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Objectives: Global navigation satellite system (GNSS) interferometric reflectometry (GNSS-IR) is a new passive remote sensing technique for determining surface environment parameters, which places an important part in the inversion of earth's surface properties, such as soil moisture monitoring, snow parameter retrieval, and vegetation remote sensing, etc. GNSS-IR offers several benefits over the traditional soil moisture inversion approach, including all-weather capability, high temporal precision, and cheap cost. Methods: Considering the fact that the existing soil moisture inversion algorithms only utilize one single feature of GNSS reflected signal and from the perspective of increasing data availability, this paper proposes a GNSS-IR soil moisture inversion approach that integrates multi-type feature data by utilizing phase, amplitude, and frequency extracted GNSS signals reflected by soil. The main work is to effectively filter all available features extracted from the original GNSS signal-to-noise ratio observations. The feasibility and effect of the suggested method are compared and evaluated using three machine learning models, including least squares support vector machine (LSSVM), random forest (RF), and back propagation neural network (BPNN). Results: Comparing the inversion effects of above three models, BPNN model has the best inversion effect, followed by RF model, and LSSVM model is the worst. The results show that the correlation coefficients between the reference value and soil moisture inversed by the multi-feature fusion method LSSVM, RF, and BPNN models are 0.830, 0.953, and 0.980, respectively, and the corresponding root mean square errors are 0.045, 0.035 and 0.032 cm³ /cm³ Conclusions: Compared with the single feature inversion method, both the accuracy and correlation coefficient of soil moisture inversion increase significantly. The results demonstrated that the proposed method has higher inversion accuracy and reliability than the single feature inversion method. [ABSTRACT FROM AUTHOR]

As the most dynamic component of terrestrial water cycling, soil moisture plays a pivotal role in the formation, transformation, and consumption of surface water. Simultaneously, it serves as a crucial parameter influencing hydrological processes, vegetation status, and climatic conditions. Therefore, highprecision, high spatiotemporal resolution soil moisture data holds significant importance across various fields, including agriculture, forestry, and meteorology. Presently, traditional methods such as the timeconsuming and labor-intensive drying-weighing technique are inadequate for large-scale monitoring demands, highlighting the advantages of remote sensing methods. However, optical sensors are susceptible to cloud cover and vegetation obscuration, while microwave remote sensing technology faces challenges in balancing spatial and temporal resolutions. The space-borne global navigation satellite system-reflectometry (GNSS R) technology, characterized by short revisit cycles and pseudo-random sampling, presents a new opportunity for soil moisture retrieval. This paper begins by introducing the fundamental theory of soil moisture retrieval using GNSS-R and analyzes error sources during the retrieval process. This includes the scattering and attenuation effects of surface factors such as vegetation and surface roughness on reflected signals, as well as the impact of water bodies on reflectivity. Existing error correction methods and their limitations are summarized, and potential avenues for improvement are discussed. Subsequently, the research progress in recent years regarding space-borne GNSS-R soil moisture retrieval is analyzed from three perspectives: (1) The development status of space-borne GNSS-R constellation. (2)An overview of algorithms for space-borne GNSS-R soil moisture retrieval, including empirical models, semi-empirical models, and machine learning methods. (3) Advancements and directions in integrating GNSS-R with other data for soil moisture retrieval. In conclusion, based on the scattering mechanism of reflected signals, the selection of auxiliary data, and the utilization of incident angle information, this paper discusses the technical challenges faced in current soil moisture retrieval using space-borne GNSS-R technology and provides insights into future research directions. [ABSTRACT FROM AUTHOR]

Objectives: With the rise of location service applications and the popularization of mobile smart terminals, the demand for high ‑ precision navigation and positioning is extending from isolated areas to seamless whole areas, from professional groups to mass users. However, real‑time, continuous, and complete pedestrian navigation still faces many challenges, such as occlusion of global navigation satellite system (GNSS) signals, frequent updates of Wi‑Fi/ bluetooth low energy (BLE)/geomagnetic fingerprint data‑ bases, and smoothly switching in the transitional area of indoor and outdoor. In addition, the positioning sensors of popular mobile smart terminals are limited by cost and power consumption, and the observation data is generally noisy and poor in stability. Methods: Therefore, this paper proposes a pedestrian indoor and outdoor seamless positioning technology based on cloud‑end collaboration of smartphone, which breaks through the crowd sourced collection and rapid update of the beacon fingerprint database, the precise positioning of the smart phone multi‑sensor information fusion, and the indoor and outdoor seamless switching. We have developed a smartphone positioning application for pedestrians. The collaborative precision positioning enhancement information is provided by the location service platform and the multi‑source observations data (GNSS, miniature inertial measurement unit (MIMU), Wi ‑ Fi, BLE, magnetometer, barometer) are obtained by the smartphone. Results: The feild test results show that the outdoor positioning accuracy of ordinary users is better than 1.5 m, and indoor accuracy is about 1 ‒ 3 m. Conclusions: It supports the demand of mass‑market users for seamless and precise positioning indoors and outdoors. [ABSTRACT FROM AUTHOR]

Urban simulation research originated between the 1980s and 1990s. Today urban simulation has become a new paradigm of urban research, which is an important outcome of computational thinking in urban research. Urban simulation methods are usually based on cellular automata (CA) and machine learning. A series of urban CA models have been developed to simulate complex urban evolution processes and associated multi-scenario analysis. This paper reviews the origin and progress of urban simulation research. With the discussion of urban CA’s general structure, we explains the necessity and feasibility of machine learning methods to support urban simulation. Furthermore, we reviews the integration of machine learning and CA in urban research, and also discusses its new trends and emerging challenges. [ABSTRACT FROM AUTHOR]

Windows are important elements of building facade. Therefore，window extraction is of significant value to building structural analysis and facade reconstruction. With respect to the inner structural feature and the distribution regularity among windows， this paper proposed a window extract method based on automatic sample selection and distribution regularity constraint. Firstly，sample selection was performed by a template matching method to select a number of window samples， both the positive and the negative， from one selected window sample. Secondly， JointBoost classifier， trained by the window samples，was employed to achieve preliminary window extraction. Then，windows distribution regularity model，which includes horizontal direction，vertical direction, point of interest and similarity，was defined and reconstructed using the preliminary window extraction. Finally， the final window extraction result was achieved by optimizing preliminary window extraction result on the constraint of distribution regularity model. The experiments proved that the proposed method has high extraction ratio and accurate ratio on images with complicated background， complex window structure and perspective distortion. [ABSTRACT FROM AUTHOR]