This restricts constellation systems from leaping from “global coverage” to “global cognition.” Targeting these bottlenecks, this paper systematically constructs a unified theoretical framework for the spatio-temporal intelligence of satellite remote sensing constellations. We formalize the constellation as a discrete-time dynamic multi-agent collaborative network and examine its three core technologies: dynamic event perception, physics-driven cognitive modeling, and autonomous cross-domain scheduling. Crucially, by combining terrestrial pre-training with on-orbit model manifold splitting and quantized Directed Acyclic Graph (DAG) offloading, this architecture achieves sub-second local operator inference on edge nodes while compressing the system-level end-to-end event response loop from traditional hours/days to minutes, thereby drastically decreasing inter-satellite communication overhead. On this basis, we further explore the deployment potential of this intelligent architecture in high-impact scenarios, such as the early warning of major ecological disasters, the tracking of high-threat maneuvering targets, and complex urban emergency responses. We also discuss its limitations encountered during deployment: the vulnerability of cross-modal perception to non-stationary spatial noise, the multi-year compute gap between space-grade radiation-hardened hardware (e.g. FPGAs/ASICs) and terrestrial GPUs, and the dynamic instability of Inter-Satellite Links (ISLs). Ultimately, this lays a theoretical foundation for expanding the cognitive boundaries of the physical world’s evolutionary mechanisms and building high-precision, highly credible aerospace decision-support systems, while providing a clear development roadmap for the evolution of next-generation autonomous intelligent aerospace systems.
Since the Reform and Opening-up, the population pattern of Chinese mainland has experienced a profound spatiotemporal restructuring. However, current mainstream open-access Gridded Population Data (GPD), including WorldPop, LandScan Global, and Global Human Settlement Population (GHS-POP), are limited by historical data gaps or the potential uncertainties introduced by estimating historical periods using recent static auxiliary data. Moreover, current population regression modeling methods typically fail to simultaneously consider nonlinearity and spatiotemporal nonstationarity, thereby limiting the accuracy of Gridded Population Mapping (GPM). To overcome these challenges, we propose a novel model, named Geographically and Temporally Neural Network Weighted Nonlinear Regression (GTNNWNR), to generate 1 km × 1 km GPD for Chinese mainland from 1982 to 2020 by integrating multi-source dynamic auxiliary data (i.e. building area, nighttime light, and land use data). Results demonstrate that GTNNWNR outperforms baseline models in county-level fitting performance, with the generated GPD exhibiting the highest accuracy at the township level. Furthermore, our dataset shows significantly higher overall accuracy compared to mainstream products. Notably, by integrating fully dynamic auxiliary variables, our dataset achieves superior temporal stability in accuracy compared with GHS-POP. Spatiotemporal analysis indicates that from 1982 to 2020, despite a significant increase in the area and population proportion of high-density regions, the macroscopic pattern across the Hu Line remained highly stable. In conclusion, this study contributes to improving the GPM method and provides researchers and policymakers with valuable datasets and insights to support sustainable development in the Chinese mainland. Moreover, the GTNNWNR model developed in this study exhibits strong spatiotemporal modeling and predictive potential for various geographic processes and environmental phenomena beyond GPM applications.
High-quality development is the primary task of building a modern socialist country in all respects, which imposes all-round demands on the aerospace information infrastructure for unified benchmarks, ubiquitous precision, multi-dimensional perception, and real-time intelligence. Starting from the deepened connotation of high-quality development, this paper demonstrates the definition, connotation, and strategic status of aerospace information as a new production factor. It systematically reviews the development trends of the three major technical systems of satellite navigation, space-terrestrial integrated communication network, and Earth observation, pointing out that aerospace information technology is undergoing a four-dimensional observation revolution in space, time, spectrum, and elements, and shows an inevitable trend of communication-navigation-remote sensing integration. A triune empowering framework of spatiotemporal intelligence, communication intelligence, and perception intelligence is proposed, clarifying that aerospace information technology supports high-quality development through three mechanisms: factor empowerment, process intelligence, and system resilience. Furthermore, the paper discusses the intelligent infrastructure characterized by the integration of professional multimodal small AI models, evolving toward system-level collaborative intelligence and embodied intelligence. With the completion of the new-generation BeiDou comprehensive spatiotemporal system, mega low-Earth-orbit communication constellations, and industry-oriented remote sensing constellations, aerospace information technology will build a multi-dimensional and all-round intelligent aerospace information infrastructure supporting China’s high-quality development, providing a ubiquitous, precise, and secure digital foundation for cultivating new quality productive forces.
