Search Results (2,016)

Remote sensing change detection (CD) is a technique for quantitatively analyzing and determining the characteristics and processes of surface change using bi-temporal remote sensing data. Deep convolutional networks have achieved remarkable success in CD tasks. However, due to the complexity of the natural lighting environment and other factors, how to use bi-temporal images and segment objects more accurately and effectively has become a focus of research. Many existing studies have overlooked the relationship between samples, disregarding the potential connection between the same semantics across the entire sample set. Moreover, they have ignored the semantic connection between bi-temporal images and have resorted to simple techniques such as concatenation or absolute value subtraction to achieve bi-temporal feature fusion, resulting in information loss. We propose a cross-image feature interaction network consisting of three modules to address the above issues: cross-image non-local enhancement (CINE) module, which can enhance the spatial dimensional links between the same type of objects in the sample space and explores the potential relationship between the same semantics samples on the whole sample set; cross-temporal feature enhancement (CTFE) module, which interacts with bi-temporal image features to enhance real change features while suppressing irrelevant change features; and difference feature adaptive fusion (DFAF) module, which can make effective use of the bi-temporal image features extracted by the network and adaptively learns the fusion parameters. We conducted extensive experiments on two CD datasets, LEVIR-CD and DSIFN-CD, and obtained evaluation scores of 90.75%/83.07% and 69.94%/53.78% on the F1-score and IoU metrics, respectively. Our strategy surpasses existing attention-based approaches such as BIT. Full article

Machine Learning (ML) modeling for disaster management is a growing field, but existing works focus more on mapping the extent of floods or broad categories of damage and they lack methods for explainability to help users understand model outputs. In this study, we propose Flood Assessment using Dempster–Shafer Fusion (FADS-Fusion), a tool for addressing post-flood damage assessment using Dempster–Shafer fusion to combine outputs from multiple deep learning models. FADS-Fusion is generalized to use any pretrained models, once outputs are post-processed for consistency, making it applicable for other disaster management or change detection applications. The novelty of our work comes from the application of Dempster–Shafer for multi-model fusion and uncertainty quantification on a flood dataset for segmenting both buildings and roads. We trained and evaluated models using the SpaceNet 8 challenge dataset and demonstrated that the fusion of the SpaceNet 8 Baseline (SN8) and Siamese Nested UNet (SNUNet) models has a modest overall improvement +1.93% to mAP, while a +12.3% increase for Precision and a −15.0% decrease in Recall are statistically significant compared to the baseline. FADS-Fusion also quantifies uncertainty by using the conflict of evidence, with a discount factor, with Dempster–Shafer fusion as both a quantitative and qualitative explainability method. While uncertainty correlates with a drop in performance, this relationship depends on values for class-weighted uncertainty and location. Mapping uncertainty back onto the original image allows for a visual inspection on fusion quality and indicates areas where a human will need to reassess. Our work demonstrates that FADS-Fusion improves post-flood segmentation performance and adds the benefit of uncertainty quantification for explainability, an aspect important for reliability and user decision-making but understudied in ML for disaster management in the literature. Full article

In recent years, rapid and reliable interpretation for emergency response to landslides and other geological hazards has become increasingly important. This paper presents DFmamba, an improved deformable dual-branch visual state-space network, to address engineering challenges such as missed large landslide bodies, boundary shifts, and loss of small-scale details. DFmamba mitigates the limited effective receptive field and window-partition constraints that often prevent existing methods from balancing large-area semantic consistency, multi-scale detection, precise boundary delineation, and computational efficiency. It employs a parallel encoder with a convolutional branch and a Visual State-Space Model (VSSM) branch to jointly capture local textures and global context. In the decoder, deformable residual blocks (DRB) enhance geometric modeling of irregular boundaries, while multi-scale feature alignment and a shallow high-frequency injection (MFP) mechanism strengthen boundary responses and preserve fine details. Experiments on the public CAS dataset against representative CNN-, Transformer-, and SSM-based baselines show that DFmamba achieves improved Precision, Recall, F1-score, and IoU, with stable performance across multi-scale scenarios, demonstrating strong robustness for landslide segmentation. Full article

Remote sensing-based Earth observation provides essential spatial data for analyzing and monitoring both natural and urban environments. Precise characterization of objects in these scenes is vital for environmental management, land-use planning, and monitoring global change. Semantic segmentation of remote sensing imagery (RSI) is a fundamental yet complex task due to significant variability in object shape, scale, and distribution, as well as the complexity of multiscale landscapes captured by advanced sensors. Convolutional neural networks, especially the U-Net architecture, have achieved notable success in segmentation tasks. However, their application in remote sensing is often impeded by persistent issues such as loss of spatial detail, substantial intra- and inter-class variability, and high sensitivity to hyperparameter settings. Manual tuning of hyperparameters is typically inefficient and error-prone, which highlights the importance of heuristic methods for automated optimization. Genetic Algorithms (GAs), Differential Evolution (DE), and Particle Swarm Optimization (PSO) are metaheuristics that provide systematic approaches for exploring large hyperparameter spaces. This study investigates an evolutionary framework for the automated optimization of four critical U-Net hyperparameters—learning rate, number of training epochs, optimizer, and loss function—using micro-evolutionary algorithms. Specifically, micro Genetic Algorithms (micro-GAs), micro Differential Evolution (micro-DE), and micro Particle Swarm Optimization (micro-PSO) are employed to efficiently explore the hyperparameter search space under reduced population settings. The experimental results demonstrate that the proposed micro-evolutionary optimization framework consistently enhances segmentation performance, achieving improvements in Mean Intersection over Union (MIoU) ranging from 3% to 35%, along with systematic gains in overall accuracy across different datasets and configurations. Full article

Segmental tunnel linings represent a conventional method commonly employed in tunnel boring machine (TBM) operations. However, this approach presents notable limitations, including handling challenges and the presence of numerous joints prone to leakage. An alternative method involving cast-in-place tunnel lining was experimentally investigated through a scaled mock-up test conducted at approximately 1:4 scale, with a total length of 0.85 m and 2 m lining diameter. In this setup, two reinforced concrete rings were constructed to simulate the surrounding geological conditions and internal formwork. Fiber-reinforced concrete was then pumped into the annular space between the rings, forming a cast-in-place lining with a thickness of 170 mm. To replicate the thrust force exerted by hydraulic actuators of a TBM, a hydrostatic pressure up to 5 MPa was applied from the front side. The experiment demonstrated a linear compaction of fresh concrete by approximately 3%, greater resistance to compaction in the lower section, and a uniformly well-compacted concrete structure throughout the entire volume. Full article

Accurate quantification of vegetation vertical structure is crucial for analyzing the ecological functions of urban green spaces. However, constrained by the complexity of vegetation structure and spatial heterogeneity, current approaches for extracting vegetation vertical structure by airborne LiDAR have limitations in terms of layer boundary identification stability, threshold dependency, and ecological plausibility. This study developed two integrated UAV LiDAR-based stratification frameworks for identifying urban riparian vegetation vertical structure by combining established statistical modeling and signal processing techniques: (1) a Gaussian Mixture Model with Bayesian Information Criterion (GMM-BIC)-based probabilistic stratification framework; (2) a Savitzky–Golay filtering and Pruned Exact Linear Time (SG-PELT)-based change-point detection framework. Furthermore, the ecological height constraint was incorporated into the model to achieve biological adjustments. Two models were applied in the study area and compared using reference data. The results showed that the GMM-BIC method achieved an overall classification accuracy of 91.06%, with a macro-averaged F1-score of 87.77%, while the SG-PELT method attained an overall accuracy of 84.57%, with a macro-averaged F1-score of 79.20%. These results demonstrate that both models can effectively identify the vertical structure of urban vegetation. In particular, the two models exhibited distinct characteristics across different scenarios. The GMM-BIC model showed superior stratification accuracy in regions where vegetation height distribution displayed pronounced multi-peak characteristics and distinct differences among height segments. In comparison, the SG-PELT model demonstrated greater sensitivity in areas with significant height variation and clearly defined abrupt transitions between layers. These models could provide new methodologies for monitoring vegetation vertical structure and offer data support for biodiversity monitoring and ecological function assessment within urban ecosystems. Full article

Accurate quantification of spatial distances between fluorescent signals in multi-channel 3D microscopy is essential for understanding genomic organization and gene regulation. However, chromatic aberration introduces systematic spatial offsets between channels that significantly bias distance measurements, particularly at short genomic distances. We present FISH-Dist, an automated computational pipeline for quantitative distance measurements in 3D fluorescence in situ hybridization (FISH) experiments acquired on standard confocal microscopes. Our method combines deep learning-based spot segmentation, 3D Gaussian fitting for sub-pixel localization, and two complementary chromatic aberration correction approaches: affine (ACC) and linear (LCC). We validated the pipeline by measuring the lengths of DNA origami nanorulers and systematically evaluated FISH probe design parameters, including probe spacing, density, and target sequence length. FISH-Dist achieves sub-pixel accuracy in signal detection and substantially reduces inter-channel distance measurement errors. This enables a reproducible quantification of spatial relationships in 3D FISH datasets. Unlike existing tools optimized for long-range chromosomal interactions or requiring super-resolution microscopy, FISH-Dist specifically addresses the technical challenges of standard confocal imaging at short genomic distances, where chromatic aberration has a proportionally greater impact on measurement accuracy. Full article

People’s perception of the comfort level of street landscape elements is influenced by the built environment, and improving the quality of street landscape environment is of great significance for promoting the sustainable development of cities. This study focuses on 12 sample streets in Zibo City. After obtaining panoramic images of the area through the OSM platform, the FCN framework was used for semantic segmentation. A combination of subjective and objective methods was adopted, and eye tracking indicators were collected using the D-Lab wearable eye tracker. At the same time, a questionnaire quantitative analysis was conducted to systematically investigate the impact mechanism of the combination characteristics of street elements on comfort perception preferences. Research has found that there is a significant correlation between the perceived comfort preference of street scenes and GVI, and the increase in total gaze time towards green elements also shows a significant improvement in perceived comfort preference. After entering the street interface, observers show a high degree of priority attention to street view elements such as building facades and advertising facilities. As the gaze time on the sky (a street view element) increases, people’s perceived comfort evaluation shows a downward trend. There are significant differences in the structural characteristics of different streets, and their impact on improving comfort also varies to some extent. This study links the comfort perception of street landscape elements with sustainable urban development planning. By reasonably allocating landscape elements such as green visibility, basic roads, building interfaces, and signage facilities, it provides certain reference suggestions for the sustainable development of urban street space and human-centered urban construction. Full article

Dynamic inductive power transfer (DIPT) can enable dynamic wireless charging for urban micromobility, but deployment is constrained by electromagnetic field (EMF) exposure compliance and by lateral and angular misalignment typical of two-wheeled vehicles. This review consolidates the state of the art and links these constraints to smart grid control and charging optimisation. It frames dynamic charging lanes as corridor infrastructure that behaves as a distributed electrical load whose demand depends on traffic and availability, with segmentation control as a key lever for controllability. It then synthesises practical system architectures that combine power electronics, segmented transmitters, sensing, communication, and supervisory control, because these interfaces determine which degrees of freedom are available to shape demand in space and time. The review also summarises coupler, shielding, and compensation choices that jointly determine efficiency, misalignment robustness, and EMF leakage. Finally, it surveys scheduling methods that incorporate network limits, output from distributed energy resources, and uncertainty through rolling horizon, robust, and risk-constrained formulations. The synthesis supports deployment aligned with renewable integration and sustainable urban mobility, and it highlights open needs in forecasting robustness, scalable optimisation, and secure interoperability. Full article

Reliable and up-to-date measurements of public space remain scarce in small and medium-sized towns (SMSTs), where conventional geospatial datasets are often outdated, inconsistent, or inaccessible. This study presents a low-cost and fully reproducible computational pipeline that integrates nadir RGB imagery captured by a DJI Mini 3 UAV with a lightweight instance-segmentation model (Ultralytics YOLOv12-seg) and GIS-based post-processing to derive class-specific surface indicators at the neighborhood scale. The workflow consists of four components: autonomous UAV acquisition over three representative zones of Sesquilé, Colombia; planar mosaic generation and georeferencing using ad hoc ground control points; fine-tuning of a YOLOv12-seg model trained on locally annotated images; and transformation of predicted masks into OSM and GeoPackage geometries for metric analysis. The trained model achieved stable convergence with mask mAP50 ≈ 0.85 and mAP50–95 ≈ 0.70, supported by balanced precision–recall behavior across classes. Spatial outputs exhibit coherent morphological contrasts between the analyzed zones. Buildings occupy 48.17% of the mapped area, vegetation 25.88%, and transport- and plaza-related public space (roadways, sidewalks, and hardscape areas) 25.95%. These proportions capture a clear gradient from a dense urban core to less consolidated peripheral sectors. Results demonstrate that very-high-resolution UAV imagery, combined with open-source deep-learning tools and structured GIS post-processing, can reliably produce operational public-space indicators for SMSTs at low cost. The methodology provides an accessible and scalable framework for evidence-based urban assessment in municipalities with limited technical and financial resources. Full article

This study proposes a clinically driven hybrid segmentation framework for dense breast tissue analysis in mammographic images, addressing persistent challenges associated with intensity inhomogeneity, low-contrast, and complex tumor morphology. The framework integrates Multiplicative Intrinsic Component Optimization (MICO_2D) for bias field correction, followed by a distance-regularized multiphase Vese–Chan level-set model for coarse global tumor segmentation. To achieve precise boundary delineation, a localized refinement stage is employed using Localized Active Contours (LAC) with Local Image Fitting (LIF) energy, supported by Gaussian regularization to ensure smooth and coherent boundaries in regions with ambiguous tissue transitions. Building upon the refined semantic tumor mask, the framework further incorporates a panoptic-style tumor instance segmentation stage, enabling the decomposition of connected tumor regions into distinct anatomical instances, which were evaluated on both MIAS and INBreast mammography datasets to demonstrate generalizability. This extension facilitates detailed structural analysis of tumor multiplicity and spatial organization, enhancing interpretability beyond conventional pixel wise segmentation. Experiments conducted on Cranio-Caudal (CC) and Medio-Lateral Oblique (MLO) mammographic views demonstrate competitive performance relative to baseline U-Net and advanced deep learning fusion architectures, including multi-scale and multi-view networks, while offering improved interpretability and robustness. Quantitative evaluation using overlap-related metrics shows strong spatial agreement between predicted and reference segmentations, with per-image Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) distributions reported to ensure reproducibility. Descriptive per-image analysis, supported by bootstrap-based confidence intervals and paired comparisons, indicates consistent performance improvements across images. Robustness analysis under realistic perturbations, including noise, contrast degradation, blur, and rotation, demonstrates stable performance across varying imaging conditions. Furthermore, feature space visualizations using t-SNE and UMAP reveal clear separability between cancerous and non-cancerous tissue regions, highlighting the discriminative capability of the proposed framework. Overall, the results demonstrate the effectiveness, robustness, and clinical motivation of this hybrid panoptic framework for comprehensive dense breast tumor analysis in mammography, while emphasizing reproducibility and conservative statistical assessment. Full article

The mainstreaming of remote work has catalyzed the rise of the new tribe, the kinetic elite, a demographic comprising digital nomads and workationers who utilize technology to separate work from geography. Yet, this apparently free lifestyle often leads to a freedom trap, where the collapsing boundaries between work and leisure necessitate intense self-discipline within spaces originally architected for tourism. Drawing on an integrated framework of quality of destination features, service, and experience, this study investigates the antecedents of satisfaction and loyalty for this niche market of mobile workforce. Data were collected from 325 international digital nomads and workationers in Thailand using a purposive sampling approach. The proposed integrated model was empirically tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The analysis challenges the hardware-first paradigm of destination development. Findings indicate that while digital infrastructure (connectivity) and geoarbitrage (value) are non-negotiable baselines, they employ limited influence on ultimate satisfaction. Instead, human infrastructure, specifically the quality of staff and host–community interactions, emerges as the primary determinant in converting a location from a travel stop into a functional home base. These results advocate for a strategic plan toward precision niche marketing, moving beyond a homogenous view of the sector to target the community-seeking segment. Furthermore, the study frames community integration as a core practice of social sustainability, suggesting that for destinations to evolve into vibrant knowledge ecologies, Destination Management Organizations (DMOs) must prioritize community facilitation and smart policies that mitigate the social isolation inherent in nomadic life. Full article

Unmanned Aerial Vehicle (UAV), when equipped as communication relays, offer a flexible solution to extend Vehicle-to-Vehicle (V2V) communications beyond fixed infrastructure and Non-Line-of-Sight constraints. In this setting, the allocation of radio resources, across time, frequency and space through beamforming, is challenged by the mobility of Connected and Autonomous Vehicles (CAVs) and their temporal dependencies, as access opportunities depend on prior transmission outcomes such as queue backlog or failed attempts. This paper proposes a Radio Resource Assignment (RRA) framework for UAV-aided V2V networks with beamforming-capable UAV relays. The model discretizes time and space to account for mobility and to track the movement of groups of CAVs across beam segments. The model also incorporates Time Division Multiple Access (TDMA)-based scheduling, beam activation constraints, and realistic traffic generation patterns. Analytical expressions are derived for per-user success probability and system throughput under both, ideal and realistic conditions, and they are validated against simulations, confirming the accuracy of the proposed approximations. Numerical results highlight trade-offs involving UAV altitude and resource allocation interval, while a heuristic beam-activation optimization strategy is shown to further enhance performance, achieving up to 12% throughput gain over uniform activation. Full article

Historic linear corridors in living-heritage settings concentrate identity, everyday mobility, and visitor experience. Balancing authenticity, adaptability, and publicness therefore benefits from evidence that jointly characterizes long-term physical change, network accessibility, and eye-level interface conditions. Existing assessments often focus on façades or single time slices, leaving limited evidence that relates decades of built-fabric reconfiguration (changes in building footprints, street edges, and open-space fragmentation) to multi-scale accessibility and pedestrian-facing qualities. We propose an integrated and interpretable workflow for the Beishan Street corridor in the West Lake World Heritage core (Hangzhou) over 1929–2024. Scale-sensitive morphological metrics, multi-radius network measures (integration and centrality), and street-view semantic segmentation are aligned at corridor-segment resolution and examined together with segment-level functional intensity derived from POIs using transparent linear models. The results indicate a long-term shift from a lakeshore-led to a road-led spatial logic, followed by post-2000 stabilization near saturation. Average integration increases, while the high-integration tail becomes thinner. In connector-removal scenarios, the eastern segment shows a relative accessibility decline, and a central hinge node emerges as a vulnerability hotspot (bottleneck) where through-movement concentrates. Eye-level profiles differ by segment: the west exhibits maximal canopy and lower sky visibility, the center shows stronger continuous walls around compounds with intermittent forecourt openings, and the east is characterized by compact residential heritage frontage with low vegetation. Segment-level associations suggest that address and wayfinding density tends to co-occur with clearer frontages, wider sky cones, and stronger tree cover. Transportation-related and access/passage facilities tend to co-occur with higher ground-plane legibility, measured as wider and more continuous road and sidewalk surfaces. Medical and government clusters tend to co-occur with lower sky openness. Recommended actions include the following: (1) mesh-aware protection of key connectors and the hinge, (2) segment-specific targets for façade share and ground cues with planned punctuations, (3) tailored interface standards for institutional clusters, (4) scalable address and wayfinding systems, and (5) event staging that preserves effective roadway and sidewalk capacity. Full article

This study addresses the shortcomings of conventional orbital dynamics methods in order to determine initial orbits for short-arc segments of space objects. By integrating the temporal characteristics of observational data, we innovate a multi-scale Informer temporal modeling approach, proposing a high-precision algorithm for short-arc-segment initial orbit determination. The study analyses why Informer models yield differing results across various time windows. First, a radar observation target model accounting for multiple perturbations and a training data generator were established to produce training data for the Informer. Subsequently, an Informer network framework was designed, encompassing data preprocessing, network architecture, and training algorithms. Realistic scenarios and evaluation metrics were then configured for digital simulation. The model’s feasibility for low-Earth-orbit satellites was validated through digital simulation for different scenarios. The results in Scenario 1 demonstrate that compared to DNN methods, this approach achieves improvements in Root Mean Square Error (RMSE) across six dimensions in ECI—x, y, z, vx, vy, and vz—of 84.04%, 80.56%, 41.38%, 60.00%, 89.03%, and 64.17% respectively; compared to the best results of the Gibbs method across different windows, this approach improves the RMSE by 25%, 23%, and 46% in the three velocity dimensions (vx, vy, and vz) in the ECI frame, respectively. The results in Scenario 2 demonstrate the universality of this method. Furthermore, the reasons for differing outcomes across Informer models with varying time windows were analyzed, alongside the rationale for the integrated Informer model outperforming individual Informer models. Full article