Mapping Framework Research Articles

Accelerated 2D radial Look-Locker T1 mapping using a deep learning-based rapid inversion recovery sampling technique.

Efficient abdominal coverage with T1-mapping methods currently available in the clinic is limited by the breath hold period (BHP) and the time needed for T1 recovery. This work develops a T1-mapping framework for efficient abdominal coverage based on rapid T1 recovery curve (T1RC) sampling, slice-selective inversion, optimized slice interleaving, and a convolutional neural network (CNN)-based T1 estimation. The effect of reducing the T1RC sampling was evaluated by comparing T1 estimates from T1RC ranging from 0.63 to 2.0s with reference T1 values obtained from T1RC =2.5-5s. Slice interleaving methodologies were evaluated by comparing the T1 variation in abdominal organs across slices. The repeatability of the proposed framework was demonstrated by performing acquisition on test subjects across imaging sessions. Analysis of in vivo data based on retrospectively shortening the T1RC showed that with the CNN framework, a T1RC =0.84 s yielded T1 estimates without significant changes in mean T1 (p> 0.05) or significant increase in T1 variability (p> 0.48) compared to the reference. Prospectively acquired data using T1RC =0.84 s, an optimized slice interleaving scheme, and the CNN framework enabled 21 slices in a 20 s BHP. Analyses across abdominal organs produced T1 values within 2% of the reference. Repeatability experiments yielded Pearson's correlation, repeatability coefficient, and coefficient of variation of 0.99, 2.5%, and 0.12%, respectively. The proposed T1 mapping framework provides full abdominal coverage within a single BHP.

Targeting attack activity-driven networks.

Real-world complex systems demonstrated temporal features, i.e., the network topology varies with time and should be described as temporal networks since the traditional static networks cannot accurately characterize. To describe the deliberate attack events in the temporal networks, we propose an activity-based targeted attack on the activity-driven network to investigate temporal networks' temporal percolation properties and resilience. Based on the node activity and network mapping framework, the giant component and temporal percolation threshold are solved according to percolation theory and generating function. The theoretical results coincide with the simulation results near the thresholds. We find that targeted attacks can affect the temporal network, while random attacks cannot. As the probability of a highly active node being deleted increases, the temporal percolation threshold increases, and the giant component increases, thus enhancing robustness. When the network's activity distribution is extremely heterogeneous, network robustness decreases consequently. These findings help us to analyze and understand real-world temporal networks.

Evaluating Flood Damage to Paddy Rice Fields Using PlanetScope and Sentinel-1 Data in North-Western Nigeria: Towards Potential Climate Adaptation Strategies

Floods are significant global disasters, but their impact in developing countries is greater due to the lower shock tolerance, many subsistence farmers, land fragmentation, poor adaptation strategies, and low technical capacity, which worsen food security and livelihoods. Therefore, accurate and timely monitoring of flooded crop areas is crucial for both disaster impact assessments and adaptation strategies. However, most existing methods for monitoring flooded crops using remote sensing focus solely on estimating the flood damage, neglecting the need for adaptation decisions. To address these issues, we have developed an approach to mapping flooded rice fields using Earth observation and machine learning. This approach integrates high-resolution multispectral satellite images with Sentinel-1 data. We have demonstrated the reliability and applicability of this approach by using a manually labelled dataset related to a devastating flood event in north-western Nigeria. Additionally, we have developed a land suitability model to evaluate potential areas for paddy rice cultivation. Our crop extent and land use/land cover classifications achieved an overall accuracy of between 93% and 95%, while our flood mapping achieved an overall accuracy of 99%. Our findings indicate that the flood event caused damage to almost 60% of the paddy rice fields. Based on the land suitability assessment, our results indicate that more land is suitable for cultivation during natural floods than is currently being used. We propose several recommendations as adaptation measures for stakeholders to improve livelihoods and mitigate flood disasters. This study highlights the importance of integrating multispectral and synthetic aperture radar (SAR) data for flood crop mapping using machine learning. Decision-makers will benefit from the flood crop mapping framework developed in this study in a number of spatial planning applications.

DEM-based pluvial flood inundation modeling at a metropolitan scale

The global increase in urban flooding presents a substantial challenge that affects communities across the globe. This study introduces a post-flood inundation modeling framework tailored to pluvial floods on a metropolitan scale. We employ a dual drainage modeling approach for enhanced accuracy. The framework comprises two primary components: a Storm Water Management Model (SWMM) for simulating water movement within the storm drain system, and an advanced DEM-based flood inundation module that leverages SWMM results to predict flood inundation areas. Compared to the conventional flood spreading models commonly used in dual drainage modeling, this module incorporates high-resolution DEM cells into the surface flow routing processes, allowing to capture intricate surface terrain complexities. Additionally, the proposed module considers direct rainfall impacts on pluvial flood inundation mapping, resulting in enhanced accuracy. To evaluate the proposed model's accuracy, a two-step validation approach has been implemented. This includes comparing the proposed model results with both the high water marks measured in the aftermath of Hurricane Harvey and the flood depth map generated by a hydrodynamic model. The findings affirm the effectiveness of our proposed framework for post-flood inundation mapping within metropolitan settings. This method provides a dependable approach to urban flood modeling and represents an advancement in addressing the challenges associated with urban flooding.

Abstract C152: Improving Access to Genetic Services for Women with Breast Cancer from Underserved Communities in California: Design and Implementation of a Community- Engaged Program

Abstract This study aims to understand the barriers to genetic services uptake at the patient, provider, and healthcare system levels, and to develop a multi-level intervention to increase access and suitability to genetic services for women from minority and medically underserved populations in California who have been recently diagnosed with breast cancer. Germline-informed cancer screening has been found to improve breast cancer survival and help identify close relatives who may have an inherited mutation that increases their susceptibility to cancer. However, only 26% of women diagnosed with breast cancer reported having germline genetic testing; marginalized populations are less likely to receive these services (Kurian et al., 2023). Using Levesque’s Conceptual Framework (Lévesque et al., 2013) and Implementation Mapping (Fernández et al., 2019), we are iteratively developing a multi-level program to explore the role that Community Health Workers (CHWs)/Promotoras can play in reducing access disparities to genetic services. Eleven CHWs/Promotoras have completed 80 hours of CHW training, 7 hours of mental health, insurance and group facilitation training, and 14 hours of cancer health training. CHWs/Promotoras have established local community breast cancer groups in their local communities across California’s Central Valley and are participating in and organizing breast cancer health fairs in their communities in partnership with Community Based Organizations (CBOs). Identifying and addressing the barriers to genetic services is critical to reducing access disparities to genetic services for underserved populations. CHWs/Promotoras are a promising resource to help reduce existing disparities in genetic services for women diagnosed with breast cancer. Citation Format: Iñigo Verduzco Gallo, Nanci Chavarria Villa, Angelica Rolon, Guadalupe Carvajal, Ana P. Estrada-Florez, Luis G Carvajal-Carmona. Improving Access to Genetic Services for Women with Breast Cancer from Underserved Communities in California: Design and Implementation of a Community- Engaged Program [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C152.

A fast and adaptive detection framework for genome-wide chromatin loop mapping from Hi-C data.

Chromatin loop identification plays an important role in molecular biology and 3D genomics research, as it constitutes a fundamental process in transcription and gene regulation. Such precise chromatin structures can be identified across genome-wide interaction matrices via Hi-C data analysis, which is essential for unraveling the intricacies of transcriptional regulation. Given the increasing number of genome-wide contact maps, derived from both in situ Hi-C and single-cell Hi-C experiments, there is a pressing need for efficient and resilient algorithms capable of processing data from diverse experiments rapidly and adaptively. Here, we propose YOLOOP, a novel detection-based framework that is different from the conventional paradigm. YOLOOP stands out for its speed, surpassing the performance of previous state-of-the-art (SOTA) chromatin loop detection methods. It achieves a 30-fold acceleration compared with classification-based methods, up to 20-fold acceleration compared with the SOTA kernel-based framework, and a fivefold acceleration compared with statistical algorithms. Furthermore, the proposed framework is capable of generalizing across various cell types, multiresolution Hi-C maps, and diverse experimental protocols. Compared with the existing paradigms, YOLOOP shows up to a 10% increase in recall and a 15% increase in F1-score, particularly noteworthy in the GM12878 cell line. YOLOOP also offers fast adaptability with straightforward fine-tuning, making it readily applicable to extremely sparse single-cell Hi-C contact maps. It maintains its exceptional speed, completing genome-wide detection at a 10 kb resolution for a single-cell contact map within 1 min and for a 900-cell-superimposed contact map within 3 min, enabling fast analysis of large-scale single-cell data.

A dual-stage coverage path planning method for bathymetric survey using an AUV in graph-based SLAM framework considering positioning uncertainty

Autonomous underwater vehicles (AUVs) are becoming increasingly popular for seabed coverage survey tasks but suffer from a serious problem of positioning error accumulation, which leads to low path tracking accuracy and a low coverage rate of the target area. A dual-stage AUV path planning (DSPP) approach was proposed within a simultaneous localization and mapping (SLAM) framework for coverage bathymetric surveys. In the first stage, a global coverage path is designed in a lawnmower style. Crossed paths are added to generate periodic loop closures in SLAM, which can improve the efficiency of online replanning. In the second stage, with a probabilistic neural network (PNN) used for feature-rich bathymetric sub-map identification, an entropy-based online path replanning approach was implemented using 20 templates to adjust the global path to produce accurate loop closures for positioning error correction. Two bathymetric datasets were used to test the proposed DSPP method using a self-developed experimental simulation system. Experiments verified that DSPP can significantly improve the positioning accuracy and coverage rate with acceptably longer paths. In a 6000 m× 5000 marea,the DSPP approach reduced the positioning error by 76% compared with the pure dead reckoning method, and the coverage ratio reached 98.15%.

Beeyond Foods: creating a sweet honey revolution

Learning outcomes After completion of the case study, the students will be able to[1] review the industry analysis using Porter’s five forces and strength, weakness, opportunities, threats framework and understand how a firm can achieve a competitive advantage, analyze the stakeholder theory and the salience of stakeholder mapping for enterprises with large number of stakeholders, apply the Mendelow framework of stakeholder mapping in this context and integrate it with stakeholder engagement for small enterprises and examine and evaluate how new age enterprises can engage better with stakeholders to provide a higher value creation. Case overview/synopsis Ayushi Srivastava started her enterprise, Beeyond Foods, in early 2021 with the aim to provide unadulterated, raw honey to consumers. She also wanted to help the beekeeping community by providing the beekeepers with a fair price for the procurement of honey from the bee hives. Beeyond Foods was a small enterprise in India that sold two variants of honey to consumers. The first variant was sourced from the Himalayan region of India, whereas the second was sourced from the Western Ghats of India. The primary distribution channel was trade fairs, where Srivastava would assemble her stall and sell honey to potential customers. Furthermore, a part of the sales was also driven by an electronic channel, which was through the company website. Customers could place their orders, and the honey would directly be delivered to their homes. With a successful start to the business, Srivastava was interested to scale her business and reach more customers. However, she was mindful that there were multiple stakeholders involved in the business. Srivastava had to study the values and needs of each stakeholder, while simultaneously formulate a strategy to expand her reach. This case study is designed to teach the concept of stakeholder value creation for small business enterprises. Complexity academic level This case study is well suited for an entrepreneurship and strategy course at the postgraduate (Master of Business Administration) level. This case study can also be taught in a marketing course. Supplementary materials Teaching notes are available for educators only. Subject code CSS3: Entrepreneurship.

Public Perception on Pharmaceutical Marketing Practices in India — An Exploratory Study

Pharmaceutical marketing practices are primarily designed to address the needs of healthcare practitioners in promoting good health. However, this focus has led to information asymmetry between the public (stakeholders) and the pharmaceutical industry, resulting in both positive and negative perceptions of pharmaceutical marketing practices among the Indian public. A Systematic review of literature (SLR), applying the TCCM (Theory, Characteristics, Context, Method) framework, bibliometric analysis, and science mapping using VOSviewer, was conducted to review four decades of existing literature (1980-2021) to identify knowledge gaps and areas for further research on public perception of pharmaceutical marketing practices in India. Using an inductive approach guided by conventional grounded theory and theoretical sampling, data collected from the public were analyzed using descriptive statistics to arrive at findings and conclusions. A public perception model, based on the Machiavellian concept, is proposed using PLS-SEM with a random sample from the collected primary data (survey). This study may help regulatory agencies and the pharmaceutical industry assess and remodel their marketing practices according to the prevailing perceptions of the public, the largest stakeholders in the pharmaceutical business.

A Method for Cropland Layer Extraction in Complex Scenes Integrating Edge Features and Semantic Segmentation

Cultivated land is crucial for food production and security. In complex environments like mountainous regions, the fragmented nature of the cultivated land complicates rapid and accurate information acquisition. Deep learning has become essential for extracting cultivated land but faces challenges such as edge detail loss and limited adaptability. This study introduces a novel approach that combines geographical zonal stratification with the temporal characteristics of medium-resolution remote sensing images for identifying cultivated land. The methodology involves geographically zoning and stratifying the study area, and then integrating semantic segmentation and edge detection to analyze remote sensing images and generate initial extraction results. These results are refined through post-processing with medium-resolution imagery classification to produce a detailed map of the cultivated land distribution. The method achieved an overall extraction accuracy of 95.07% in Tongnan District, with specific accuracies of 92.49% for flat cultivated land, 96.18% for terraced cultivated land, 93.80% for sloping cultivated land, and 78.83% for forest intercrop land. The results indicate that, compared to traditional methods, this approach is faster and more accurate, reducing both false positives and omissions. This paper presents a new methodological framework for large-scale cropland mapping in complex scenarios, offering valuable insights for subsequent cropland extraction in challenging environments.

Establishing the longitudinal hemodynamic mapping framework for wearable-driven coronary digital twins

Understanding the evolving nature of coronary hemodynamics is crucial for early disease detection and monitoring progression. We require digital twins that mimic a patient’s circulatory system by integrating continuous physiological data and computing hemodynamic patterns over months. Current models match clinical flow measurements but are limited to single heartbeats. To this end, we introduced the longitudinal hemodynamic mapping framework (LHMF), designed to tackle critical challenges: (1) computational intractability of explicit methods; (2) boundary conditions reflecting varying activity states; and (3) accessible computing resources for clinical translation. We show negligible error (0.0002–0.004%) between LHMF and explicit data of 750 heartbeats. We deployed LHMF across traditional and cloud-based platforms, demonstrating high-throughput simulations on heterogeneous systems. Additionally, we established LHMFC, where hemodynamically similar heartbeats are clustered to avoid redundant simulations, accurately reconstructing longitudinal hemodynamic maps (LHMs). This study captured 3D hemodynamics over 4.5 million heartbeats, paving the way for cardiovascular digital twins.

Joint-optimized coverage path planning framework for USV-assisted offshore bathymetric mapping: From theory to practice

Designing effective coverage routes for unmanned surface vehicles (USVs) is crucial to improve the efficiency of offshore bathymetric surveys. However, existing coverage planning methods for practical use are limited, primarily due to the large-scale surveying areas and intricate region geometries caused by coastal features. This study aims to address these challenges by introducing a coverage path planning framework for USV-assisted bathymetric mapping, specifically aimed at the joint optimization of paths to cover numerous complex regions. Initially, we conceptualize the large-scale bathymetric survey mission as an integer programming model. The model uses four distinct decision variables to meticulously formulate length calculations, inter-regional connections, entry and exit point selections, and line sweep direction. Then, a novel hierarchical algorithm is devised to solve the problem. The method first incorporates a bisection-based convex decomposition method to achieve optimal partitioning of complex regions. Additionally, a hierarchical heuristic optimization algorithm that seamlessly integrates the optimization of all influencing factors is designed, which includes order generation, candidate pattern finding, tour finding, and final optimization. The reliability of the framework is validated through semi-physical simulations and lake trials using a real USV. Through comparative studies, our model demonstrates clear advantages in computational efficiency and optimization capability compared to state-of-the-arts, with its superiority becoming more pronounced as the problem scale increases. The results from lake trials further affirm the efficient and reliable performance of our model in practical bathymetric survey tasks.

A novel ANN-CA and MCDA integrated framework for predicting urban expansion and its implications on future flood risk, Accra Metropolis

Urban development in African countries significantly impacts environmental sustainability and city resilience, particularly in flood risk management. The Accra Metropolis, in particular, faces increasingly prevalent annual floods that disproportionately affect the urban poor living in informal settlements, resulting in significant loss of life and disruption of livelihoods. This study developed a GIS-based flood risk mapping framework that integrates artificial neural network (ANN) and cellular automata (CA) modelling with multi-criteria decision analysis. This hybrid approach was employed to predict flood scenarios for the Metropolis in 2032 and 2042. The framework incorporated six hydro-geomorphological indicators influencing extreme events: LULC, slope, elevation, drainage density, soil type, and proximity to rivers. The study analysed LULC projections, revealing a trend of substantial urban expansion with an estimated 10.9% increase in built-up areas within the next 20 years. This growth is expected to significantly heighten future flood risk in both the central upstream and downstream portions of the Metropolis, particularly in low-lying informal settlements close to the Odaw River and Korle Lagoon. Model performance was validated by ROC curve analysis, with AUC values of 0.927 and 0.922 for 2032 and 2042, respectively, which resonates with the records of previous flood distribution studies of the area. This study highlights the crucial need for sustainable urban development, improved infrastructure, and proactive flood mitigation measures in Accra to assist urban planners, policymakers, and stakeholders in managing flood risks effectively.

Clinician perspectives and recommendations regarding design of clinical prediction models for deteriorating patients in acute care

BackgroundSuccessful deployment of clinical prediction models for clinical deterioration relates not only to predictive performance but to integration into the decision making process. Models may demonstrate good discrimination and calibration, but fail to match the needs of practising acute care clinicians who receive, interpret, and act upon model outputs or alerts. We sought to understand how prediction models for clinical deterioration, also known as early warning scores (EWS), influence the decision-making of clinicians who regularly use them and elicit their perspectives on model design to guide future deterioration model development and implementation.MethodsNurses and doctors who regularly receive or respond to EWS alerts in two digital metropolitan hospitals were interviewed for up to one hour between February 2022 and March 2023 using semi-structured formats. We grouped interview data into sub-themes and then into general themes using reflexive thematic analysis. Themes were then mapped to a model of clinical decision making using deductive framework mapping to develop a set of practical recommendations for future deterioration model development and deployment.ResultsFifteen nurses (n = 8) and doctors (n = 7) were interviewed for a mean duration of 42 min. Participants emphasised the importance of using predictive tools for supporting rather than supplanting critical thinking, avoiding over-protocolising care, incorporating important contextual information and focusing on how clinicians generate, test, and select diagnostic hypotheses when managing deteriorating patients. These themes were incorporated into a conceptual model which informed recommendations that clinical deterioration prediction models demonstrate transparency and interactivity, generate outputs tailored to the tasks and responsibilities of end-users, avoid priming clinicians with potential diagnoses before patients were physically assessed, and support the process of deciding upon subsequent management.ConclusionsPrediction models for deteriorating inpatients may be more impactful if they are designed in accordance with the decision-making processes of acute care clinicians. Models should produce actionable outputs that assist with, rather than supplant, critical thinking.

Remote sensing framework for geological mapping via stacked autoencoders and clustering

Supervised machine learning methods for geological mapping via remote sensing face limitations due to the scarcity of accurately labelled training data that can be addressed by unsupervised learning, such as dimensionality reduction and clustering. Dimensionality reduction methods have the potential to play a crucial role in improving the accuracy of geological maps. Although conventional dimensionality reduction methods may struggle with nonlinear data, unsupervised deep learning models such as autoencoders can model non-linear relationships. Stacked autoencoders feature multiple interconnected layers to capture hierarchical data representations useful for remote sensing data. We present an unsupervised machine learning-based framework for processing remote sensing data using stacked autoencoders for dimensionality reduction and k-means clustering for mapping geological units. We use Landsat 8, ASTER, and Sentinel-2 datasets to evaluate the framework for geological mapping of the Mutawintji region in Western New South Wales, Australia. We also compare stacked autoencoders with principal component analysis (PCA) and canonical autoencoders. Our results reveal that the framework produces accurate and interpretable geological maps, efficiently discriminating rock units. The results reveal that the combination of stacked autoencoders with Sentinel-2 data yields the best performance accuracy when compared to other combinations. We find that stacked autoencoders enable better extraction of complex and hierarchical representation of the input data when compared to canonical autoencoders and PCA. We also find that the generated maps align with prior geological knowledge of the study area while providing novel insights into geological structures.

RLI-SLAM: Fast Robust Ranging-LiDAR-Inertial Tightly-Coupled Localization and Mapping.

Simultaneous localization and mapping (SLAM) is an essential component for smart robot operations in unknown confined spaces such as indoors, tunnels and underground. This paper proposes a novel tightly-coupled ranging-LiDAR-inertial simultaneous localization and mapping framework, namely RLI-SLAM, which is designed to be high-accuracy, fast and robust in the long-term fast-motion scenario, and features two key innovations. The first one is tightly fusing the ultra-wideband (UWB) ranging and the inertial sensor to prevent the initial bias and long-term drift of the inertial sensor so that the point cloud distortion of the fast-moving LiDAR can be effectively compensated in real-time. This enables high-accuracy and robust state estimation in the long-term fast-motion scenario, even with a single ranging measurement. The second one is deploying an efficient loop closure detection module by using an incremental smoothing factor graph approach, which seamlessly integrates into the RLI-SLAM system, and enables high-precision mapping in a challenging environment. Extensive benchmark comparisons validate the superior accuracy of the proposed new state estimation and mapping framework over other state-of-the-art systems at a low computational complexity, even with a single ranging measurement and/or in a challenging environment.

Applying Experienced-Based Co-Design principles to improve digital health demand management processes in a large metropolitan multi-hospital health system

ObjectiveThis study describes the application of Experienced-Based Co-Design (EBCD) principles with an embedded implementation technology framework for improving digital health and informatics demand management processes in a multi-hospital healthcare system. The study identified barriers and enablers within the existing demand management system, proposed interventions to address these challenges and engaged in collaborative co-design with stakeholders. MethodsA multi-method qualitative approach aligning with EBCD principles was used. Framework mapping (Non-adoption, Abandonment, Scale-up, Spread, Sustainability) was applied to barriers and enablers associated with baseline practices. Reflexive thematic mapping and process charts were used to inform stakeholder priorities and co-design. Prototyping was iterative using feedback for continuous improvement with ongoing monitoring. ResultsFragmentation of information was a technological barrier, decentralized processes and service duplication were organizational barriers, and opportunities to improve clarity of governance policies were identified. Solutions were co-generated and prioritised by stakeholders. The co-design and prototyping phase followed an iterative approach which generated two interventions. For centralizing intake and management, a new workflow process was devised. For improving project portfolio visibility and developing a weighted scoring criterion, a single tool to track projects across the lifecycle and a scoring model based on prioritization techniques were created. ConclusionA novel application of an EBCD approach was used to improve demand management processes in a digital health and informatics service context in a large health system. It highlights the value and agility of EBCD to generate flexible and modular solutions for this digital health context and compares favorably to analogous approaches. Public interest summaryIn today's fast-paced digital age, it is vital for hospitals to manage health information innovation efficiently. This study used an Experienced-Based Co-Design design (EBCD) approach to improve how a multi-hospital health system handles and prioritizes digital health projects. We listened to stakeholders, identified challenges, including fragmented information and unclear processes, then co-created solutions. The EBCD produced an implementable streamlined method for managing digital health project requests, including prioritizing and tracking projects from start to finish. This outcome of the exemplar EBCD process is likely to ensure that the most critical health projects are prioritized. For the public, this means better, faster, and more efficient digital health services in the future.

Navigating Resilience: Developing The MAP Framework for Supporting Children Through Familial Adversity in Hong Kong

Navigating Resilience: Developing The MAP Framework for Supporting Children Through Familial Adversity in Hong Kong

Investigating palaeodune orientations and contemporary wind regimes in Southeast Kazakhstan using a semi‐automated mapping framework

Investigating palaeodune orientations and contemporary wind regimes in Southeast Kazakhstan using a semi‐automated mapping framework

BA-CLM: A Globally Consistent 3D LiDAR Mapping Based on Bundle Adjustment Cost Factors.

Constructing a globally consistent high-precision map is essential for the application of mobile robots. Existing optimization-based mapping methods typically constrain robot states in pose space during the graph optimization process, without directly optimizing the structure of the scene, thereby causing the map to be inconsistent. To address the above issues, this paper presents a three-dimensional (3D) LiDAR mapping framework (i.e., BA-CLM) based on LiDAR bundle adjustment (LBA) cost factors. We propose a multivariate LBA cost factor, which is built from a multi-resolution voxel map, to uniformly constrain the robot poses within a submap. The framework proposed in this paper applies the LBA cost factors for both local and global map optimization. Experimental results on several public 3D LiDAR datasets and a self-collected 32-line LiDAR dataset demonstrate that the proposed method achieves accurate trajectory estimation and consistent mapping.