The rapid development of artificial intelligence (AI) and ubiquitous sensing equipment is reshaping the demand structure, supply modes, and organizational models of surveying and mapping services. From the perspective of new-quality productive forces, this paper analyzes the new requirements placed on surveying and mapping services in the AI era and identifies three core characteristics: dynamic surveying and mapping, product currency, and scenario-oriented applications. It then discusses how AI technologies, unmanned surveying equipment, and public participation affect production modes, quality evaluation, and the reconstruction of standards. On this basis, the paper elaborates the connotations of new-quality productive forces in surveying and mapping from four dimensions: product systems, surveying and mapping standards, data processing, and scenario adaptation. Using new public geospatial products and autonomous surveying and mapping in unknown spaces as examples, it illustrates pathways for restructuring production organization models. The analysis suggests that the key to cultivating new-quality productive forces in surveying and mapping lies in shifting from traditional surveying-based support to spatiotemporal intelligent services, thereby promoting the evolution of surveying and mapping geographic information toward spatiotemporal intelligent science.

A key scientific question for the large-scale operation of the low-altitude economy is how to establish a continuous, reliable, and controllable spatio-temporal cognition framework for low-altitude space under infrastructure-degraded conditions-where external communication, navigation, and other support systems are intermittent or unavailable-so as to enable intelligent perception, safe regulation, and sustainable governance of low-altitude airspace. To address this question, this paper proposes and systematically elaborates a low-altitude spatio-temporal support framework structured around three components: unmanned autonomous surveying and mapping as the foundational driver, the low-altitude digital-intelligent brain as the core hub, and the self-sustaining capability of low-altitude unmanned systems as the safety baseline. The paper analyzes the underlying logic of “data-driven input-platform-level empowerment-capability-level safeguarding” among the three components, and clarifies the dual role of unmanned autonomous surveying and mapping in the low-altitude economy: it serves simultaneously as the primary data source enabling the low-altitude digital-intelligent brain to achieve high-real-time situational awareness and intelligent scheduling, and as the intrinsic capability foundation allowing low-altitude unmanned systems to maintain safe and autonomous operation under infrastructure-degraded conditions. Building on this, the paper systematically presents the “1+2+3+N” overall architecture of the low-altitude digital-intelligent brain, the five-dimensional self-sustaining capability of low-altitude unmanned systems, and the development trends of swarm intelligence oriented toward large-scale operations. This paper argues that unmanned autonomous surveying and mapping is not a peripheral supporting tool for the low-altitude economy, but rather the critical link connecting low-altitude infrastructure with low-altitude intelligent agents-its capability boundary directly determining the safety baseline of the low-altitude economic operating system. The findings offer a theoretical framework and methodological reference for the intelligent development and systematic governance of low-altitude space.

Low-altitude economic operations require machine systems to achieve autonomous perception, planning, and management of low-altitude airspace, with the fundamental prerequisite that the physical low-altitude environment must first be transformed into a machine-processable digital form. The computabilization of the low-altitude environment is essentially a geospatial problem, and geospatial surveying and mapping technology is the core discipline for transforming physical geographic space into digital computable form. In this process, geospatial surveying and mapping technology constitutes an irreplaceable foundation for the computabilization of the low-altitude economy across three dimensions: the dynamic robust estimation capability of positioning technology supports real-time autonomous navigation of UAVs in complex unstructured environments; the geometric-topological dual representation capability of GIS supports the structural transformation of low-altitude airspace from a static digital foundation to a dynamically plannable route network; and the semantic understanding and information extraction capability of remote sensing supports the decision-level transformation of the flight environment from geometric description to risk quantification. This paper systematically reviews the supporting mechanisms and technical boundaries of geospatial technologies across the full value chain of the low-altitude economy along four layers—spatial cognition, airspace utilization, flight execution, and airspace management—analyzes their stratified contributions at each stage of the upstream, midstream, and downstream industrial chain, and verifies the engineering feasibility and application boundaries of relevant technical solutions through typical cases of low-altitude inspection and logistics. This paper proposes for the first time a four-layer technical framework for achieving the computabilization of the low-altitude economy from a geospatial perspective, reveals the irreplaceable supporting role of geospatial information in the low-altitude economy, and provides a systematic reference for theoretical research on the low-altitude economy and the interdisciplinary development of the geospatial discipline.

Landfills will continue to experience long-term settlement due to biodegradation and compression after closure, with strong spatial variability and complex temporal characteristics. Against the backdrop of the transition of current urban solid waste disposal facilities toward safer and more long-term management models, the evolution of surface settlement after landfill closure has become particularly important, urgently requiring surface deformation monitoring and risk assessment. This paper takes the Guangdong Xingfeng landfill, once the largest single treatment facility in Asia, as an example. It integrates Envisat and Sentinel-1 data, utilizes displacement tracking technology to process different satellite data to fill in deformation gaps during data gaps, and combines the small baseline subset technique to construct an 18-year long-term deformation sequence, while predicting future deformation trends. The monitoring results show significant spatially uneven settlement, with the degree of unevenness highly correlated with landfilling processes and waste types. The eastern landfill area exhibits severe surface settlement (4.4 m of cumulative settlement between 2008 and 2025), while the western emergency landfill area shows relatively stable surface changes (1.9 m of cumulative settlement between 2008 and 2025). The landfill settlement shows a strong correlation with climatic factors, with a 3~4 month lag effect, revealing the control of climate change on the surface stability of landfills. The deformation prediction results indicate that for a long period in the future, the landfill will remain in an uneven settlement state, with the eastern landfill area requiring 8~10 years to achieve surface stability and the western landfill area reaching surface stability in 5 years. This study achieves the identification of the full-cycle deformation characteristics of landfills through long-term deformation monitoring and quantifies the deformation characteristics of each division using deformation decomposition. Based on the ConvLSTM model, it evaluates the future deformation, risk characteristics, and surface stability time of the landfill. The research approach can provide a reference for the long-term full-cycle surface deformation and risk assessment of similar landfills.

The total solar eclipse on April 8, 2024, offered a unique opportunity to investigate short-period ionospheric disturbances, the mechanisms of which were less well understood than the large-scale effects induced by eclipses. This study used data from high-density GPS stations, ionosondes, and balloon radiosondes across the United States to analyze the propagation characteristics of short-period ionospheric disturbances. ① All GPS stations located along the totality path observed ionospheric disturbances with fluctuation periods of 14~45 min and a central period of 19~34 min. The maximum negative disturbance appeared 4~8 min after totality, and the disturbances propagated along the direction of the totality path. Similar ionospheric disturbances were also detected in the detrended F2-layer critical frequency time series from ionosondes. In contrast, the detrended total electron content time series from GPS stations at progressively greater perpendicular distances from the totality path showed that the amplitude of ionospheric disturbances gradually decreased with increasing distance from the totality path. ② During the eclipse, short-period ionospheric disturbances resembling bow waves were observed. The disturbances were primarily concentrated near the path of totality and located predominantly behind the moving umbral shadow. ③ Two distinct types were identified based on the propagation characteristics. The first type propagated at a speed consistent with the movement of the total solar eclipse, with a propagation trajectory highly coincident with the eclipse path, and was unlikely to be a large-scale traveling ionospheric disturbance excited by gravity waves. The second type of disturbance had a horizontal phase velocity of 296~312 m/s, a horizontal wavelength of 340~393 km, and a period of 21~24 min, propagating outward from the totality center to both sides of the path. These are very likely medium-scale traveling ionospheric disturbances caused by eclipse-induced gravity waves. Balloon radiosonde observations indicate that this disturbance was very likely influenced by lower-atmospheric gravity waves.

Satellite attitude models influence variations in the Yaw angle, thereby changing the line-of-sight projection of the satellite antenna phase center offset (PCO) and the phase Wind-up correction, and ultimately affecting the modeling and positioning results of precise point positioning (PPP). Such effects become more pronounced under attitude-sensitive conditions, such as low-β angles and eclipse periods. In this study, attitude quaternion products (OBX/ORBEX) released by the International GNSS Service (IGS) are introduced. Under a unified PPP processing strategy and with consistent precise products from the same analysis center, differences between the nominal attitude model and quaternion-based attitude products from four analysis centers—Wuhan University (WUM), the Center for Orbit Determination in Europe (CODE), the German Research Centre for Geosciences (GFZ), and the Groupe de Recherche de Géodésie Spatiale (GRG)—are compared for four GNSS constellations: GPS, BDS, Galileo, and GLONASS. Their impact path is further investigated along the “Yaw-PCO-Wind-up-PPP” chain. The results show that, within attitude maneuver windows, Yaw angles can undergo rotations close to±180°, and different analysis centers exhibit discrepancies in maneuver evolution and branch selection. The attitude-induced PCO differences are 2~3 cm for GPS, 0.7~0.8 cm for BDS, about 1 cm for Galileo, and up to 10~16 cm for GLONASS. Phase wind-up differences are also significant during maneuver periods, reaching 0.8~1.0 cycles for GPS and BDS, while being relatively smaller for Galileo and GLONASS, at 0.2~0.4 cycles. Three-day PPP results and multi-station statistics indicate that quaternion-based attitude products can reduce the three-component PPP RMS in most cases, but the improvement exhibits clear system-dependent and analysis-center-dependent characteristics. Specifically, the improvement for GPS is limited, with a maximum of 7.96%; BDS shows the most significant improvement, reaching up to 22.05%; Galileo performs relatively stably, with a maximum improvement of 13.39%; and GLONASS shows relatively small improvement, with a maximum of 7.78%. Slight degradation may still occur in a few combinations, with a maximum of about－0.89%. In summary, quaternion-based attitude products can significantly affect PPP accuracy during attitude-sensitive periods, but their practical application still requires system-specific and analysis-center-specific evaluation and quality control.

Precise delineation of continuous surface elevation changes on ice shelves is critical for understanding ice-ocean interactions. However, existing observation frameworks are constrained by the sparse spatial sampling of satellite altimetry and the topographic advection artifacts inherent to high-resolution optical imagery under traditional Eulerian differencing. To address this issue, this study proposes a long-term, high-resolution retrieval method for surface elevation change rates based on REMA optical stereo strips. The method uses ITS_LIVE velocity fields to drive material-point backtracking and establishes a Lagrangian spatiotemporal matching model under the absolute elevation constraints of CryoSat-2 Swath altimetry, thereby effectively suppressing geometric artifacts arising from ice-flow advection. Using the Dotson Ice Shelf as a case study, and based on REMA strips and multi-source satellite observations from 2010 to 2022, we reconstructed a time series of surface elevation change rates on a 50 m common analysis grid using a strategy that combines long-term trends with sliding windows. Results show that: ① compared with the traditional Eulerian framework, the proposed method reduces the core error metric (NMAD) by 51.6% and substantially weakens spurious elevation-change signals in fast-flowing regions; ② independent validation against ICESat-2 ATL06 data yields RMSEs of 4.64 m and 2.97 m for the long-term retrieval and near-contemporaneous single-strip comparison, respectively, confirming the stability of the framework; ③ based on the constructed thinning intensity classification system, intense thinning zones (<－1 m/a) are highly concentrated near the grounding line and channel core areas (accounting for 9.2% of the area); the central Dotson melt channel exhibits a nonlinear evolution of “acceleration (2010—2016) followed by deceleration (2016—2022)”, while the grounding-line vicinity shows a continuous thinning trend. These results indicate that, under unified physical corrections and a Lagrangian framework, REMA strip data can be used to construct continuous products of ice-shelf surface elevation change rates, thereby providing finer observational constraints for analyzing BMC-related surface responses in fast-flowing ice shelves.

Global navigation satellite system (GNSS) code biases are primarily characterized by three forms: observation-specific signal bias (OSB), differential code bias (DCB), and inter-frequency clock bias (IFCB). Precise processing of these code biases is critical for achieving consistency in observation data across different GNSS frequencies. However, due to differences in estimation models, error handling strategies, and reference definition, code bias products from different sources and in different forms exhibit inconsistencies. To address this issue, this study derives a compatible and unified functional model for estimating different forms of code biases and establishes a unified correction method for these code bias forms. Using global observation data from the BeiDou-3 Global System (BDS-3), the three forms of code bias (OSB, DCB and IFCB) are estimated and the results are evaluated. Results demonstrate that systematic biases exist between the code biases estimated from the uncombined model and external products, which are attributed to inconsistencies in the clock datum. Additionally, high consistency is observed among the OSB, DCB, and IFCB estimates, with root mean square (RMS) differences all within 3.0×10^－3 ns. The estimated code biases are further applied to precise point positioning (PPP), achieving unified correction of multi-form code biases at the clock offset level. Experimental results demonstrate that, compared with using external OSB products, the code biases estimated in this study led to average reductions in the RMS of 3D positioning errors during the convergence period by 4.9% and 9.3% for the B1I/B2a and B1C/B2a frequency combinations, respectively. All three code bias forms demonstrated consistent positioning performance during both convergence period and after convergence. Therefore, in practical applications, users can flexibly select any form of code bias according to their data conditions to achieve consistent and high-precision positioning results.

LuTan-1 (LT-1) as the L-band bistatic InSAR satellite system, features strong penetration capabilities and holds great potential for retrieving high-precision sub-canopy topography. However, due to the influence of volume scattering in forests, the LT-1 SAR echo signal contains mixed contributions from both the ground surface and forest canopy. The single-polarization LT-1 InSAR data lacks sufficient observational information to effectively separate forest scattering components. Moreover, in current practices, system errors in terrain estimation are typically corrected using a block adjustment model with spaceborne LiDAR data as ground control points. However, these methods neglect the mismatch in scattering centers between InSAR and LiDAR in forested areas, and the alternative approach of relying solely on bare ground control points suffers from the limited number of such points. To address these issues, this study proposes a frequency-domain physical interpretation method based on sub-aperture decomposition to mitigate the issue of insufficient observational information. On this basis, a block adjustment model based on multi-scale least-squares collocation is developed to solve the problems of inconsistent scattering centers and the coupling of system errors and scattering model errors. The proposed method is tested and validated in two study areas with different terrain and forest conditions, namely Guangzhou and Genhe test site. The root mean square errors of the estimated sub-canopy topography are 3.09 and 1.36 m, representing improvements of 47.4% and 70.2% over the conventional InSAR DEMs (5.87 and 4.56 m), respectively. Furthermore, the proposed method exhibits superior elevation accuracy compared to existing global DEMs and publicly available sub-canopy topography products.

Improving the adjustment accuracy of stereo mapping satellite imagery under uncontrolled conditions is a critical step for acquiring geographic information from overseas or hard-to-reach areas (such as remote islands). Addressing the challenge of limited satellite laser data volume for uniform control on small islands far from the mainland, this study proposes a joint adjustment method for island stereo image pairs and satellite-based laser altimetry points based on shoreline constraints. First, by leveraging the consistent elevation characteristics of waterlines surrounding islands and reefs, spatial coordinates are obtained through vectorization of image waterlines. Second, elevation constraints from the ALOS World 3D-30 m (AW3D30 DEM) are applied to refine the ICESat-2 ATL03 data. Finally, joint adjustment is performed by integrating image waterline points, ATL03 laser photon data, and stereo image pairs to improve the elevation accuracy of the regional network imagery. Joint adjustment experiments were conducted using multiple stereo image pairs from Wuzhizhou Island and Ganquan Island captured by ZY-3 and GF-7 satellites. Results showed that the root mean square error (RMSE) of elevation measurements for Wuzhizhou Island decreased from an average of 4.07 m to 1.54 m, while Ganquan Island's RMSE decreased from 7.06 m to 0.91 m. These improvements represent accuracy gains of 62% and 87%, respectively, demonstrating that this method can achieve ideal joint adjustment results between satellite-borne laser data and island stereo image pairs, with a particularly pronounced enhancement effect for islands with flat topography.

Precise alignment monitoring for long-distance linear infrastructure such as railway tracks, pipelines and bridges serves as a critical safeguard for operation and maintenance safety. In recent years, integrated inertial alignment measurement has been increasingly widely adopted in alignment variation monitoring, benefiting from its dual advantages of high efficiency and high precision. Nevertheless, the errors inherent in this measurement method feature strong nonlinearity, multi-source coupling and spatiotemporal accumulation, rendering traditional analytical methods incapable of accurate quantitative analysis. To tackle this practical engineering challenge, this paper proposes a Monte Carlo-based method for error simulation and accuracy evaluation. Specifically, high-fidelity trajectory generation is realized through spline function fitting combined with inverse calculation of inertial mechanization; a multi-source error coupling model is established, and realistic sensor errors are injected via the Monte Carlo approach; trajectory reconstruction is performed using Kalman filtering integrated with Rauch-Tung-Striebel (RTS) smoothing; and visualized assessment of alignment measurement errors is achieved by means of error ellipses. Simulations are carried out for measurement tasks on straight and U-shaped alignment structures, represented by pre-embedded pipelines inside dams and railway tracks. Validated with field measurement data from dam pipelines, the correlation coefficient between simulation results and measured data reaches 87.3%. Further experiments investigate the influence patterns of key factors including sensor accuracy, moving velocity and control point spacing on measurement accuracy. This study develops an integrated methodological framework covering trajectory generation, error injection, trajectory reconstruction and accuracy evaluation. It can effectively support the optimization of engineering schemes such as sensor selection and control point deployment, and provides technical reference for deformation monitoring of long-distance linear engineering structures.

Segment joints are critical weak points affecting the structural safety of shield tunnels, and their accurate recognition is essential for structural health monitoring. Rail-borne mobile laser scanning (RMLS) provides a high-quality data foundation for the recognition of segment joints. However, noise, occlusions, and uneven point cloud density in tunnels reduce the accuracy and robustness of existing methods. To address these limitations, this study proposes a shield tunnel segment joint recognition method based on the segment joint multi-measure interaction (SJMI) index. The method first removes clearly non-lining points using a cross-sectional ellipse fitting approach. Next, a SJMI is constructed by combining a bilateral wedge-fall geometric measure with an intensity residual measure, quantitatively capturing the distinctive characteristics of segment joints. Finally, the constructed SJMI index is used to sequentially recognize circumferential and longitudinal joints with high accuracy. Experiments on real RMLS point cloud data demonstrate that the proposed SJMI-based method effectively captures segment joint features, improves the recognition of actual joints, and suppresses noise and false positives. Quantitative evaluation shows that for circumferential joints, the IoU, recall, and precision reach 90.21%, 90.32%, and 99.84%, respectively, while for longitudinal joints, the corresponding metrics are 94.52%, 95.89%, and 98.23%. Compared with the current best-performing method, the IoU of circumferential and longitudinal joints increased by 2.25 percentage points and 7.96 percentage points, respectively, clearly demonstrating the significant accuracy advantage of the proposed method. In addition, the SJMI-based approach maintains strong robustness in complex tunnel environments and provides a reliable technical basis for the digital operation and maintenance of shield tunnels, segment joint anomaly detection, and structural health assessment.

With the rapid development of urban rail transit, the demand for water leakage detection of subway tunnels is increasing. There are many attachments on the surface of tunnel lining, which block the water leakage area, and some of the water leakage boundaries are blurred, which brings great challenges to the detection. The existing methods have insufficient ability to capture the edge features of water leakage in complex tunnel background, which limits the detection accuracy and robustness of water leakage areas with blurred boundaries or variable shapes. In view of the above problems, this paper proposes a tunnel water leakage detection method based on multi-scale edge information fusion. The method consists of multi-feature fusion image enhancement algorithm and global-local edge information fusion YOLO (GLEF-YOLO) detection model. In the image preprocessing stage, the wavelet transform enhanced anisotropic diffusion filtering and adaptive histogram equalization are combined to enhance the edge details and local contrast of the water leakage area. In the detection model, a multi-scale edge information transfer module (MEIT) and a global-local perception fusion module (GLAFusion) are introduced to transmit shallow edge information across layers, and local details and global semantic features are fused to improve the recognition ability of the model for irregular water leakage targets in complex backgrounds. The experimental results show that the mAP of GLEF-YOLO is 8.4 percentage points higher than that of the baseline model. Compared with the recent excellent target detection methods, it has achieved the best results in all indicators, and has higher accuracy in the detection of water leakage in complex backgrounds, and is more robust to pipeline, appendage occlusion and background texture interference. The effectiveness of the proposed method is verified, which provides an effective solution for the intelligent and high-precision detection of tunnel water leakage.