ABSTRACT Precise landslide monitoring is crucial for understanding and mitigating their impact. The Three Gorges Dam has exacerbated landslides due to steep slopes and rising water levels. This study used ALOS-2 PALSAR-2 (2016–2017) and Sentinel-1A (2016–2018) data to monitor the Daping slope. Findings showed deformation rates of −86.3 mm/y (ALOS-2) and −62 mm/y (Sentinel-1A), confirmed by GNSS data. Analysis indicated that rainfall and water level fluctuations drive the Daping landslide, highlighting key factors influencing landslides in the Three Gorges region and their threats to life and property.

ABSTRACT Three-dimensional (3D) geographic information systems (GIS) are vital for urban planning and architectural design, enabling detailed visualization and analysis of urban landscapes. However, the growing size of 3D city data and the transition to web environments cause performance challenges for real-time applications. This study addresses the need for optimizing 3D spatial analyses for the web using Quadtree data structures and multiprocessing tools. Optimization scenarios based on data size and hardware configurations are evaluated, and the best hosting plans for GIS applications on cloud services are also considered.

ABSTRACT The smartphone-based precise point positioning (PPP) technique is usually used in limited satellite visibility environments, resulting in discontinuities in carrier phase observations. This study proposes an approach to improve the smartphone-based PPP performance by predicting the missed carrier phase observations with Doppler observations. Kinematic smartphone-based PPP experiments in the tree canopy and high-building environments are conducted, and results show that the proposed PPP approach improves the horizontal positioning accuracy by over 31% and 14% and vertical positioning accuracy by over 23% and 10% when compared to the PPP approaches without predictions and with linear predictions, respectively.

ABSTRACT Outliers can significantly impact the accuracy and reliability of Global Navigation Satellite System (GNSS) real-time kinematic (RTK) positioning. To enhance the robustness of RTK in GNSS-challenged environments, we propose an enhanced random sample consensus RTK (RANSAC-RTK) algorithm capable of effectively handling multiple and continuous outliers. The enhancements to the RANSAC algorithm include threshold setting, pre-screening samples and sample verification tailored to the characteristics of GNSS data. The experimental results indicate that the standard RTK algorithm is vulnerable to outliers. By contrast, the enhanced RANSAC-RTK algorithm can effectively handle multiple and continuous outliers, resulting in 33% increase in the ambiguity fixing rate and 15% improvement in positioning accuracy.

ABSTRACT High-precision global navigation satellite system (GNSS) time transfer depends on the carrier phase observations, and ionospheric delay affects rapid integer ambiguity resolution. Therefore, this work applies the ionospheric-weighted single-differenced model. Experimental results indicate that for a time-link of 67.9 km, the time transfer accuracy of different single-system is better than 0.03 ns. Compared with single-system, multi-system demonstrates 8% to 14% improvement in accuracy and improvement of no more than 31% in frequency stability. Additionally, compared with the case without ionospheric constraint, imposing ionospheric constraints can improve performance of time transfer, but the gain decreases as the time-link length increases.

ABSTRACT Urban reforestation mitigates climate change by sequestering carbon, but quantifying carbon gains requires accurate aboveground biomass estimation. This study estimated carbon sequestration in a reforested urban landscape using PlanetScope, Sentinel-1A, Sentinel-2A, SRTM data, and field measurements. Non-parametric machine learning algorithms (k-nearest neighbor, support vector machines, extreme gradient boosting, random forests) with 39 predictor features generated aboveground biomass density maps. The extreme gradient boosting model performed best, predicting 4.1–286.5t ha-1 aboveground biomass, demonstrating its effectiveness for modeling reforested biomass with multi-source data. Findings highlight extreme gradient boosting’s promise for urban biomass estimation, the importance of multi-source data, and machine learning’s potential in addressing environmental challenges like climate change.

ABSTRACT This study explores the fusion of Sentinel-2A (S2) and different bands of UAV imagery (Blue, Green, Red and Panchromatic (PAN)) to enhance spatial and spectral information for crop health monitoring. It assesses the quality of fused images, accuracy of the classified fused images and statistical analysis between the fused and S2 vegetation indices. Various fusion techniques are examined to select an optimal combination of bands. The quality of the fused images is evaluated using image quality assessment metrics and classification employs the Random Forest algorithm, assessing accuracy with metrics like F-score and Kappa Coefficient. Statistical analysis involves comparing vegetation indices from fused and S2 imagery. Notably, the fusion of UAV Green and Red bands with S2 imagery, using BT and PCA techniques, emerges as an effective combination for plant-level agricultural health monitoring. This research contributes to advancing precision agriculture techniques by leveraging multispectral imaging fusion for enhanced crop monitoring and management.

ABSTRACT This study presents a novel filtering methodology for Mobile Laser Scanning (MLS) data using robust iterative reweighting. Initially, 3D point clouds are projected onto a 2D grid to create surfaces from the lowest points. Weights are assigned based on the Height Above Ground (HAG) of these points. Ground points are distinguished by applying a surface function to the dataset via iterative reweighting. Among the tested four robust weight functions, the Denmark and Beaton-Tukey functions outperformed others, achieving total error values of 2.30 and 2.32 across three test areas, respectively. This method efficiently filters MLS data, irrespective of ground point proportions.

ABSTRACT This paper concentrates on establishing a connectivity graph between building entities to model hazard flow and the interaction between pedestrians and hazards. Although three different ways are explored, the Uniform raycast-based approach resulted in being the most robust. We establish a link between hazards, indoor spaces and pedestrians through voxels, where navigable voxels are first derived, and further classified as IfcSpace and IfcDoor. In the end, only voxels with unique properties are kept, being < 1% of the total number of a building. Once the connectivity graph and voxels are identified the interaction between pedestrians and hazards is presented as straightforward.

ABSTRACT Neural networks have been extensively utilized in the classification of hyperspectral images. The study introduces a novel dual-branch structured network named MGACN for hyperspectral image classification under the condition of small sample sizes. This model a multi-hop graph network (MHGCN) and a graph attention convolutional network (GACNN). Graph Convolutional Network (GCN) integrates multi-hop mechanism to obtain and aggregate information between long-distance non-neighbor nodes. The Graph Attention Network (GAT) combines 2D-CNN by incorporating a noise reduction mechanism, as well as position and channel attention mechanisms. The classification accuracy reaches 98.43%, 99.02%, and 98.55% on the Indian Pines, Salinas, and Pavia University datasets, which is significantly better than that of the comparison model.