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As the elderly population rises, the growth rate of age-related diseases such as Alzheimer’s disease and cognitive impairments increases. Wandering is one of the first and the most progressive and challenging behaviors; that manifest with certain patterns in the mobility behavior of the patients in the early stages of the disease. Timely diagnosis of wandering can prevent irreparable damages, including the risk of losing the patient, severe physical injuries caused by accidents, and even death. So far, numerous studies have focused on the development of wandering detection algorithms. Most of these mainly rely on two approaches of extracting features from patients’ trajectory history and estimating the path complexity. Motion signal processing methods have rarely been used in this area. An important consideration for these patients is the instability in their movement behaviors, which has received limited attention in previous research, leading to less compatibility of models with this feature. Therefore, this paper proposes an algorithm based on motion signal processing using the Short-time Fourier transform (STFT). This algorithm detects wandering patterns only by using the patient’s trajectory data and changes in their zero and non-zero frequency components. The efficiency of the proposed algorithm has been evaluated using the Geolife open-source dataset, considering the macro-average of metrics such as accuracy, precision, specificity, recall, and F-score, achieving respective values of 96.38%, 94.89%, 96.36%, 96.36%, and 95.58%. The results have validated the proposed algorithm’s strong performance in diagnosing wandering behavior, which, on one hand, helps prevent adverse consequences and, on the other hand, aids in the diagnosis and predicting the progression of the disease to severe Alzheimer’s stage.

Hyperspectral datasets captured by airborne are one of the important data sources in the field of hyperspectral image fusion. However, the limited number of data samples often hinders the optimal performance of deep learning-based methods on these datasets. Domain adaptation is expected to alleviate the issue of data scarcity. However, there are two issues that need to be addressed in applying domain adaptation to hyperspectral image fusion, which are the heterogeneity and spatial degradation diversity of hyperspectral data. In this paper, we propose a domain adaptation hyperspectral image fusion network based on spatial-spectral domain separation. The model, constructed with three-dimensional convolutional layers, effectively addresses the challenge of hyperspectral data heterogeneity. Compared with 2D convolution, 3D convolution is able to consider both spatial and spectral dimensions and extract spatial-spectral features efficiently. Based on this, we design a domain-separation architecture to extract domain-invariant and domain-private features in the spatial-spectral domain. The architecture achieves domain adaptation by extracting domain-invariant features from both datasets to obtain the prior knowledge in the source dataset and apply it to the target dataset. Additionally, the spatial degradation of different datasets varies due to distinct shooting conditions during the acquisition. Therefore, the fusion algorithms should be robust to various degradation scenarios. To tackle this challenge, we design the Observation Module to predict degradation information and the Degradation Information Modulation Module to apply it to the input, thereby enhancing the network’s robustness. Experiments on various datasets demonstrate that our method is qualitatively and quantitatively superior to state-of-the-art methods.

Connectivity enhancement of ecological networks is an efficient and advantageous spatial planning strategy, and it is a hot research topic to analyze ecological networks based on complex systems theory and perform model optimization. As a typical geographic network, the evolution process of ecological network, such as node and edge growth, will be subject to geospatial constraints, and the current research lacks optimization models that take the geospatial constraints into account. This study focused on urban ecological network connectivity and the distribution of nodes as well as structural fractures where the connectivity of the ecological network was hindered were analyzed. Then it introduced a local world network model to optimize network connectivity and designed a network growth mechanism with limited added nodes and optimal connections. An ecological network optimization model considering geospatial constraints was constructed, and the network closeness index was used to fit the objective function curve to select optimization schemes. This study used Shenzhen, China as a case study, and the results indicated that the initial ecological network was significantly fractured by urban construction. In the optimization scenario, the network evolution reached its optimal state at the simulation step size of t = 26. At this point, the closeness index of the optimized network was increased by 15%. The optimization model constructed in this paper emphasizes the importance of functional restoration of existing network nodes, and the results of the study can provide support for ecospatial layout optimization and management.

To enhance the adaptability and application capability of the cloud detection model in different remote sensing satellite domains, unsupervised domain adaptation methods are employed to improve the model’s robustness and transferability. However, conducting fully unlabeled training in the target domain to be adapted may impose difficulty on further improving the accuracy. It is more practical to obtain a few labeled target data for domain adaptation. Additionally, the detection of complex cloud scenes remains a persistent challenge. To address these issues, an Active Domain Adaptation network for Cloud detection (ADAC) is proposed in this paper. This network employs active learning to select a small number of representative samples for annotation, facilitating the transfer of a source domain-trained model to the target domain. Moreover, a novel Generator (G) designed for cloud segmentation is developed, where G utilizes a progressive learning strategy by dividing the network into two stages to learn gradient information, guiding cloud boundary segmentation and enabling the distinction between cloud-like objects and clouds. Furthermore, G integrates Transformer and Convolutional Neural Network to address the issues of misclassifying thin clouds. The proposed method is validated on Landsat 8 and GF-2 datasets, representing the source and target domains, respectively. The experimental results demonstrate ADAC achieved a 4% improvement in the overall accuracy with only 5% labeled training samples in the target domain, achieving the effective adaptation performance in the GF-2 domain.

High-resolution digital elevation models (DEMs) of the lunar surface are crucial for lunar exploration missions and scientific research. The emergence of advanced technologies, such as Shape-from-Shading (SfS), has enabled the generation of pixel-wise high-resolution DEMs from monocular images of the lunar surface. However, SfS encounters significant challenges in locations with limited illumination, such as the lunar south pole, where the surface is largely covered by shadows. Therefore, this paper presents a novel shadow-constrained SfS approach for pixel-wise 3D surface reconstruction at the lunar south pole. The proposed approach uses multiple high-resolution images captured under different illumination conditions and an existing low-resolution DEM as the inputs and generates a high-resolution DEM with the same resolution as that of the input image through hierarchical SfS processing incorporating shadow constraints. Experiments were conducted using actual images collected by the Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) at the lunar south pole. Comparisons with respect to photogrammetric DEMs generated from stereo NAC images show that the DEMs generated using the proposed approach exhibit the smallest root mean square error (RMSE). Moreover, shaded relief images rendered from the DEMs generated using the proposed approach demonstrate the highest similarity to the actual NAC images. Detailed profile comparisons further validate the effectiveness of the shadow constraint in optimizing 3D reconstruction in regions in proximity to shadows and within shadowed regions. The proposed shadow-constrained SfS approach can be used to generate high-resolution DEMs to support future missions to explore the lunar south pole, with applications including landing site evaluation and route planning for lunar probes or astronauts.

Building use identification is crucial in urban planning and management. Current identification methods often rely on a single data source and neglect spatial proximity. In this paper, we propose a stepwise urban building use identification framework that integrates remote sensing and social sensing data with spatial constraints based on the association of buildings with Point of Interest (POI), Area of Interest (AOI) and remote sensing data. First, the study data are preprocessed using geometric correction and POI and AOI reclassification. Then, we identify buildings with the quantitative-density index of the POIs as well as the spatial relationships between the AOIs and the buildings. Next, we generate Traffic Analysis Zones (TAZs) from road network data and utilize the similarity in physical features of buildings from remote sensing data to identify building use within spatial constraints. Finally, POI kernel density estimation is used to determine the semantic features of the buildings, and the similarity of the features between the buildings is utilized to identify the remaining buildings. The specificity of our proposed framework lies not only in the combination of multiple source data at the building-level but also in the introduction of the spatial relationships of AOIs and spatial constraints. Shenyang is selected as an example. The proposed framework identifies buildings as commercial, residential, industrial, public service and scenic spots. The accuracy assessment indicates that the proposed framework performs well, with an Overall Accuracy (OA) of 87.1% and a kappa coefficient (kappa) of 73.4%. The results of the comparison experiments show that the consideration of spatial constraints and the integration of multiple data sources help to improve the accuracy of building use identification. The proposed framework provides a new tool for better identification of urban building use, and the generated data are suitable for in-depth analyses such as building-level urban heat islands.

Travel mode recognition is a key issue in urban planning and transportation research. While traditional travel surveys use manual data collection and have limited coverage, poor timeliness, and insufficient sample capacity, recent advancements in Global Positioning System (GPS) technology allow large-scale data collection and offer novel opportunities to enhance travel mode recognition. However, existing studies often neglect regular differences and changes in motion states across different travel modes and fail to fully integrate multi-scale spatio-temporal features, which limits the accurate classification of travel modes. To fill this gap, this study proposes a multi-scale spatio-temporal attribute fusion (MSAF) model for precise travel mode identification using solely GPS trajectories without altering their sampling rate. The MSAF model segments GPS trajectories into various temporal and spatial scales, extracting local motion states and spatial features at multiple scales. The spatio-temporal feature extraction module is constructed to extract local motion states and capture spatio-temporal dependencies. Additionally, the model incorporates a multi-scale feature fusion module, which effectively combines features of various scales through a series of fusion techniques to obtain a comprehensive representation, enabling automatic and accurate travel mode identification. Experiments on real-world datasets, including the GeoLife Trajectories dataset and the Sussex-Huawei Locomotion-Transportation (SHL) dataset, demonstrate the effectiveness of the MSAF model, achieving a competitive accuracy of 95.16% and 91.70%. This represents an improvement of 2.50% to 7.95% and 0.8% to 6.62% over several state-of-the-art baselines, effectively addressing sample imbalance challenges. Moreover, the experiments demonstrate the significant role of multiscale feature fusion in improving model performance.

Aiming at the issue of low accuracy in crop mapping and growth monitoring caused by imprecise calibration of radar time-series data, this paper proposed a Synthetic Aperture Radar (SAR) spatio-temporal error compensation method. By constructing a compensation reference image using the buildings with stable backscattering coefficients in the study area, the error correction of time-series SAR data was realized based on the compensation model. The compensated SAR data were used to establish the Sugarcane Growth Index (SGI) according to the growth curve of sugarcane. The sugarcane area was extracted after combining the compensated SGI and optical image, and the accuracy of F1 on sugarcane distribution extraction was 92.12%. There was an improvement of 4.11% compared with no compensation method. On this basis, the newly planted and ratoon sugarcane were further classified by the expectation maximization algorithm based on the compensated SAR image at the seedling stage. The newly planted sugarcane and the ratoon sugarcane could be accurately distinguished with area extraction accuracy of 82.06% and 80.59%, respectively. Moreover, the compensated SAR data of time series during the tillering to maturity stages were used to estimate the sugarcane height, yielding an R2 value of 0.717 using a quadratic polynomial model. The Spatio-temporal error compensation method for distributed targets proposed in this study can reduce the loss of SAR data across various times and regions, and achieve accurate results of sugarcane distribution extraction and plant height estimation.

Urban land use information is essential for urban planning and sustainable development. However, the increasing prevalence of Mixed-Use Urban Land (MUUL) introduces uncertainty in land use mapping. Due to the compatibility of different human activities within MUUL, identifying MUUL is still a challenge for scholars. In this study, we proposed a novel framework by integrating geographic big data and machine learning to identify it. First, to be consistent with urban planning practices, we delineated mapping units from urban planning map. Second, based on the variability of the spatial pattern of POIs, two indices, the Amount of POI (AP) and the Deviation Index (DI), were developed. Finally, the random forest model was employed. The empirical study in the Jianghan District of Wuhan, China, showed that the MUUL and non-MUUL exhibited significant spatial pattern of POIs separability, the average AP and DI of the MUUL were much larger than those of the non-MUUL. Moreover, the relatively high identification accuracy (Kappa Coefficient (KC) = 0.85, Hellden’s Mean Accuracy (MA) = 0.87) further demonstrated the effectiveness of the developed indices. The proposed framework in this study can reduce the adverse impacts of MUUL on urban land identification by avoiding the MUUL from being incorrectly identified as a single function. Moreover, by extracting MUUL in advance, it can improve the research efficiency by avoiding the application of the segmentation algorithm to all mapping units as in the previous study. Overall, this study provides important references for scholars to increase the accuracy of urban land use identification and allows authorities and policymakers to monitor the dynamics of MUUL that are essential to urban vibrancy. The spatial pattern indices proposed in this study can also be applied to urban land use identification studies as important features.

Customer numbers and comments have been extensively studied to increase hotel arrivals and loyalty, whereas the specific distribution and sentiment variations have received less attention. This study constructed a data set of hotel customers and the deep learning model was applied to quantify unstructured text, which can mine the distribution and sentiment variation patterns of different star hotel customers. The findings show that customers of different star-rated hotels have different variation patterns of sentiment and distribution. There is a negative correlation between the visit heat and sentiment of some high-star hotels in various administrative regions: regions with lower visit heat have higher sentiment scores. In addition, season and location are important factors that affect customers’ sentiments. This study is the first to use a quantitative approach to examine the distribution and sentiment variation patterns of hotel customers. It provides insights into appropriate marketing strategies for the hotel industry and offers practical suggestions to hotel customers.

The global outbreak of the novel coronavirus has had a great impact on commercial areas, particularly in Wuhan, the first city in China affected by the epidemic. Exploring the changing pattern of these areas during the epidemic period is crucial for effectively restoring the urban economic level and promoting economic development on the basis of epidemic prevention and control in the post-epidemic era. The paper proposes a resilience analysis of urban commercial areas during the COVID-19 epidemic using time-series night-time light remote sensing data. Based on the constructed time-series night-time light remote sensing dataset and considering the urban impervious surface, the urban commercial area was extracted under multi-scale segmentation. Based on night-time light data before and after the epidemic, the resilience of representative commercial areas in Wuhan City, Hubei Province during the COVID-19 epidemic was analysed using a modified improved resilience assessment framework. The results showed that the lighting values of commercial areas and impervious areas decreased by up to 30% following the implementation of city lockdown measures. The change of light brightness in the commercial areas was more prominent, and the range of change is larger than that in the non-commercial areas. The lighting brightness of the commercial areas exhibited a significant downward trend throughout the entire lockdown period, and the downward trend will still remain for a period of time after unlocking. Among all the commercial areas, Wuguang commercial area demonstrated the highest recovery rate during the lockdown period and instantaneous recovery rate after the lockdown, with relatively low closure loss and the highest level of resilience.

Disposal of municipal solid waste is a significant environmental issue that has detrimental effects on both the environment and human life. The selection of landfill sites is critical and essential to implementing a comprehensive waste management strategy. This study optimizes the selection of landfill sites in Kuwait through the use of overlay analysis and Geographic Information System in conjunction with multi-criteria decision analysis. The study analysis takes into account human, physical economical, environmental, and governmental factors. Seven optimal locations are depicted on the resulting suitability map, each occupying 10 square kilometres or more and have the capacity to handle solid waste for at least one decade per site, considering the current reported waste generation rate. Furthermore, results of a preliminary geotechnical investigation encompassing field and laboratory tests conducted on soil samples collected at one of the proposed sites are presented. The characterization of the retrieved samples included major geotechnical parameters that are of concern in landfill design and siting studies. The results indicate that the soil at the proposed site exhibited low values of hydraulic conductivity, ranging between 3.50 X 10−07 and 8.96 X 10−08 cm/sec, confirming its suitability for siting landfills. There is an urgent need to develop and implement an integrated waste management system in Kuwait. By leveraging the findings of this study, policymakers and governmental authorities can optimize their decision-making procedures to ensure more effective site selection. This approach will significantly reduce contamination risks and enhance sustainability, leading to a more environmentally responsible waste management system in the country.

The façades of photogrammetric building mesh models frequently lack detailed semantics, such as windows, doors, and balconies. To address this, we introduce SemanticMesh, a methodology designed to enrich façades with detailed semantics by integrating discrete components seamlessly. The process begins by transforming a localized area of the building surface into a 2D planar mesh using geodesic vectors, based on the component poses. This is followed by the application of 2D constrained triangulation to map the component boundaries onto the parameterized plane, after which the mesh is reconverted into 3D space. To achieve a smooth and aesthetically pleasing integration, we employ a Laplacian mesh deformation technique along the 3D embedding boundary. Our experiments across three distinct datasets – featuring near-planar, non-planar, and noisy surfaces – demonstrate that SemanticMesh provides superior modeling outcomes compared to conventional approaches.

The ecological quality of a region is significantly influenced by its geographical conditions, which can yield different effects on ecosystems. Nevertheless, the lack of adequate technology has impeded quantitative investigations into these differences. Consequently, there is an increasing demand for effective techniques to quantitatively measure differences in ecological quality resulting from variations in geographical conditions. This study applied the novel Remote Sensing-based Ecological Index (RSEI) concurrently to two distinct provincial-level regions in China, Fujian and Ningxia, to quantitatively detect their ecological differences. These two regions possess contrasting geographical conditions, with Fujian having high forest coverage and abundant rainfall, while Ningxia features low forest coverage and extensive loess plateau and desert terrain. By linking geographical factors with their corresponding ecological responses, we conducted a comprehensive analysis to determine whether the contrasting geographical conditions between the two regions had caused significant disparities in their ecological status. The results indicate that the contrasting geographical conditions have indeed led to marked ecological differences, with Fujian exhibiting excellent ecological status, while Ningxia lags behind due to unfavorable geographical conditions. In terms of RSEI scores, Fujian consistently achieved higher RSEI values (>0.8) in the study years, reaching an excellent ecological level, whereas Ningxia recorded scores lower than 0.45 during the comparable years, corresponding to a poor to moderate ecological level. Regarding the impact of geographical factors on ecological conditions, the positive contributions of greenness and wetness indicators to the ecology in Fujian were significantly greater than those in Ningxia (58% vs. 39%), whereas the contributions of negative indicators, dryness and hotness, were notably higher in Ningxia compared to Fujian (|–61|% vs. |–42|%). The successful concurrent application of RSEI to these two geographically distant regions also demonstrates the robustness of the RSEI technique.

Spatial synergy is strengthened integration and connection between cities in a mega-city region, transcending administrative boundaries. The central flow theory suggests that the mega-city regions are formed by the interconnected flows of people across cities, making the spatial synergy can be measured by assessing the aggregation and intensity of flows and interactions between cities and regions. Human mobility data, such as mobile phone data and social media check-ins, enable the tracking of human movements, thus facilitating the transition of central flow theory from theoretical constructs to empirical research. To this end, this study presents an alternative data-driven framework to reveal the multi-scale spatial synergy of mega-city regions from a human mobility perspective. It uncovers homogeneously spatial communities with high inter-city integration using community detection. Strong internal spatial connections of 2.13 billion mobility are filtered using network backbone extraction. An experiment in the Pearl River Delta (PRD), China, demonstrates a multi-scale and multi-core hierarchical spatial synergy in the PRD region. The detailed findings are as follows: (1) Three cities attract the majority of human mobility. Mobility distance is short in urban centers and long in suburban areas. (2) The spatial integration pattern shows the detected communities reveal the hierarchical integration pattern with three main integrated regions: Guangzhou-Foshan-Zhaoqing, Shenzhen-Dongguan-Huizhou, and Zhuhai-Zhongshan-Jiangmen. (3) The spatial connection pattern illustrates the close ties of 9 cities and three core cities, including Guangzhou, Shenzhen, and Foshan. These results provide a human-centric understanding of urban synergy and deeper insights into central flow theory, which supports cooperative development in mega-city regions.

The Arctic region is undergoing significant changes in maritime accessibility. This study investigates observed ship trajectories from 2013 to 2020 to demonstrate the recent trends of Arctic traffic. A notable surge in maritime activities has been observed, particularly during summer months, driven by economic interests and the increasing popularity of existing routes. Unique patterns in the northern Barents Sea have been observed where ships favour different routes based on seasonal ice conditions. Another contribution from this work is the validation of the Arctic Traffic Accessibility Model (ATAM) using the observed ship traffic data. Results show that the ATAM model underestimates the accessibility and vessel travel speed. This is largely due to outdated model parameters. The predefined ice multipliers and calculation of ice numerals used in the ATAM may not accurately reflect real-world conditions.

In the pursuit of sustainable urban development, it is imperative to address strategies for optimizing and harmonizing the configuration of urban spatial layouts, especially in response to rapid urbanization. Urban road networks play a pivotal role in shaping intricate urban spatial patterns as they intricately weave throughout the cityscape, significantly influencing land utilization, spatial arrangements, and the mobility of residents. The urban environment is a complex system teeming with multifaceted human activities. With the continuous evolution of advanced data and computational technologies, it is now feasible to construct intricate models for an in-depth exploration of urban spatial structures. In this study, we constructed an analytical framework that leveraged road network image recognition, multi-scale influence factors analysis, and predictions using codeless visual programming. Within this framework, deep learning and unsupervised clustering are applied to complete the hierarchical clustering of road networks and exploit the intrinsic association between cities. Meanwhile, hierarchical clustering and natural and humanistic attributes of cities are connected to investigate the strength of different attributes on specific clusters. The research has the following results: (1) In addition to the alignment of urban road network clustering outcomes, quantitative unevenness and geographical heterogeneity are also observed. (2) Multiple attributes have a complicated impact on urban space, including city size, and socioeconomic and geographic characteristics. Among these factors, socio-economic variables emerge as the principal drivers influencing urban spatial structure. (3) The research enhances the model’s generalization capability and predictive accuracy. As attributes increase and model learning improves, both the Area Under Curve (AUC) and F-1 scores exhibit continuous enhancements, ultimately achieving error-free predictions. The study’s findings offer valuable insights into urban spatial structures and systems, supporting urban decision-makers in addressing challenges associated with urban growth.

High-precision positioning and navigation are necessary for autonomous driving. GNSS RTK and INS integrated system is commonly used in vehicular navigation. But it suffers from severe signal reflections and blockages of GNSS signals and error accumulation of INS with MEMS-IMU in GNSS-challenging environment. In addition, high-definition (HD) maps in vector format and light detection and ranging (LiDAR) are two common options for intelligent vehicles. A tightly coupled localization method with vector HD map, LiDAR, GNSS RTK, and INS is proposed to take advantage of their complementary characteristics to accurately navigate a vehicle in a GNSS-challenging environment. The method is based on a particle filter (PF) framework. Lateral positions of particles are constrained by LiDAR measurements and lane information in the vector HD map. A constrained and damped LAMBDA searching method is proposed to update particle weights, aiming to find accurate longitudinal localization. Experimental results prove that our method can maintain sub-meter level horizontal positioning accuracy in GNSS-challenging environment, with improvements of (77%, 75%, 64% and 65%), regarding to three-dimensional position and yaw, compared to traditional GNSS-RTK/INS integration, while the improvement of the popular GNSS-RTK/INS/LiDAR integrated framework LIO-SAM is (16%, 53%, 48% and 51%).

Low Earth Orbit (LEO) stands as the primary access point for deep space exploration endeavors, encompassing a variety of space missions such as communication satellites, Earth observation platforms, and scientific research missions. In light of the expanding scope of human activities in space, the cost associated with accessing LEO has emerged as a critical focal point for spacecraft designers. Within the spectrum of proposed solutions, laser-powered vehicle has emerged as a viable avenue toward achieving cost-effective launch options. In order to significantly augment the payload capacity of laser-powered launch vehicles destined for LEO, this study introduced a novel Relay Solution Trajectory Flight Scheme. Central to this scheme was the implementation of a relay mirror system affixed to a satellite, which served to stabilize the incident angle of the laser beam directed toward the laser-powered vehicle, thereby ensuring continuous alignment between the laser beam and the vehicle’s axis throughout the duration of the flight. Following the development of comprehensive structural, propulsive, aerodynamic, and trajectory models tailored for the laser-powered vehicle, an optimization of its flight trajectory was conducted, comparing this innovative strategy with two conventional flight strategies. The outcomes of this analysis showcased a notable enhancement of 269.29% in payload capacity compared to conventional strategies that lacked a relay satellite system, within a designated 400 km LEO flight scenario. This endeavor underscored the transformative potential of advanced relay systems in reshaping payload delivery mechanisms and elevating mission feasibility levels, notwithstanding the intricate coordination challenges posed by intricate satellite systems.

The assessment of urban renewal (UR) potential aims to prioritize areas for UR, which are essential for sustainable urban revitalization. However, conventional data sources often fall short in encompassing diverse urban characteristics in the evaluation process, such as urban three-dimensional (3D) building information and the intensity of human activities. To address this gap, this study integrated 3D building data and geographic data to create a comprehensive set of 28 indicators spanning four dimensions: natural environmental conditions, land use, socio-economic factors, and building conditions. These indicators take into account vertical dimensions, dynamic aspects, and fine-scale details. Leveraging the existing UR practices as a positive example, we established an UR potential assessment model at street block scale using Presence and Background Learning combined with extreme gradient boosting algorithms (PBLXGBoost). Our findings revealed that the highest accuracy in evaluating industrial UR potential was achieved in Shenzhen (Fpb_avg = 0.80, RMSEavg = 0.21), followed by residential and commercial UR potential assessments. Conversely, other type UR exhibit lower accuracy. Street blocks with significant UR potential are predominantly located in Bao’an, Longgang, and Longhua Districts. Furthermore, employing the SHAP model to elucidate the evaluation results uncovered intricate hierarchical, positive-negative, and overlapping relationships among various factors and different UR types, where geographic big data and 3D building information showed strong correlations. The methodology proposed in this study enables objective and precise assessments of UR potential, offering valuable support for UR practice and sustainable urban development.

Recent advancements in cross-domain object detection have primarily relied on unsupervised domain adaptation (UDA) techniques to bridge domain gaps in remote sensing images. However, traditional UDA approaches require access to source domain data, creating privacy and transmission barriers. In response, we first introduce a novel setting called source-free object detection (SFOD) for remote sensing images. SFOD exclusively leverages pre-trained models from the source domain and unlabeled data from the target domain for adaptation. Our method emphasizes the detection model’s focus on domain-invariant features by ensuring consistency between the perturbed and target domains. We initiate domain perturbation by manipulating domain-specific features at both image and feature levels, guided by considerations of style and color biases. Subsequently, we employ a multi-level alignment mechanism to ensure label-level and feature-level consistency between the perturbed and target domains. Our method incorporates the Mean Teachers framework and prototype-based feature distillation to alleviate noise arising from pseudo-labels. Extensive experiments conducted across various adaptation scenarios validate the effectiveness of our approach.

The uncertainties in microwave land surface emissivity (MLSE) measurements have long limited the use of spaceborne microwave radiometer data. As an emerging observation method, Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated great potential in several land and ocean applications. In this study, a method for obtaining daily MLSE dataset in the pan-tropical region from Cyclone GNSS (CYGNSS) observations is presented and evaluated. The CYGNSS observations are first aggregated into the Equal-Area-Scalable-Earth (EASE) 2.0 36 km grid by a combined weight function of distance, time, and signal-to-noise ratio variance. Then, the method employs a pixel-by-pixel regression algorithm to conduct the daily MLSE retrieval using reference emissivity derived from the Soil Moisture Active Passive (SMAP) brightness temperature. The CYGNSS MLSE shows good agreement with SMAP MLSE, delivering an overall root-mean-square error (RMSE) of 0.022 and 0.017 for horizontal and vertical polarization, respectively, during the training set spanning the whole year of 2018. Furthermore, on the test set from January 2019 to May 2019, the RMSE values amounted to 0.030 and 0.023 for horizontal and vertical polarization, respectively. Temperature records from the International Soil Moisture Network are employed to calculate the emissivity and for in-situ validation, which yield an RMSE of 0.034 and 0.026 for the two polarizations, respectively. The proposed algorithm provides an encouraging approach to obtain accurate daily MLSE dataset for microwave remote sensing. Compared to the SMAP MLSE, the CYGNSS MLSE has a remarkable improvement of 86% in temporal resolution, greatly complementing the existing microwave emissivity datasets.

Ecosystems at the southern edge of the permafrost distribution are highly sensitive to global warming. Changes in soil freeze-thaw cycles can influence vegetation growth in permafrost regions. Extant studies mainly focused on analyzing the differences of vegetation dynamics in different permafrost regions. However, the intrinsic drivers of permafrost degradation on vegetation growth remain elusive yet. Based on the top temperature of permafrost (TTOP) model, we simulated the spatial distribution of permafrost in Northeast China (NEC) from 2001 to 2020. Using the data of the vegetation Net Primary Productivity (NPP), vegetation phenology, climate and permafrost phenology, and analytical methods including partial correlation, multiple linear regression, and path analysis, we explored the response of vegetation growth and phenology to soil freeze-thaw changes and climate change under different degrees of permafrost degradation. Overall, the start date of the growing season (SOS) was very sensitive to the start date of soil thaw (SOT) changes, and multiple regression analyses showed that SOT was the main factor influencing SOS in 41.8% of the NEC region. Climatic factors remain the main factors affecting vegetation NPP in NEC, and the results of partial correlation analysis showed that only 9.7% of the regional duration of soil thaw (DOT) had a strong correlation with vegetation NPP. Therefore, we determined the mechanism responsible for the soil freeze-thaw changes and vegetation growth relationship using the path analysis. The results indicated that there is a potential inhibitory effect of persistent permafrost degradation on vegetation growth. Our findings would contribute to the improvement of process-based models of forest dynamics in the boreal region, which would help to plan sustainable development and conservation strategies in permafrost areas.

Evaluating how land cover is being transformed is essential to identify patterns necessary to infer the change trajectories and the driving factors. This study considers the case of Nigeria, where various natural ecosystems are being converted and for which a current national scale assessment at high spatial resolution is lacking. Producing 30 m Landsat-based time-series data, we analyze change among land cover types (i.e. tree-covered area, grassland, wetland, waterbody, cropland, artificial surface, and otherland) across seven agroecological zones. The annual change intensity was assessed at multi-levels across two time-intervals (i.e. 2000–2013, 2013–2022). Distinguishing between natural land cover and human activity-related land-use, we estimate the extent of change signifying how humans have appropriated natural land cover. Insights from analysis at the interval level reveal that land transformation accelerated from 3.3% in 2000–2013 to 4.5% during 2013–2022 in all agroecological zones (e.g. rainforest, mangrove), except in Sudan savannah and Sahel savannah where speed was higher in 2000–2013 as grasslands were increasingly cultivated. Cropland expanded almost two-fold (22% to 37%), whereas tree-cover declined from 50% to 31% and wetland from 7% to 3.7% over the 23 years. Much loss of natural land cover (e.g. tree-cover, grassland, and wetland) to cropland occurred in 2000–2013 (22%) when most irrigation schemes in Nigeria were established. In contrast, the loss of natural land cover to settlement (0.9%) during 2000–2013 increased to 2.0% in 2013–2022. Of all agroecological zones, the mangrove zone was most disturbed as its persisting land cover areas reduced from 69% to 5% between 2000–2013 and 2013–2022. The amount of persisting land cover was highest in the Sudan savannah at 44% in 2000–2013 and 49% in 2013–2022. Processes of human-appropriated natural land cover in Nigeria are related to urbanization and cropland expansion into natural areas with some instances of natural regeneration, especially in croplands and abandoned settlement areas.

This paper explores the use of digital footprints data to improve on the limited information available on the size and distribution of the residential housing stock in the United Kingdom over the past two decades. We use a subset of a large dataset consisting of the names and addresses of more than one billion individuals dating back to 1997 (LCRs: Linked Consumer Registers) to calibrate property lifecycle information within an authoritative geolocated address and property dataset. We validate the results of our novel calibration method against official estimates by the UK government covering the same period. We further show that our calibration method, based on individual level data, captures a much larger share of the residential housing stock since 1997 than would have been possible with other available data sources. We argue that these housing stock estimates are fundamental to the development of a comprehensive digital footprints data infrastructure that has the potential to significantly enhance population, migration, and other social statistics in the UK. More generally, this study highlights the benefits of using digital footprints data in refining, calibrating, and extending existing datasets.

An existing technique for quickly obtaining the spatial distribution data of soil organic matter (SOM) involves combining an unmanned aerial vehicle (UAV) platform and imaging spectroscopy technology; this technique is suitable for precision agricultural management and real-time monitoring. In this study, the average spectral data were first extracted via circular regions of interest (ROIs) of different radii and modelled using the measured SOM to determine the optimal extraction range for the spectral data. Partial least-squares regression (PLSR) and support vector machine (SVM) models were constructed based on the raw spectra, standard normal transform spectra, multiple scattering-corrected spectra, and first-order differential spectra. The difference index, ratio index, and normalized index were calculated and analysed for their correlation with SOM content, and the spectral indices were screened to establish the SOM prediction model. Finally, the accuracy of the model was evaluated, and we selected the optimal model to map the spatial distribution of SOM. The results of our study show that 1) the radius of the ROI significantly affects the model’s accuracy; 2) the accuracy of the nonlinear SVM model is much higher than that of the linear PLSR model; 3) the prediction model based on spectral indices is better than full-band model. This study can serve as a model for the application of UAV imaging spectroscopy technology to field-scale SOM estimations and rapid monitoring, as well as provide technical support for precision agriculture and climate change studies.

This paper reviews and assesses the prospects for developing geographically enabled research ready data (RRD) with reference to current UK initiatives. Examples of projects for which such data have been provisioned are given.

Rock discontinuity information plays a key role in engineering activities. In severely fragmented and discontinuous samples, the identification accuracy of rock discontinuities can be improved with the help of geologic knowledge with identified spatial features and geoscience interaction mechanisms. Therefore, we proposed a new method – a knowledge-based intelligent recognition method for rock discontinuities that used point cloud data – to correct inaccurate identification by considering the spatial relationships of rock discontinuities. This paper integrated the geologic knowledge bases in rock discontinuity information extraction rules and combined the generative rule inference model with the region growing method for intelligent extraction. In the case study, we developed four typical rule sets as examples based on the constructed rule system to group rock discontinuities and subsume the fractured rock discontinuities. Results show that this new method is effective in identifying and classifying rock discontinuities of complex areas with a greater reduction in manual intervention.

Building facade structures serve as important data support for three-dimensional (3D) building modelling. A building facade structure extraction method based on 3D Laser Point Cloud Data (LPCD) by considering semantic information is proposed. The proposed method mainly involves three steps. First, an improved 3D LPCD semantic segmentation method is introduced to extract and label point clouds as the building structure and walls. Second, structure line segments are recognized from the initial building facade structure based on the Regulated Block RANSAC (RB-RANSAC) algorithm. Third, geometric information such as distance, vector and positional relationship between structure line segments is considered to optimize the initial building facade structure. The proposed method can effectively extract and fit segment structures by considering the semantic and geometric information, obtaining significantly accurate and concise building facade structures. Case studies on 3D LPCD of local buildings and open-source datasets (Semantic3D) prove the extraction accuracy and efficiency of the proposed method.

The distribution of surface particulate matter (PM2.5) exhibits significant spatiotemporal patterns, especially with a spatial clustering effect. Therefore, resolving spatial characteristics is essential in the modeling process. This study proposes a space–time varying coefficients (STVC-LG) model by considering spatial effects from the perspective of eigenvector spatial filtering and incorporating spatial effects and temporal features into the LightGBM model to estimate ground PM2.5. A site-based cross-validation result of the estimated daily 1 km PM2.5 concentration in China from 2016 to 2020 shows that the proposed model offers high accuracy, an R2 of 0.88, and an RMSE of 12.21 μg/m3 (17% and 32% enhanced compared to the original LightGBM model). It uses a series of spatial eigenvectors to describe different spatial patterns. It then constructs spatial interaction terms by combining the eigenvectors and influencing variables, allowing the values of the variable to vary spatially with the eigenvectors. The obtained PM2.5, with a decreasing trend, had a clear distributional consistency with NO2 and SO2 according to the co-occurrence distribution maps, particularly in winter. In areas where high concentrations co-occur, both NO2 and SO2 concentrations are Granger-causing PM2.5 concentrations from a statistical perspective.

Both the physical features and social functions of urban green spaces (UGSs) are crucially important to the ecological and social benefits of urban residents. Increasing attention has been focused on exploring how UGS social functions affect the ecological and social benefits of urban residents, but the social functional classification of UGSs has rarely been studied, and related efficient classification methods are urgently needed. Thus, a novel methodological framework for accurately mapping UGS social functional zones was proposed by integrating remote sensing images, crowd-sourced geospatial data (i.e. point of interest data, the OpenStreetMap road network, and Baidu Map boundary), and a deep learning algorithm. A sequence of combination experiments and ablation experiments were designed for performance validation and for quantifying the contributions of individual crowd-sourced geospatial data to UGS social functional classification. The results showed that the proposed methodological framework can precisely and effectively map UGS social functional zones and that all kinds of crowd-sourced geospatial data contributed to improving the accuracy of UGS social functional classification. This study can assist planners and government departments in the rapid monitoring, effective management, and scientific planning of UGS social functional zones by providing accurate data sources and an effective mapping tool.

Forests are of vital importance in maintaining the balance of ecosystems, and accurate forest cover maps provide basic data for research. Existing forest cover products suffer from substantial discrepancies in forest definition, area estimation, statistical accuracy, and spatial consistency, complicating their use. This paper uses the Dempster-Shafer theory to create a precise forest cover map of China using forest datasets from 2020. The basic probability assignment (BPA) function calculation adopts a combination of global constraints and local adaptive approach. The experimental results reveal that the accuracy of the fused forest cover map (FFCM) is significantly higher than that of the reference maps, with 91.62% user accuracy, 91.61% producer accuracy, 91.61% F1 score, and 94.65% overall accuracy. The area of China’s forests in 2020 is estimated at 229.43 million ha. The higher-precision forest cover map provides reliable data for research and a reference for the use of existing products.

This article describes the forecasting system urbanAQF, which incorporates several developments to deal with the complexities of air pollution in cities, including the adaptation of high-resolution numerical weather prediction data to the urban canopy, the coupling with regional forecast data, and an interactive web service for public dissemination of urban air quality information. The system applies a unique bias correction algorithm that adjusts boundary conditions and traffic emissions to observations of the previous days. An evaluation of the air quality forecasts during 2021 for Hamburg, Germany, against a comprehensive dataset of the administrative monitoring network and meteorological data, demonstrated the system’s capability to describe space and time variations of NO2 and PM10. At traffic sites, the high number of missed alerts in relation to exceedance of the daily mean limit for NO2 indicates the need to improve the simulation of traffic emissions. The forecast of PM2.5 alerts was affected by the time lag of the automatic correction, leading to a low number of correct alerts. The overall performance for O3 was very good, despite frequent false alarms connected to the prediction of unstable atmospheric conditions. The urbanAQF system empowers policymakers to implement effective measures for improving air quality in cities.

ABSTRACTVirtual geographical scenes aim to naturally represent the real world and aid in comprehending geographic information. However, current virtual geographical scene modeling methods have problems detecting change information and low efficiency in dynamic modeling. Additionally, the modeling process lacks guidance from domain experts, resulting in poor modeling standardization. Therefore, this paper proposes a combined data-driven and knowledge-driven virtual geographical scene twin modeling method. We discuss the three-domain association geographical scene knowledge graph, change information detection network based on knowledge graph and deep learning, and knowledge-guided semantic modeling algorithms in detail. These methods enhance the ability of virtual geographical scenes to describe the changing real world. Furthermore, we select an urban bridge construction geographical scene with obvious change characteristics as a typical case, develop a prototype system, and conduct an experimental analysis. The results show that our method can fully and effectively utilize the geographical scene data and knowledge and improve the reusability of the knowledge. The average change detection accuracy in geographical scene information reaches 92.46%, and the dynamic modeling efficiency of virtual geographical twin scenes reaches 29.97 fps. Compared to other 3D modeling methods, the proposed method can represent the dynamic real world more timely, accurately, completely, and comprehensively.

ABSTRACTThe Medium Resolution Spectral Image (MERSI) is a MODIS-like sensor aboard Fengyun-3 satellite. The first version of MERSI aerosol algorithm has been developed based on MODIS dark target (DT) algorithm, with modified models for estimating surface reflectance and an adjusted inland water masking method to release haze aerosols. This study applies MERSI DT algorithm to the global observations from the upgraded MERSI sensor (MERSI-II) on Fengyun-3D. And then, the Aerosol Optical Depth (AOD) results from the year of 2019–2020 are validated against the Aerosol Robotic Network (AERONET) data. In addition, analyses of the spatial distribution and error characteristics of MODIS and MERSI-II retrievals are presented. The overall validation demonstrates that MERSI-II retrievals perform well globally, with a correlation coefficient of 0.877 and 67.1% of matchups within the Expected Error envelope of ± (0.05 + 0.2τ), which are close to the statistic metrics of MODIS products. In addition, MERSI-II and MODIS AODs exhibit similar error trends and error dependence. Moreover, the similar global distribution characteristics of the two AODs are revealed in the retrieval performance at site and regional scales, as well as in the analysis of monthly averages. These findings indicate the success of the ported MERSI algorithm.

ABSTRACTAmidst rapid global urbanization, comprehending urban area growth (UAG) dynamics is vital for urban planning and sustainability. Despite ample urban development research, the historical developmental patterns of UAG remain underexplored. Long-term UAG exhibits an initial acceleration followed by deceleration, thus, using 200-years UAG data from the U.S., we meticulously compare the S-shaped curve (Logistics and Gompertz models) with other time-series models, and unveil insights into UAG's intricacies by examining how S-shaped models perform across different urban stages. Notably, the Logistics model emerges as the more accurate modeling tool, boasting an RMSE of 0.019, which surpasses the Gompertz model's 0.032. Moreover, the parameters of the S-curve explicitly describe the fundamental properties of UAG, and we unveil the remarkable stability of the maximum growth rate in the S-shaped model through a thorough parameter analysis, which underscores its role as a reliable and universally applicable assessment tool for UAG. Fundamentally, this meticulously analytical research delves deep into the complexities of the UAG curve, robustly confirming the S-shaped trend as the ‘realistic’ consequence of UAG over time. Importantly, this enduring S-shaped trend remains consistent across historical and contemporary contexts. These findings significantly advance our understanding of UAG dynamics for informed urban planning and development.

ABSTRACTSpaceborne photon-counting light detection and ranging (LiDAR) have been extensively applied in shallow-water bathymetry. The density of underwater topographic photons (UTP) varies and is discontinuous due to sunlight noise, beam intensity, and seabed reflectivity, which differ from the land photon distribution due to the attenuation of water. Therefore, a general method for extracting and grading UTP is still lacking. We propose an active contour method combined with a variable convolution kernel method to calculate the photon range by considering the energy contributions of adjacent photons. Adaptive parameters under different observation conditions were determined to obtain the optimal convolution kernel using a kernel ridge regression model. This implies that the number of photons contained in the buffer zone was largest after the extracted UTP was fitted to a curve. Quantitative and qualitative verifications proved that the method performed well under different conditions. The photons obtained by the energy functional and the curve obtained by the fitting method were then used to grade the photons. Finally, an online developed UTP dataset and extraction framework were proposed to provide an applicable method for current and subsequent spaceborne photon-counting LiDAR.

ABSTRACTAccurately predicting the geometric structure of a building's roof as a vectorized representation from a raster image is a challenging task in building reconstruction. In this paper, we propose an efficient and precise parsing method called Roof-Former, based on a vision Transformer. Our method involves three steps: (1) Image encoder and edge node initialization, (2) Image feature fusion with an enhanced segmentation refinement branch, and (3) Edge filtering and structural reasoning. Our method outperforms previous works on the vectorizing world building dataset and the Enschede dataset, with vertex and edge heat map F1-scores increasing from [Formula: see text], [Formula: see text] to [Formula: see text], [Formula: see text], and from [Formula: see text], [Formula: see text] to [Formula: see text], [Formula: see text], respectively. Furthermore, our method demonstrates superior performance compared to the current state-of-the-art based on qualitative evaluations, indicating its effectiveness in extracting global image information while maintaining the consistency and topological validity of the roof structure.

Coordinate transformation is a fundamental issue in the related studies of measurement. However, existing methodologies often need to pay more attention to the available spatial information, leading to suboptimal results. This paper addresses this issue by incorporating geometric constraints into the symmetric coordinate transformation. We propose the so-called geo-constrained transformation method based on the joint adjustment of the coordinate transformation in conjunction with the geometric constraints over a set of points. By formulating the geometric constraints as the conditional model, we analyze the effects of geometric constraints on the estimated transformation parameters and point locations. By removing such effects during the symmetric transformation algorithm, the results show better statistical performance and satisfy geometric constraints. Two numerical examples are given to demonstrate the expected improvement in the statistical accuracy. It is shown that the improvement of the point determination accuracy can go beyond 50%.

Recent advancements in satellite remote sensing technology and computer vision have enabled rapid extraction of road networks from massive, Very High-Resolution (VHR) satellite imagery. However, current road extraction methods face the following limitations: 1) Insufficient availability of accurate and diverse training datasets for global-scale road extraction; 2) Costly and time-consuming manual labeling of millions of road samples; and 3) Limited generalization ability of deep learning models across diverse global contexts, resulting in better performance for regions well-represented in the training dataset, but worse performance when faced with domain gaps. To address these challenges, a semi-supervised framework was developed in this study, which includes a global-scale benchmark dataset – termed GlobalRoadSet (GRSet) – and a pseudo-label guided semi-supervised road extraction network – termed GlobalRoadNetSF (GRNetSF). The GRSet dataset was constructed using high-resolution satellite imagery and open-source crowdsourced OpenStreetMap (OSM) data. It comprises 47,210 samples collected from 121 capital cities across six populated continents. The GRNetSF trains the network by generating pseudo-labels for unlabeled images, combined with a few labeled samples from the target region. To enhance the quality of the pseudo-labels, strong data augmentation perturbation and auxiliary feature perturbation techniques are employed to ensure model prediction consistency. The proposed GRNetSF_GRSet framework was implemented in over 30 cities worldwide, where most of the Intersection-over-Union (IoU) values increased by more than 10%. This outcome confirms its strong generalization ability.

Current nautical chart generalization methods are notably labor intensive, requiring significant levels of human intervention to compile, update, and maintain chart products. The ideal situation would be a fully automated solution for generating nautical charts seamlessly from a comprehensive database, on demand, at the appropriate scale, at the point of use, and respecting the product constraints. However, regardless of the various research efforts and advancements in technology, including those involving AI, nautical chart generalization tasks are still performed manually, or semi-manually, where a likelihood of human error is expected. This manuscript presents a research effort toward automated chart compilation through scales. Nautical chart generalization guidelines are extracted, categorized, and translated into machine readable rules, utilized by a multi-agent model to perform the generalization of the source data to the target scale with no topological violations. This is illustrated in three testbeds for the most important ENC feature classes. While topology is maintained, the model utilizes readily available algorithms that, generally, compromise safety. Therefore, a custom validation tool detects safety violations for user intervention. The model has been made flexible to incorporate algorithms that align with application constraints, especially safety, as they become available.

The assessment of ecological functions, such as those of forest structure zoning and carbon sinks, heavily relies on forest age classification. Therefore, monitoring forest age is a crucial element of forest resource surveys. With the increased availability of high-quality open-access satellite data and advancements in Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) technology, remote sensing has emerged as an essential method for acquiring accurate forest age information. In this study, Sentinel-2 remote sensing data, UAV-LiDAR data, and combined Sentinel-2 and LiDAR data are used as data sources. Three machine learning algorithms, Adaptive Boosting (AdaBoost), Random Forest (RF), and Extreme Random Tree (ERT), are used to predict forest age in a Masson pine (Pinus massoniana Lamb.) forest. The optimal model is used to predict the forest age and simulate the spatial age distribution. The machine learning models based on separate Sentinel-2 and LiDAR data accurately predict the age of the Masson pine forest. Nevertheless, the accuracy of the RF model with combined data was higher than that in other cases, with an accuracy R value of 0.81. The model displayed good stability, and the spatial uncertainty of age estimation was low. Compared with the RF model using only Sentinel-2 data (R = 0.43), the RF model with combined LiDAR and Sentinel-2 data achieved the highest accuracy, with R values 88.37% higher. In addition, the forest canopy structure parameters, such as the average height of the forest stand extracted from UAV-LiDAR data, had a significant impact on the estimation of forest age. Thus, when the combined Sentinel-2 and LiDAR data were used to establish these parameters, the highest accuracy in the estimation of Masson pine was obtained. The findings of this study provide new insights for forest age estimation based on multi-source remote sensing data.

Multi-source social sensing data provide new opportunities to identify urban functions from the perspective of human activity. The information embedded in multi-source data typically needs to be fused to obtain a comprehensive view of urban functions. Although multi-view clustering has been successfully used to fuse multi-source social sensing data, the adaptive determination of fusion weights for high-dimensional and noisy multi-source social sensing data remains challenging. Therefore, this study proposes an adaptive weighted multi-view subspace clustering (AWMSC) method. First, we use two neural networks to map multi-source data into a common latent representation and multiple specific latent representations, which serve as the query vector and input vectors of the attention mechanism, respectively. Then, the weight of each type of data is calculated based on the attention mechanism. Finally, the specific latent representations of the multi-source data are weighted and fused into a shared subspace representation, which is used as the input of the spectral clustering algorithm to obtain clustering results. AWMSC is applied to identify urban functional zones in Beijing using bus transactions, taxi trajectories, and points of interest datasets. The results show that AWMSC outperforms the typical single-view, weighted-average, and representative multi-view methods. AWMSC can obtain a comprehensive understanding of urban functional zones which may help government departments make more accurate strategic decisions.

The Gross Primary Productivity (GPP) is an important component in regional and global carbon budgets. Southeastern China has experienced vegetation greening and climate change. Yet, it remains unclear how these changes have impacted GPP in this region. As one of six special national parks in China with complex topography, high biodiversity, and little destructive human activities to the ecosystems, the Wuyi Mountain region is selected to study these impacts. In this study, we use a hydroecological model (BEPS-TerrainLab V2.0) to simulate the spatial and temporal variations of GPP in the Wuyi Mountain region over 2001–2018. We quantitatively separate the effects of vegetation greening and climate change on the trend and interannual variation in GPP through sensitivity experiments. The results show a significant increasing trend in Leaf Area Index (LAI) in the region over 2001–2018 (0.06 m2 m−2 yr−1, p

OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in economic modeling at different spatial scales in China was explored by using a geographic information system (GIS) analysis method and a geographically and temporally weighted regression (GTWR) model. Four machine learning models, SVM, RF, XGBoost and CatBoost, were used to simulate the grid-scale GDP, and the effectiveness of these simulations was discussed. The results showed that (1) the weights of OSM data quality indicator factors vary across different spatial scales. (2) From 2014 to 2020, the quality of national-scale OSM data first increased, then decreased and then gradually stabilized. In addition, the quality of OSM data at the provincial and municipal scales is significantly different, and the distribution is affected by the population and geographical environment. (3) Over time, the spatial clustering characteristics of OSM data quality at different spatial scales in China has continuously strengthened. In addition, the quality of Chinese OSM data displays obvious local spatial autocorrelation characteristics, which are dominated by H-H clustering and L-L clustering. (4) The GTWR model performs well in predicting and revealing the spatiotemporal correlation characteristics between GDP and OSM data quality indicators at the provincial and municipal scales in China. The correlations increase with decreasing spatial scale (provincial to municipal). Moreover, the GDP modeling ability is better in economically underdeveloped Northwest China and economically developed East China. (5) Four machine learning models coupled with road network length completeness, relative linear density, road name attribute completeness, POI name attribute completeness, road network accuracy, road network update frequency, POI update frequency, topological consistency and directional similarity yielded the best grid-scale GDP simulation values. Notably, the CatBoost model provided the best accuracy, and the results further verified that it is feasible to use the proposed OSM data quality index system to predict the regional economic development level in China.

Ground filtering is a fundamental step in airborne LiDAR data processing toward a variety of applications. However, existing algorithms remain tremendously challenging in complex environments, e.g. steep hillsides, ridges, valleys, discontinuities, and numerous objects. We presented a new ground filtering algorithm that can handle various landscapes. First, the multi-view window is developed to increase the number of ground seeds on the various terrains. Second, multi-resolution hierarchical cloth simulation is used to rapidly construct the high-resolution reference terrain, and bidirectional internal force operation is proposed to improve the accuracy of reference terrain by smoothing the spikes in cloth. Finally, ground and non-ground points are classified based on the height differences between points and the reference terrain. The proposed algorithm was validated not only in the International Society for Photogrammetry and Remote Sensing (ISPRS) but also karst datasets, where particularly complex environments is contained. Results showed that the proposed algorithm outperformed the existing algorithms, with the lowest average total error of 3.85% and the highest average kappa coefficient of 87.75%. Moreover, the proposed algorithm can completely preserve complex terrain, e.g. extremely steep hillsides, and sharp ridges. This study had great potential to provide a useful tool for LiDAR data processing.

The Tibetan Plateau (TP) has been the focus of numerous studies examining the energy and water cycle variations, but there is still a lack of long-term, quantitative precise assessments of evapotranspiration. This research first provided two sets of long-term comprehensive observational datasets, and an advanced monitoring technique to measure soil moisture, which can improve the estimation accuracy of evapotranspiration. Subsequently, using microwave data, the Surface Energy Balance System model and Machine Learning methods, it calculated a complete set of long-term evapotranspiration data. At the same time, based on reasonable assumptions, it also estimated the total evaporation from plateau lakes. These findings contribute significantly to the understanding of the relationship between the Asian monsoon, the TP’s physical characteristics, and its atmosphere, thereby improving predictions of water resource variability in the TP. The study’s innovative methodologies and synthesis of diverse data sources provide critical information for informed and sustainable water management strategies in the region.

Due to the small size, variety, and high degree of mixing of herbaceous vegetation, remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories, lacking detailed depiction. This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources. To address this issue, this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area. It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification, thereby enhancing the accuracy and refinement of grassland classification. The results demonstrate the following: (1) To meet the supervision requirements of grassland resources, we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method, specifically applicable to natural grasslands in northern China. (2) By utilizing the high-spatial-resolution Normalized Difference Vegetation Index (NDVI) synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model (STNLFFM), we are able to capture the NDVI time profiles of grassland types, accurately extract vegetation phenological information within the year, and further enhance the temporal resolution. (3) The integration of multi-seasonal spectral, polarization, and phenological characteristics significantly improves the classification accuracy of grassland types. The overall accuracy reaches 82.61%, with a kappa coefficient of 0.79. Compared to using only multi-seasonal spectral features, the accuracy and kappa coefficient have improved by 15.94% and 0.19, respectively. Notably, the accuracy improvement of the gently sloping steppe is the highest, exceeding 38%. (4) Sandy grassland is the most widespread in the study area, and the growth season of grassland vegetation mainly occurs from May to September. The sandy meadow exhibits a longer growing season compared with typical grassland and meadow, and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.

The two vegetation transfer parameters of [Formula: see text] (Vegetation Optical Depth,VOD) and [Formula: see text] (Omega) could vary significantly across microwave channels in terms of frequencies, polarizations, and incidence angles, and their channel-dependent characteristics have not yet been fully investigated. In this study, we investigate the channel dependence of vegetation effects on microwave emissions from soils using a higher-order vegetation radiative transfer model of Tor Vergata. Corn was selected as the subject of investigation, and a corn growth model was developed utilizing field data collected from the multifrequency and multi-angular ground-based microwave radiation experiment from the Soil Moisture Experiment in the Luan River (SMELR). Upon compilation of the simulation dataset of microwave emissions of the corn field, the effective scattering albedo across different channels were calculated using the Tor Vergata model. Results show that vertical polarization of the vegetation optical depth is more affected by incidence angle changes, while horizontal polarization exhibits lower variations in vegetation optical depth due to incidence angle adjustments. The channel dependence of vegetation optical depth can be described as the polarization dependence parameter ([Formula: see text]) and the frequency dependence parameter ([Formula: see text]). These two parameters enable the calculation of vegetation optical depth at any channel under three adjacent frequencies (L-band, C-band and X-band). The effective scattering albedo of vegetation does not vary significantly with vegetation height or angle. It primarily depends on frequency and polarization, showing an overall increasing trend with increasing frequency. The effective scattering albedo with vertical polarization is slightly higher than that with horizontal polarization at higher frequencies, while both are lower in the L-band. This investigation is helpful for understanding the vegetation effects on microwave emissions from soils, ultimately advancing the accuracy of large-scale soil moisture retrieval in vegetated areas.

This paper contrasts predicted X-band sea surface backscattering from slick-free and oil-covered sea surfaces with actual measurements acquired by the X-band satellite TerraSAR-X and COSMO-SkyMed Synthetic Aperture Radar (SAR) missions. Two SAR scenes were acquired with a temporal difference of about 36 minutes, under similar met-ocean conditions, during the North Sea’s Gannet Alpha oil spill accident. The normalized radar cross section of the slick-free sea surface is predicted using the Advanced Integral Equation Model (AIEM) while the backscatter from the oiled sea surface is predicted by the AIEM augmented with the Model of Local Balance (MLB) to include the damping effect of oil slicks. Experimental results show that X-band co-polarized numerical predictions agree reasonably well with both TSX and CSK actual measurements collected over slick-free sea surfaces. When dealing with oil-covered sea surfaces, the predicted backscattering reasonably agrees with TSX measurements, while it overestimates the CSK ones. This is likely due to the different spreading conditions of the oil imaged by the two satellite missions.