The dense high-rise buildings and multipath effects in urban areas significantly reduce the positioning signal accuracy of laser scanning systems, leading to layering and offset issues in the collected point cloud data on the same road. In order to acquire comprehensive and consistent three-dimensional information on the objects, thereby providing field inspection data for large-scale road traffic network scenarios, in this paper, an improved point cloud registration method is proposed to divide the registration process into two stages: elevation registration and plane registration. Elevation registration takes the ground point cloud as the registration primitive, reduces the number of point clouds through curvature down-sampling, and constrains the feature point sequence with a fixed range to provide a good initial pose for fine registration. The plane registration first inherits the elevation registration parameters, combining the dynamic distance parameters of spherical region step based on the median, using robust multi-scale loss functions to address residual points, effective adjacent point pairs are selected to obtain the spatial transformation matrix, and realizes accurate registration. Experimental results with multiple sets of urban point cloud data show that the root mean square error of point cloud registration can be controlled within 0.06 m, achieving a relatively superior registration accuracy, it can provide detailed prior data for measurement information analysis.

Flooding is among the world’s most destructive natural disasters. From 27 July to 1 August 2023, Zhuozhou City and surrounding areas in Hebei Province experienced extreme rainfall, severely impacting local food security. To swiftly map the spatial and temporal distribution of the floodwaters and assess the damage to major crops, this study proposes a water body identification method with a dual polarization band combination for synthetic-aperture radar (SAR) data to solve the differences in water body feature recognition in SAR due to different polarization modes. Based on the SAR water body extent, the flood inundation extent was mapped with GF-6 optical data. In addition, Landsat-8 data were used to generate information on significant crops in the study area, while Sentinel-2 data and the Google Earth Engine (GEE) platform were used to classify the extent of crop damage. The results indicate that the flood inundated 700.51 km², 14.10% of the study area. Approximately 40,700 hectares (ha) or 8.46% of the main crops were affected, including 33,700 ha of maize, 4300 ha of vegetables, and 2800 ha of beans. Moderate crop damage was the most widespread, affecting 37.62% of the crops, while very extreme damage was the least, affecting 5.10%. Zhuozhou City experienced the most significant impact, with 13,700 ha of crop damage, accounting for 33.70% of the total. This study provides a computational framework for rapid flood monitoring using multi-source remote sensing data, which also serves as a reference for post-disaster recovery, agricultural production, and crop risk assessment.

SDG 9.1.1 and SDG 11.2.1 are significant evaluation indicators of the United Nations Sustainable Development Goals related to transportation accessibility and are used to measure the proportion of the population facilitating the use of road services in rural areas and the proportion of the population facilitating the use of public transportation services in urban areas, respectively. However, there are currently challenges related to incomplete data and the inadequate interpretation of the indicators. In this study, we therefore evaluate the spatiotemporal patterns of the indicators and the number of disadvantaged groups in 337 Chinese cities from 2015 to 2022 based on multi-source data, and explore the spatial aggregation of the indicators and the driving factors. The results demonstrate that the indicator values of SDG 9.1.1 and SDG 11.2.1 reached 99.36% and 90.00%, respectively, in 2022, and the number of vulnerable groups decreased to approximately 1.89 million and 2.82 million. The indicator values of SDG 9.1.1 are high in the eastern part of China and low in the western part of the country, whereas the indicator values of SDG 11.2.1 exhibit spatial agglomeration in regions such as the Pearl River Delta. The average rural elevation and the density of urban public transportation stops are the most influential factors for these two indicators, respectively. The insights and data from this study provide support for improving transportation infrastructure and inequality in China, contributing to the achievement of the 2030 Sustainable Development Goals.

Online semantic Gaussian SLAM typically updates three-dimensional semantic maps using frame-wise predictions from two-dimensional semantic segmentation networks. However, occlusions, viewpoint changes, and object boundaries may introduce transient misclassifications, which can further cause cross-frame semantic inconsistency and error propagation. To address this issue, we propose HCSG-SLAM (History-Consistent Semantic Gaussian SLAM), a lightweight history-consistent semantic fusion method for Gaussian SLAM that introduces a compact Gaussian-level historical semantic state without modifying the main RGB/depth tracking and reconstruction pipeline of SGS-SLAM. Each Gaussian maintains only a candidate semantic label, a voting state, and an effective historical observation count. A streaming voting strategy forms the historical semantic consensus, while a strictly causal target-before-current-update strategy prevents the current observation from contributing to its own correction target. Experiments on Replica and ScanNet used segmentation-network predictions rather than ground-truth semantics as input. On Replica, the mean mIoU increased from 0.300830 to 0.307010, with positive gains in all 15 paired runs. On ScanNet, the evaluation was extended to five scenes, where the overall mean mIoU increased from 0.366279 to 0.372166, with positive gains in 22 out of 25 paired scene–seed runs. Per-class analysis shows that the gains were concentrated in several structural and furniture categories, while a small number of classes remained unchanged or slightly decreased. Positive gains were also observed under all nine controlled transient-corruption settings, and additional persistent class-confusion and structured boundary-shift tests yielded positive or comparable performance across the evaluated scenes. Compared with FD-EMA, HCSG-SLAM achieved comparable semantic accuracy while requiring only 12 B of additional temporal state per Gaussian compared with approximately 202 B for FD-EMA. HCSG-SLAM introduced approximately 2.05% additional mapping time relative to the Baseline while leaving tracking essentially unchanged. These results indicate that compact Gaussian-level temporal semantic states can improve semantic consistency and robustness with modest online fusion overhead.