In recent years, China has experienced growing impacts from extreme weather events, emphasizing the importance of understanding regional atmospheric moisture dynamics, particularly Precipitable Water Vapor (PWV), to support sustainable environmental and urban planning. This study utilizes ten years (2013–2022) of Global Navigation Satellite System (GNSS) observations in typical cities in eastern China and proposes a comprehensive multiscale frequency-domain analysis framework that integrates the Fourier transform, Bayesian spectral estimation, and wavelet decomposition to extract the dominant PWV periodicities. Time-series analysis reveals an overall increasing trend in PWV across most regions, with notably declining trends in Beijing, Wuhan, and southern Taiwan, primarily attributed to groundwater depletion, rapid urban expansion, and ENSO-related anomalies, respectively. Frequency-domain results indicate distinct latitudinal and coastal–inland differences in the PWV periodicities. Inland stations (Beijing, Changchun, and Wuhan) display annual signals alongside weaker semi-annual components, while coastal stations (Shanghai, Kinmen County, Hong Kong, and Taiwan) mainly exhibit annual cycles. High-latitude stations show stronger seasonal and monthly fluctuations, mid-latitude stations present moderate-scale changes, and low-latitude regions display more diverse medium- and short-term fluctuations. In the short-term frequency domain, GNSS stations in most regions demonstrate significant PWV periodic variations over 0.5 days, 1 day, or both timescales, except for Changchun, where weak diurnal patterns are attributed to local topography and reduced solar radiation. Furthermore, ERA5-derived vertical temperature profiles are incorporated to reveal the thermodynamic mechanisms driving these variations, underscoring region-specific controls on surface evaporation and atmospheric moisture capacity. These findings offer novel insights into how human-induced environmental changes modulate the behavior of atmospheric water vapor.

Lane detection is one of the key technologies for local map construction, and it is also a challenging task in intelligent driving, where various computer vision-based methods have been applied to address this issue. However, these methods often suffer from redundancy issues due to the sparse and narrow structure of the lane lines, and full generalization to lane detection needs more effort. To solve these problems, we propose a stepwise positive guidance strategy that utilizes the visually presented lane structure characteristics, which are inspired by the reference points in the DETR-Family methods. This strategy guides the network detection from the reference points to the reference lanes, improving the accuracy of the detection process. Moreover, we propose a new multi-scale feature fusion strategy that directly performs feature fusion on high-quality proposals. This approach differs from traditional object detection models using the Feature Pyramid Network (FPN). It fully uses the sparsity of lanes and reduces the network’s redundant computation. We proposed ProposalLaneNet, which takes full advantage of the lanes’ structure and sparse distribution characteristics. Significant improvements in speed and accuracy have been achieved by our method, enabling it to reach the state-of-the-art performance on the popular datasets CULane and TuSimple. Our method can be used as a new detection paradigm for lane detection.

Urbanmobilelaserscanning(MLS)pointcloudsprovidedetailedthree-dimensionalobservationsfor urbangeoinformationextraction,roadsideinfrastructureinventory,andhigh-definitionmapupdating. However,accuratesemanticsegmentationoflarge-scaleMLSpointcloudsremainschallengingdueto irregularpointdistributions,insufficientlong-rangecontextualmodeling,complexfine-grainedroadside structures,andsevereclassimbalance.Toaddresstheseissues,weproposeHS-SSM,aHilbert-aware dual-scalestate-spacemodelingframeworkforurbanMLSpointcloudsemanticsegmentation.HS-SSM introducesBi-SSMat theshallowfeaturestagetoenhancebidirectionalcontextual interactionon high-resolutionserializedfeatures,andHD-SSMatthebottleneckstagetoorganizesparsefeaturesinto Hilbert-ordereddual-scalesequencesforhierarchicallong-rangedependencymodeling.Inaddition,an object-category-prioradaptiveloss,termedOCP-AL,dynamicallyadjustsclass-dependentsupervision toimproveminority-classlearningunderlong-taileddistributions.ExperimentsonHuangshiRoad, WHU_MLS,andMLS-SdemonstratetheeffectivenessandgeneralizabilityofHS-SSM.Compared withthebest-performingbaselineoneachdataset,HS-SSMimprovesmIoUby6.12,6.55,and8.45 percentagepoints, respectively.Class-wiseresultsshowclear improvementsforfine-grainedand minorityroadsideobjects,includingfences,utilitypoles,electricalwires,streetlights,poles,andwires. TheseresultsindicatethatHS-SSMprovidesaneffectivesolutionforcategory-balancedsemantic segmentationofcomplexurbanMLSscenes.

Accurate extraction of building vector contours from high-resolution remote sensing images is a fundamental task for urban mapping and geographic information systems. However, existing approaches often suffer from blurred boundaries and geometric distortions when dealing with buildings of complex shapes, limiting the accuracy and usability of the extracted building footprints. To address these challenges, this paper proposes a multi-level building contour refinement framework based on morphological perception. The proposed framework integrates a three-stage contour optimization strategy, including principal direction extraction, morphology-based contour reconstruction, and geometry-aware regularization, to progressively refine complex building contours under geometric constraints. In addition, a multi-dimensional contour complexity model and an adaptive threshold optimization network are introduced to dynamically adjust refinement parameters according to contour complexity. Experimental results on the WHU-Mix dataset demonstrate that the proposed method outperforms state-of-the-art approaches, achieving 87.52%, 77.43%, and 87.35% in boundary F1, vertex F1, and mIoU, respectively. These results indicate that the proposed framework provides an effective and robust solution for high-precision building vector contour extraction in complex remote sensing scenarios.

To address the uncertainties in emergency shelter siting under compound disaster scenarios, based on the connotative characteristics and formation mechanisms of urban natural hazards, a hazard assessment system for geological and flood disasters was constructed using the Random Forest (RF) algorithm. On this basis, considering the triggering relationships between disasters, the hazard intensity of disaster chains was adjusted by using a multi-hazard coupling incentive model, and the comprehensive hazard index of geological-flood compound disasters was calculated. Then, from the perspectives of accessibility and safety, a comprehensive analysis of the suitability of candidate emergency shelter sites was conducted via the Gaussian Two-step Floating Catchment Area (G2SFCA) method, where the comprehensive hazard assessment coefficient of geological-flood compound disasters was incorporated as a weighting factor affecting the suitability evaluation. Furthermore, an urban emergency shelter siting model was established by using the Multi-Objective Gray Wolf Optimizer (MOGWO). Taking Sanya City as a case study, the results show that: (1) The estimation of area under the curve (AUC) of the single hazard assessment models for geological and flood disasters constructed by the RF were 0.904 and 0.899, respectively. The high-hazard zones of geological-flood compound disasters were mainly concentrated in the mountain-valley transition zones of Tianya District and Jiyang District, as well as the potential storm surge-affected zones along the southern coast. (2) The model constructed based on the MOGWO under the influence of compound disasters could effectively make up for the deficiencies in the spatial layout of current shelter siting schemes and achieve effective connection between hazard assessment and spatial planning. The methods mentioned provide a scientific basis for risk zoning control in urban territorial spatial planning and disaster prevention and mitigation in emergency management.