Map Generation Research Articles

Enhancing network security with hybrid feedback systems in chaotic optical communication

This paper presents a pioneering approach to bolstering network security and privacy by implementing chaotic optical communication with a hybrid optical feedback system (HOFS). The current baseline methods in network security are often less feasible for hybrid feedback systems, including limited robustness, compromised security, and synchronization challenges. Therefore, this paper proposes a hybrid approach to address these shortcomings by integrating the HOFS into chaotic optical communication systems (HOFS-COCS) to overcome the baseline challenges. This paper aims to improve network security while significantly maintaining efficient communication channels. Moreover, We designed two algorithms, one for chaotic maps generation and another for text encryption and decryption, to improve security in the hybrid feedback system. Our findings demonstrate through rigorous experimentation and analysis that the proposed (HOFS-COCS) method significantly improves network security by enabling reliable chaos generation, synchronization, and secure message transmission in chaotic optical communication systems. This research represents a significant advancement towards enhanced secrecy and synchronization in chaotic optical communication systems, promising a paradigm shift in network security protocols.

Road Boundary Extraction Method from Mobile Laser Scanning Point Clouds

Abstract With the rapid development of mobile laser scanning (MLS) technology, high-precision three-dimensional point cloud data has shown great potential in different fields such as topographic mapping, road asset management and smart city construc-tion. Three-dimensional point cloud data contains not only position information, but also the shape and attributes of the target, which is very convenient for obtaining road information. Accurate extraction of the road boundary is a basic task for obtaining road infrastructural data, which can support the generation of high-precision maps, vehicle navigation and auton-omous driving. However, road boundary extraction in urban environments is easily occluded such as vehicles and pedestri-ans on the road, which leads to problems such as difficulty and incomplete extraction of MLS point cloud road boundaries. To address this problem, this study proposes a road boundary extraction method that integrates pavement edge information, accurately considers the position of the road boundary from two dimensions, eliminates false boundaries, and completes the missing boundary through the extracted boundary spatial relationship. First, grid elevation filtering is used to remove high-level non-ground points. Then the pavement edges and curb stone points are extracted from the preprocessed point cloud, and they are superimposed to remove false boundary points to obtain accurate road boundaries. Finally, based on the spatial relationship of road boundaries, missing parts are detected and repaired to obtain complete road boundaries. Experimental results show that the accuracy on real road scenes exceeds 98%, the completeness rate is above 91%, and the extraction quality is above 90%, which verifies the effectiveness and accuracy of this method.

Automated Generation of Urban Land Cover Maps and Their Enhancement and Regularization

Automated Generation of Urban Land Cover Maps and Their Enhancement and Regularization

Deep-learning-based attenuation map generation in kidney single photon emission computed tomography

BackgroundAccurate attenuation correction (AC) is vital in nuclear medicine, particularly for quantitative single-photon emission computed tomography/computed tomography (SPECT/CT) imaging. This study aimed to establish a CT-free quantification technology in kidney SPECT imaging using deep learning to generate synthetic attenuation maps (μ-maps) from SPECT data, thereby reducing radiation exposure and eliminating the need for CT scans.ResultsA dataset of 1000 Tc-99m DTPA SPECT/CT scans was analyzed for training (n = 800), validation (n = 100), and testing (n = 100) using a modified 3D U-Net for deep learning. The study investigated the use of primary emission and scattering SPECT data, normalization methods, loss function optimization, and up-sampling techniques for optimal μ-map generation. The problem of checkerboard artifacts, unique to μ-map generation from SPECT signals, and the effects of iodine contrast media were evaluated. The addition of scattering SPECT to primary emission SPECT imaging, logarithmic maximum normalization, the combination of absolute difference loss (L1) and three times the absolute gradient difference loss (3 × LGDL), and the nearest-neighbor interpolation significantly enhanced AI performance in μ-map generation (p < 0.00001). Checkerboard artifacts were effectively eliminated using the nearest-neighbor interpolation technique. The developed AI algorithm produced μ-maps neutral to the presence of iodine contrast and showed negligible contrast effects on quantitative SPECT measurement, such as glomerular filtration rate (GFR). The potential reduction in radiation exposure by transitioning to AI-based CT-free SPECT imaging ranges from 45.3 to 78.8%.ConclusionThe study successfully developed and optimized a deep learning algorithm for generating synthetic μ-maps in kidney SPECT images, demonstrating the potential to transition from conventional SPECT/CT to CT-free SPECT imaging for GFR measurement. This advancement represents a significant step towards enhancing patient safety and efficiency in nuclear medicine.

MapGen-Diff: An End-to-End Remote Sensing Image to Map Generator via Denoising Diffusion Bridge Model

Online maps are of great importance in modern life, especially in commuting, traveling and urban planning. The accessibility of remote sensing (RS) images has contributed to the widespread practice of generating online maps based on RS images. The previous works leverage an idea of domain mapping to achieve end-to-end remote sensing image-to-map translation (RSMT). Although existing methods are effective and efficient for online map generation, generated online maps still suffer from ground features distortion and boundary inaccuracy to a certain extent. Recently, the emergence of diffusion models has signaled a significant advance in high-fidelity image synthesis. Based on rigorous mathematical theories, denoising diffusion models can offer controllable generation in sampling process, which are very suitable for end-to-end RSMT. Therefore, we design a novel end-to-end diffusion model to generate online maps directly from remote sensing images, called MapGen-Diff. We leverage a strategy inspired by Brownian motion to make a trade-off between the diversity and the accuracy of generation process. Meanwhile, an image compression module is proposed to map the raw images into the latent space for capturing more perception features. In order to enhance the geometric accuracy of ground features, a consistency regularization is designed, which allows the model to generate maps with clearer boundaries and colorization. Compared to several state-of-the-art methods, the proposed MapGen-Diff achieves outstanding performance, especially a 5% RMSE and 7% SSIM improvement on Los Angeles and Toronto datasets. The visualization results also demonstrate more accurate local details and higher quality.

Small-scale noise mapping for arbitrary regions using open source noise prediction module "NoiseModelling" and global databases

Extensive noise mapping has been crucial in shaping noise mitigation policies; however, assessing noise exposure in different scenarios remains challenging due to static maps that only represent pre-calculated sound levels. To fill this gap, this study introduces a novel noise mapping framework tailored to calculate sound levels in specific environments within small, arbitrary regions. Using the open-source noise prediction tool NoiseModelling and global databases of road networks, buildings, terrain, and population distribution on QGIS, this framework seamlessly integrates noise mapping procedures, including comprehensive data collection, receiver placement, sound level calculation, and visualization. The user-friendly graphical interfaces enable quick and easy generation of noise maps. The approach offers flexibility through customizable geometry and parameter settings to account for variations in noise sources and propagation characteristics, thereby providing stakeholders with valuable insights for policy formulation, environmental impact assessment, and community engagement. This methodology not only improves the accessibility and relevance of noise mapping but also empowers decision makers to implement tailored noise mitigation strategies that promote healthier and more livable environments.

Skin Tone Analysis Through Skin Tone Map Generation With Optical Approach and Deep Learning.

Skin tone assessment is critical in both cosmetic and medical fields, yet traditional methods like the individual typology angle (ITA) have limitations, such as sensitivity to illuminants and insensitivity to skinredness. This study introduces an automated image-based method for skin tone mapping by applying optical approaches and deep learning. The method generates skin tone maps by leveraging the illuminant spectrum, segments the skin region from face images, and identifies the corresponding skin tone on the map. The method was evaluated by generating skin tone maps under three standard illuminants (D45, D65, and D85) and comparing the results with those obtained using ITA on skin tone simulationimages. The results showed that skin tone maps generated under the same lighting conditions as the image acquisition (D65) provided the highest accuracy, with a color difference of around 6, which is more than twice as small as those observed under other illuminants. The mapping positions also demonstrated a clear correlation with pigment levels. Compared to ITA, the proposed approach was particularly effective in distinguishing skin tones related toredness. Despite the need to measure the illuminant spectrum and for further physiological validation, the proposed approach shows potential for enhancing skin tone assessment. Its ability to mitigate the effects of illuminants and distinguish between the two dominant pigments offers promising applications in both cosmetic and medicaldiagnostics.

FHLight: A novel method of indoor scene illumination estimation using improved loss function

In augmented reality tasks, especially in indoor scenes, achieving illumination consistency between virtual objects and real environments is a critical challenge. Currently, mainstream methods are illumination parameters regression and illumination map generation. Among these two categories of methods, few works can effectively recover both high-frequency and low-frequency illumination information within indoor scenes. In this work, we argue that effective restoration of low-frequency illumination information forms the foundation for capturing high-frequency illumination details. In this way, we propose a novel illumination estimation method called FHLight. Technically, we use a low-frequency spherical harmonic irradiance map (LFSHIM) restored by the low-frequency illumination regression network (LFIRN) as prior information to guide the high-frequency illumination generator (HFIG) to restore the illumination map. Furthermore, we suggest an improved loss function to optimize the network training procedure, ensuring that the model accurately restores both low-frequency and high-frequency illumination information within the scene. We compare FHLight with several competitive methods, and the results demonstrate significant improvements in metrics such as RMSE, si-RMSE, and Angular error. In addition, visual experiments further confirm that FHLight is capable of generating scene illumination maps with genuine frequencies, effectively resolving the illumination consistency issue between virtual objects and real scenes. The code is available at https://github.com/WA-tyro/FHLight.git.

TAENet: Two-branch Autoencoder Network for Interpretable Deepfake Detection

Deepfake detection attracts increasingly attention due to serious security issues caused by facial manipulation techniques. Recently, deep learning-based detectors have achieved promising performance. However, these detectors suffer severe untrustworthy due to the lack of interpretability. Thus, it is essential to work on the interpretibility of deepfake detectors to improve the reliability and traceability of digital evidence. In this work, we propose a two-branch autoencoder network named TAENet for interpretable deepfake detection. TAENet is composed of Content Feature Disentanglement (CFD), Content Map Generation (CMG), and Classification. CFD extracts latent features of real and forged content with dual encoder and feature discriminator. CMG employs a Pixel-level Content Map Generation Loss (PCMGL) to guide the dual decoder in visualizing the latent representations of real and forged contents as real-map and fake-map. In classification module, the Auxiliary Classifier (AC) serves as map amplifier to improve the accuracy of real-map image extraction. Finally, the learned model decouples the input image into two maps that have the same size as the input, providing visualized evidence for deepfake detection. Extensive experiments demonstrate that TAENet can offer interpretability in deepfake detection without compromising accuracy.

Transforming satellite imagery into vector maps using modified GANs

Vector maps find widespread utility across diverse domains due to their capacity to not only store but also represent discrete data boundaries such as building footprints, disaster impact analysis, digitization, urban planning, location points, transport links, and more. Although extensive research exists on identifying building footprints and road types from satellite imagery, the generation of vector maps from such imagery remains an area with limited exploration. Furthermore, conventional map generation techniques rely on labor-intensive manual feature extraction or rule-based approaches, which impose inherent limitations. To surmount these limitations, we propose a novel method called HPix, which utilizes modified Generative Adversarial Networks (GANs) to generate vector tile map from satellite images. HPix incorporates two hierarchical frameworks: one operating at the global level and the other at the local level, resulting in a comprehensive model. Through empirical evaluations, our proposed approach showcases its effectiveness in producing highly accurate and visually captivating vector tile maps derived from satellite images. We achieved a pixel-level accuracy of 61.04% and an SSIM score of 0.75, outperforming all other existing methods. We further extend our study’s application to include mapping of road intersections and building footprints cluster based on their area. We also show usability of our proposed architecture as a general-purpose solutions in other tasks like edges-to-photo, BW-to-color, or labels-to-street scene. GitHub:https://github.com/aditya-taparia/Satellite-Image-to-Vector-Map.

P-MapNet: Far-Seeing Map Generator Enhanced by Both SDMap and HDMap Priors

P-MapNet: Far-Seeing Map Generator Enhanced by Both SDMap and HDMap Priors

Myrmecophaga tridactyla (Linnaeus, 1758) subfossils from Abismo Anhumas, Bonito/MS, Brazil: Morphology, isotopic habitat (δ13C, δ18O), radiocarbon dating, biogeography and human impact on the species conservation in Brazil

ABSTRACTMyrmecophaga tridactyla Linnaeus, 1758 is the largest extant anteater, being distributed in most biomes from southern Central America and northern South America. Herein, we analyzed cranial and postcranial elements of three partial skeletons of M. tridactyla found submerged in Abismo Anhumas cave (Bonito, Mato Grosso do Sul, Brazil). The bones collected were the skull (LEG 2598), left humerus (LEG 2601), ungueal phalanx (LEG 2602), and lumbar vertebrae (LEG 2599; LEG 2600). Radiocarbon dating and isotope analyses indicate that these animals lived between 360 and 560 cal a BP in an arboreal to open savanna habitat associated with the Cerrado, a Seasonal Dry Forest. Radiocarbon‐dated oxygen isotopes are possibly in agreement with dated oxygen isotopes found in stalagmites, suggesting a wet period between 442–364 cal a BP in the region. Finally, we created paleo‐species distribution models, which allowed the generation of a consensus map showing a historically stable area between 21 and 6 ka for this species. Currently, more than 50% of this area has been destroyed by human activity.

Tactile Simultaneous Localization and Mapping Using Low-Cost, Wearable LiDAR

Tactile maps are widely recognized as useful tools for mobility training and the rehabilitation of visually impaired individuals. However, current tactile maps lack real-time versatility and are limited because of high manufacturing and design costs. In this study, we introduce a device (i.e., ClaySight) that enhances the creation of automatic tactile map generation, as well as a model for wearable devices that use low-cost laser imaging, detection, and ranging (LiDAR,) used to improve the immediate spatial knowledge of visually impaired individuals. Our system uses LiDAR sensors to (1) produce affordable, low-latency tactile maps, (2) function as a day-to-day wayfinding aid, and (3) provide interactivity using a wearable device. The system comprises a dynamic mapping and scanning algorithm and an interactive handheld 3D-printed device that houses the hardware. Our algorithm accommodates user specifications to dynamically interact with objects in the surrounding area and create map models that can be represented with haptic feedback or alternative tactile systems. Using economical components and open-source software, the ClaySight system has significant potential to enhance independence and quality of life for the visually impaired.

MRI-Based Deep Learning for Differentiating Between Bipolar and Major Depressive Disorders

Mood disorders, particularly bipolar disorder (BD) and major depressive disorder (MDD), manifest changes in brain structure that can be detected using structural magnetic resonance imaging (MRI). Although structural MRI is a promising diagnostic tool, prevailing diagnostic criteria for BD and MDD are predominantly subjective, sometimes leading to misdiagnosis. This challenge is compounded by a limited understanding of the underlying causes of these disorders. In response, we present SE-ResNet, a Residual Network (ResNet)-based framework designed to discriminate between BD, MDD, and healthy controls (HC) using structural MRI data. Our approach extends the traditional Squeeze-and-Excitation (SE) layer by incorporating a dedicated branch for spatial attention map generation, equipped with soft-pooling, a 7 × 7 convolution, and a sigmoid function, intended to detect complex spatial patterns. The fusion of channel and spatial attention maps through element-wise addition aims to enhance the model's ability to discriminate features. Unlike conventional methods that use max-pooling for downsampling, our methodology employs soft-pooling, which aims to preserve a richer representation of input features and reduce data loss. When evaluated on a proprietary dataset comprising 303 subjects, the SE-ResNet achieved an accuracy of 85.8%, a recall of 85.7%, a precision of 85.9%, and an F1 score of 85.8%. These performance metrics suggest that the SE-ResNet framework has potential as a tool for detecting psychiatric disorders using structural MRI data.

RiskPath: Explainable deep learning for multistep biomedical prediction in longitudinal data.

Predicting individual and population risk for disease outcomes and identifying persons at elevated risk is a key prerequisite for targeting interventions to improve health. However, current risk stratification tools for the common, chronic diseases that develop over the lifecourse and represent the majority of disease morbidity, mortality and healthcare costs are aging and achieve only moderate predictive performance. In some common, highly morbid conditions such as mental illness no risk stratification tools are yet available. There is an urgent need to improve predictive performance for chronic diseases and understand how cumulative, multifactorial risks aggregate over time so that intervention programs can be targeted earlier and more effectively in the disease course. Chronic diseases are the end outcomes of multifactorial risks that increment over years and represent cumulative, temporally-sensitive risk pathways. However, tools in current clinical use were constructed in older data and utilize inputs from a single data collection step. Here, we present RiskPath, a multistep deep learning method for temporally-sensitive biomedical risk prediction tailored for the constraints and demands of biomedical practice that achieves very strong performance and full translational explainability. RiskPath delineates and quantifies cumulative multifactorial risk pathways and allows the user to explore performance-complexity tradeoffs and constrain models as required by clinical use cases. Our results highlight the potential for developing a new generation of risk stratification tools and risk pathway mapping in time-dependent diseases and health outcomes by leveraging powerful timeseries deep learning methods in the wealth of biomedical data now appearing in large, longitudinal open science datasets.

A Robust Generalized Zero-Shot Learning Method with Attribute Prototype and Discriminative Attention Mechanism

In the field of Generalized Zero-Shot Learning (GZSL), the challenge lies in learning attribute-based information from seen classes and effectively conveying this knowledge to recognize both seen and unseen categories during the training process. This paper proposes an innovative approach to enhance the generalization ability and efficiency of GZSL models by integrating a Convolutional Block Attention Module (CBAM). The CBAM blends channel-wise and spatial-wise information to emphasize key features, thereby improving the model’s discriminative and localization capabilities. Additionally, the method employs a ResNet101 backbone for systematic image feature extraction, enhanced contrastive learning, and a similarity map generator with attribute prototypes. This comprehensive framework aims to achieve robust visual–semantic embedding for classification tasks. The proposed method demonstrates significant improvements in performance metrics in benchmark datasets, showcasing its potential in advancing GZSL applications.

A constructive approach to strategy game map generation

Designing strategy games poses unique challenges due to the complicated nature of the genre where balancing game elements is a major concern. Maps in strategy games commonly contain elements such as resources to gather and buildings with different capabilities, playing a crucial role in the game life cycle. Resource and base placements must be done carefully. It is difficult to design maps manually and even harder to design multiple variations. This study presents a method for procedurally creating strategy game maps. A strategy game was developed and used as a test-bed for the study. A constructive approach is followed in the main steps of the algorithm design and a novel procedural noise function is used together with a uniform stratified sampling method to create and distribute the resources. Then eligible base locations are detected and scored according to a fitness function that defines how favorable a location is. These scores are used to place bases appropriately. The algorithm is evaluated using simulations and a small-scale user study is conducted at the end. The proposed method is capable of creating playable, diverse, and fair maps and is fast enough to work at runtime.

Online Fast Assessment of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Proliferation Through Replication

Purpose. A virus, when it infects a host organism, multiplies its ribonucleic acid (RNA) to make it more harmful. We analyze if a view-based temporal template method, the motion history image (MHI) algorithm, may be of use to extract information on the progression of viral interactions over time. Methods. We employ an alternative online processing modality to comprehend and analyze the replication dynamics of severe acute respiratory syndrome coronavirus 2 (SARS CoV 2). The data are evaluated using the computation of a quantitative algorithm, the absolute value of differences (AVD), followed by the generation of temporal template map, the MHI of viral contact. Results. We show that the MHI identifies various stages to examine cultural characteristics and information on the progression of viral interactions over time. The results are more easily obtained compared to a visual inspection of the frames constituting the recordings. MHI also has the advantage of relying on a simple algorithm. The AVD index further provides deeper insights into the dynamics of the viral interaction over time, and its measurement suggests that SARS CoV-2 virus is becoming more widespread. This study shows that the replication process damages the host cell over time, in addition to causing SARS CoV-2 to proliferate. Conclusions. The MHI provides a framework for automated feature extraction from time series data. The combined approach results in decreased computational complexity and enables online assessment of the assay. Clinical and research studies can be conducted to evaluate MHI and AVD and their utility in aiding diagnostic analysis.

Targeting high-potential mineral prospects in the Ezzhiliga region, Moroccan central massif, using spectral data from the ASTER sensor

This research paper introduces a comprehensive methodology for assessing hydrothermal alteration zones and structural complexity in the Ezzhiliga region, situated in the Hercynian Central Massif of Morocco. The approach utilizes ASTER imagery as the primary data source. The main objectives were to identify and analyze these geological features and then create a potential map for mineral exploration by integrating fuzzy logic and fractal concentration area analysis. Band ratios (RBD) and principal component analysis (PCA) were employed to detect and map argillic, phyllic, propylitic, and iron oxide alteration zones. Additionally, structural lineaments were extracted from the PC1 imagery to understand the structural pattern of the survey area. The incorporation of hydrothermal alteration zones and structural lineaments was achieved through the application of a fuzzy logic model, resulting in the generation of a mineral favorability map. The fuzzy logic model was customized to combine hydrothermal alteration and lineaments density maps, effectively eliminating false spectral anomalies induced by various interference factors. This map was then analyzed using the fractal concentration-area (C-A) model, which separated the anomaly from the geological background and generated a final mineral potential map. Analysis of fractal concentration-area was employed to define thresholds with greater precision, enhancing the reliability of mineral prospectivity assessments. Furthermore, laboratory analyses were performed to verify the outcomes of the mineral potential map. The obtained results revealed a significant affinity with the field data and indicated that the highly prospective zones are perfectly limited in spatial extent and generally associated with the contact of the Zaër granitic pluton with the metamorphic host rock, except for the anomaly identified to the southeast of the study area, along a major NE-SW trending fault.

Recurrent and Concurrent Prediction of Longitudinal Progression of Stargardt Atrophy and Geographic Atrophy towards Comparative Performance on Optical Coherence Tomography as on Fundus Autofluorescence

Stargardt atrophy and geographic atrophy (GA) represent pivotal endpoints in FDA-approved clinical trials. Predicting atrophy progression is crucial for evaluating drug efficacy. Fundus autofluorescence (FAF), the standard 2D imaging modality in these trials, has limitations in patient comfort. In contrast, spectral-domain optical coherence tomography (SD-OCT), a 3D imaging modality, is more patient friendly but suffers from lower image quality. This study has two primary objectives: (1) develop an efficient predictive modeling for the generation of future FAF images and prediction of future Stargardt atrophic (as well as GA) regions and (2) develop an efficient predictive modeling with advanced 3D OCT features at ellipsoid zone (EZ) for the comparative performance in the generation of future enface EZ maps and prediction of future Stargardt atrophic regions on OCT as on FAF. To achieve these goals, we propose two deep neural networks (termed ReConNet and ReConNet-Ensemble) with recurrent learning units (long short-term memory, LSTM) integrating with a convolutional neural network (CNN) encoder–decoder architecture and concurrent learning units integrated by ensemble/multiple recurrent learning channels. The ReConNet, which incorporates LSTM connections with CNN, is developed for the first goal on longitudinal FAF. The ReConNet-Ensemble, which incorporates multiple recurrent learning channels based on enhanced EZ enface maps to capture higher-order inherent OCT EZ features, is developed for the second goal on longitudinal OCT. Using FAF images at months 0, 6, and 12 to predict atrophy at month 18, the ReConNet achieved mean (±standard deviation, SD) and median Dice coefficients of 0.895 (±0.086) and 0.922 for Stargardt atrophy and 0.864 (±0.113) and 0.893 for GA. Using SD-OCT images at months 0 and 6 to predict atrophy at month 12, the ReConNet-Ensemble achieved mean and median Dice coefficients of 0.882 (±0.101) and 0.906 for Stargardt atrophy. The prediction performance on OCT images is comparably good to that on FAF. These results underscore the potential of SD-OCT for efficient and practical assessment of atrophy progression in clinical trials and retina clinics, complementing or surpassing the widely used FAF imaging technique.