High-resolution Mapping Research Articles

Robust algorithm for automatic surface-based outlier detection in MBES point clouds

Bathymetric multibeam echosounder systems (MBES) provide high-resolution mapping of underwater topography but are highly susceptible to errors due to harsh environmental conditions and the measurement process. Traditionally, manual post-processing is required to ensure data quality, a time-consuming, expensive, and subjective task. To address this issue, we propose a surface-based algorithm for pre-processing and cleaning MBES data that reduces manual intervention and improves consistency. A surface-based algorithm models the underwater topography as a surface instead of processing individual points. By assuming a continuous surface for underwater geometry, the algorithm easily identifies observations that deviate significantly from this model. The method combines a hierarchical B-spline surface with iterative robust estimation to automate data cleaning. Preliminary results on example datasets show a balanced outlier detection accuracy of 0.99, with manual processing time reduced from 2 days to just 30 min.

High-resolution bathymetry coupled with 3D models of hydrothermal vents from opportunistically-acquired imagery: Aurora vent field, Arctic Ocean

ABSTRACT Active venting at the Aurora hydrothermal field was first located in 2014. In July 2023, the AUV/ROV Nereid Under Ice (NUI) expanded the known size of the Aurora hydrothermal field, discovering 7 ‘black smokers’ together with associated lower-temperature flow. In this study, we present a new high-resolution bathymetric map acquired from NUI which has allowed us to identify morphological features previously undetectable from ship multibeam. All known active vents are aligned along a single 230 m-long ridge, parallel to the Gakkel Ridge strike direction and intersected by a scarp following the general trend of the Lena Trough. The new vents were measured at up to 17 m height from 3D models generated using structure-from-motion techniques applied to opportunistically acquired imaging data collected while in exploration mode. The extent of extinct sulfides present, together with the towering height of vents are consistent with a period of sustained high-temperature venting at Aurora.

Integrating thermal and optical models with legacy excavation data for spatial inventories

Researching at previously excavated archaeological sites comes with its own challenge set, which includes ground-truthing previously unearthed architectural features to correlate old and new trenches. This study presents a comprehensive examination technique to approach architectural features at mound-type archaeological settlements through the synergistic utilization of thermal and optical imagery obtained via remote sensing technologies. Employing an interdisciplinary approach involving various types of imagery (optical, thermal, and radar) embedded in archaeological data, this research aims to reevaluate legacy spatial data and enhance the understanding of subsurface archaeological features by exploiting the complementary advantages of both thermal and optical sensors.This study involves the acquisition, processing, and integration of Unmanned Aerial Vehicle (UAV)-based thermal and optical datasets to derive a complete representation of archaeological landscapes. Ground-truthing and validation exercises are incorporated to ensure the accuracy and reliability of the results. The results contribute to advancing the field of archaeological remote sensing and highlight the significance of integrating diverse sensing modalities for a comprehensive and nuanced interpretation of archaeological landscapes. The techniques exemplified with two mound-type case sites help to generate high-resolution mappings which provide tools to increase the accuracy of on-site legacy data and help digital protection of the sites by generating digital twins. This presents a less costly method than conventional photogrammetric techniques, making it feasible and advantageous for field archaeologists or researchers dealing with legacy spatial data.

Mapping fertility rates at national, sub-national, and local levels in Ethiopia between 2000 and 2019.

Fertility rates are key indicators of population health and demographic change, influencing economic development, healthcare planning, and social policies. Understanding subnational variation in fertility rate is important for effective geographical targeting and policy prioritization. This study aimed to identify geographic variation, trends, and determinants of fertility rates in Ethiopia over the past two decades. We estimated total fertility rates (TFRs) and age-specific fertility rates (ASFRs) using five nationally representative cross-sectional Demographic and Health Surveys collected in Ethiopia between 2000 and 2019. ASFRs represent the number of live births per 1,000 women aged 15 to 49 during the 3 years before each survey, while TFRs indicate the average number of children a woman would have by the end of her reproductive years, calculated as the sum of ASFRs over five-year intervals. We developed model-based geostatistics by incorporating demographic and healthcare access data with spatial random fields to produce high-resolution fertility rate maps. These results were then aggregated to produce fertility rate estimates at local, sub-national, and national levels in Ethiopia. The national TFR gradually declined from 4.8 live births in 2000 to 4.2 live births in 2019, but it is still above the replacement level of 2.1 children per woman. There were sub-national and local variations in TFR, ranging from 5.7 live births in Somalia and 5.3 Oromia regions to 2.7 live births in Addis Ababa and 3.6 live births Dire Dawa cities. Geographical areas with high TFR were mostly associated with a high proportion of Muslim women and low access to health facilities. Despite a decline in fertility rates among women of reproductive age over the past two decades, marked spatial variation persists at sub-national and local levels in Ethiopia, with demographic factors determining the spatial distribution and rate of decline, highlighting the need for tailored programs and strategies in high-fertility areas to increase access to family planning.

Family-Wide Photoproximity Profiling of Integrin Protein Social Networks in Cancer.

Integrin family transmembrane receptors mediate dynamic interactions between cells and their extracellular microenvironment. The heterogeneous interaction partners of integrins directly regulate cell adhesion, motility, proliferation, and intracellular signaling. Despite the recognized importance of protein-protein interactions and the formation of signaling hubs around integrins, the ability to detect and quantify these dynamic binding partners with high spatial and temporal resolution remains challenging. Here, we developed an integrin-family-directed quantitative photoproximity protein interaction (PhotoPPI) profiling method to detect and quantify native integrin-centered protein social networks on live cells and tissues without the need for genetic manipulation, antibodies, or non-physiologic cell culture conditions. We drafted quantitative maps of integrin-centered protein social networks, highlighting conserved and unique binding partners between different cell types and cellular microenvironments. Comparison of integrin social networks in cancer cell lines of diverse tissue of origin and disease state identified specific AND-gate binding partners involved cell migration, microenvironmental interactions and proliferation that serve as markers of tumor cell metastatic state. Finally, we identified unique combinations - or barcodes - of integrin-proximal proteins on the surface of pre- and post-metastatic triple negative breast cancer (TNBC) cells whose expression strongly correlate with both positive and negative disease progression and outcomes in TNBC patients. Taken together, these data provide the first family-wide high-resolution maps of native protein interactors on live cells and identify dynamic integrin-centered social networks as potential AND-gate markers of cell identity, microenvironmental context and disease state.

Non-destructive visualisation and in situ quantification of micronutrient mobilisation in germinating rice grains using synchrotron-based X-ray fluorescence microscopy

Maintaining micronutrient availability in germinating rice tissues is crucial for seedling vigour and agronomic performance. However, current methods for visualising micronutrient dynamics often require destructive sample preparation. Here we report that synchrotron-based X-ray fluorescence microscopy (XFM) could enable non-destructive, in situ visualisation and quantification of zinc (Zn) mobilisation in intact, germinating rice grains. Using XFM on whole grains of salt-tolerant Pokkali and salt-sensitive Nipponbare varieties, we generated high-resolution elemental maps revealing the spatiotemporal distribution of Zn and other micronutrients. Direct quantification by seed volume using element association window proved inaccurate due to tissue heterogeneity. However, normalisation by areal density showed Zn accumulation was highest in the embryo, followed by the aleurone layer and shoots. Zn was initially localised in the aleurone layer and embryo, with subsequent mobilisation to developing shoots and roots during germination. Calcium was predominantly detected in the hull, while potassium was enriched in growing roots and shoots. This non-destructive XFM method provides unprecedented insights into real-time micronutrient fluxes during rice germination, offering a powerful tool for studying nutrient dynamics in diverse contexts such as biofortification, stress responses, and varietal differences. Our findings contribute to understanding the mechanisms underlying micronutrient allocation during early seedling development, with potential applications in improving rice nutritional quality and seedling establishment.

High-resolution functional mapping of RAD51C by saturation genome editing

Pathogenic variants in RAD51C confer an elevated risk of breast and ovarian cancer, while individuals homozygous for specific RAD51C alleles may develop Fanconi anemia. Using saturation genome editing (SGE), we functionally assess 9,188 unique variants, including >99.5% of all possible coding sequence single-nucleotide alterations. By computing changes in variant abundance and Gaussian mixture modeling (GMM), we functionally classify 3,094 variants to be disruptive and use clinical truth sets to reveal an accuracy/concordance of variant classification >99.9%. Cell fitness was the primary assay readout allowing us to observe a phenomenon where specific missense variants exhibit distinct depletion kinetics potentially suggesting that they represent hypomorphic alleles. We further explored our exhaustive functional map, revealing critical residues on the RAD51C structure and resolving variants found in cancer-segregating kindred. Furthermore, through interrogation of UK Biobank and a large multi-center ovarian cancer cohort, we find significant associations between SGE-depleted variants and cancer diagnoses.

Internal deformation of the North Andean Sliver in Ecuador-southern Colombia observed by InSAR

Summary In the Northern Andes, partitioning of oblique subduction of the Nazca plate beneath the South American continent induces a northeastward motion of the North Andean Sliver. The strain resulting from this motion is absorbed by crustal faults, which have produced magnitude 7 + earthquakes historically in the Andean Cordillera of Ecuador and southern Colombia. In order to quantify the strain in that area, we derive a high-resolution surface velocity map using InSAR time-series processing. We analyzed 6 to 8 years of Sentinel-1 data and combined different satellite line-of-sight directions to produce a reliable velocity map in the East direction. We use interpolated GNSS data to express the velocity map with respect to Stable South America and remove the long-wavelength pattern due to the post-seismic deformation following the 2016 Mw 7.8 Pedernales earthquake. The InSAR velocity map finds high E-W shortening strain rates along N-S trending structures within the Western Cordillera and the Interandean valley, with little deformation taking place east of them. This result strengthens the previous proposition of a ∼350 km long Quito-Latacunga tectonic block, forming a restraining bend in the overall right-lateral strike-slip fault system accommodating the northeastward escape motion of the North Andean Sliver. However, the high spatial resolution provided by InSAR indicates that previously proposed boundaries for this block need to be revised. In particular, InSAR results highlight high strain rate (>300 nstrain/yr) along undescribed active structures, south and west of the proposed limits for the Quito-Latacunga block, respectively in Peltetec and Ibarra regions. Interestingly, the two areas with the largest strain rates spatially correlate with the proposed areas of large historical earthquakes. Modeling of the InSAR and GNSS velocities in these areas suggests shallow coupling and high slip rates on structures which, previously, were not identified as active. We also demonstrate a slow-down of the shallow aseismic slip on the Quito fault after the Pedernales earthquake, suggesting that stress changes following large megathrust events might trigger transient slip behaviors on crustal faults. The high-resolution strain map provided by this work provides a new basis for future tectonic models in the Ecuadorian and southern Colombian Andes, and will contribute to the seismic hazard assessment in this highly populated area of the Andes.

Molecular architecture of the assembly of Bacillus spore coat protein GerQ revealed by cryo-EM

Protein filaments are ubiquitous in nature and have diverse biological functions. Cryo-electron microscopy (cryo-EM) enables the determination of atomic structures, even from native samples, and is capable of identifying previously unknown filament species through high-resolution cryo-EM maps. In this study, we determine the structure of an unreported filament species from a cryo-EM dataset collected from Bacillus amyloiquefaciens biofilms. These filaments are composed of GerQ, a spore coat protein known to be involved in Bacillus spore germination. GerQ assembles into a structurally stable architecture consisting of rings containing nine subunits, which stacks to form filaments. Molecular dockings and model predictions suggest that this nine-subunit structure is suitable for binding CwlJ, a protein recruited by GerQ and essential for Ca2+-DPA induced spore germination. While the assembly state of GerQ within the spores and the direct interaction between GerQ and CwlJ have yet to be validated through further experiments, our findings provide valuable insights into the self-assembly of GerQ and enhance our understanding of its role in spore germination.

Spatiotemporal Mapping of the Oxytocin Receptor at Single-Cell Resolution in the Postnatally Developing Mouse Brain.

The oxytocin receptor (OXTR) has garnered increasing attention for its role in regulating both mature behaviors and brain development. It has been established that OXTR mediates a range of effects that are region-specific or period-specific. However, the current studies of OXTR expression patterns in mice only provide limited help due to limitations in resolution. Therefore, our objective was to generate a comprehensive, high-resolution spatiotemporal expression map of Oxtr mRNA across the entire developing mouse brain. We applied RNAscope in situ hybridization to investigate the spatiotemporal expression pattern of Oxtr in the brains of male mice at six distinct postnatal developmental stages (P7, P14, P21, P28, P42, P56). We provide detailed descriptions of Oxtr expression patterns in key brain regions, including the cortex, basal forebrain, hippocampus, and amygdaloid complex, with a focus on the precise localization of Oxtr+ cells and the variance of expression between different neurons. Furthermore, we identified some neuronal populations with high Oxtr expression levels that have been little studied, including glutamatergic neurons in the ventral dentate gyrus, Vgat+Oxtr+ cells in the basal forebrain, and GABAergic neurons in layers 4/5 of the cortex. Our study provides a novel perspective for understanding the distribution of Oxtr and encourages further investigations into its functions.

Enhancer-promoter hubs organize transcriptional networks promoting oncogenesis and drug resistance

Recent advances in high-resolution mapping of spatial interactions among regulatory elements support the existence of complex topological assemblies of enhancers and promoters known as enhancer-promoter hubs or cliques. Yet, organization principles of these multi-interacting enhancer-promoter hubs and their potential role in regulating gene expression in cancer remain unclear. Here, we systematically identify enhancer-promoter hubs in breast cancer, lymphoma, and leukemia. We find that highly interacting enhancer-promoter hubs form at key oncogenes and lineage-associated transcription factors potentially promoting oncogenesis of these diverse cancer types. Genomic and optical mapping of interactions among enhancer and promoter elements further show that topological alterations in hubs coincide with transcriptional changes underlying acquired resistance to targeted therapy in T cell leukemia and B cell lymphoma. Together, our findings suggest that enhancer-promoter hubs are dynamic and heterogeneous topological assemblies with the potential to control gene expression circuits promoting oncogenesis and drug resistance.

A panorama-based technique to estimate sky view factor and solar irradiance considering transmittance of tree canopies

Street-view-based techniques for assessing the sky view factor (SVF) and solar irradiance under trees are gaining attention as tools for evaluating trees as nature-based solutions to mitigate urban heat risks. Although these metrics significantly depend on the morphology of trees and resulting canopy transmittance, an existing approach, termed the Solid Canopy Method (SCM), assumes zero transmission and has not accounted for these variations. This paper advances the computation of both metrics, improving their accuracy and application — we developed the Transmissive Canopy Method (TCM), a panorama-based approach that integrates semantic segmentation and binarization to evaluate SVF and solar irradiance while accounting for transmittance of tree canopies. Using a study area on a university campus in Singapore, we collected data on solar irradiance and 360°imagery to validate our method. The results indicated improved accuracy with MAE, RMSE, and R2 values of 77.8 Wm−2, 105.0 Wm−2 and 0.90, respectively — significantly outperforming the SCM. We showcased two use cases of our method: (1) high-resolution mapping of SVF and solar irradiance in a field with trees, and (2) walking route optimization considering sunlight exposure. Our findings highlight the strong capability of our TCM to evaluate the effects of trees in mitigating urban heat more accurately than the existing method. Additionally, the TCM has potential applications in urban planning and management, enabling strategic tree planting prioritizing areas lacking sufficient shading and developing tools for optimizing walking routes to minimize sunlight exposure.

Reshaping landscape factorization through 3D landscape clustering for urban temperature studies

As urban populations grow and cities expand, the challenge of managing urban heat and its environmental impacts becomes increasingly critical. Traditional methods for analyzing urban temperature dynamics often fall short in precisely capturing the complexity of urban landscapes. This paper introduces the 3D Landscape Clustering (3LC) framework, a new tool designed to analyze urban temperature dynamics by factoring in landscape variables. It clusters landscapes into homogeneous groups using high-resolution 3D land cover maps. The 3LC adopts a clustering mechanism to enhance flexibility and objectivity in landscape categorization, thereby enhancing the depth and accuracy of urban climate studies and moving beyond traditional classification frameworks such as the Local Climate Zone (LCZ). Case studies demonstrate its capability to provide detailed insights into the relationships between urban landscape features and temperature variations. Additionally, the paper details how the framework can excel in multi-city analyses and outlines advanced analytical techniques. Promising research opportunities and limitations are identified. This research reshapes our approach to landscape categorization, advancing our understanding of the interactions between landscape and climate dynamics, and contributing to more sustainable, climate-resilient cities.

Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) quantifies acoustic tissue properties such as the speed-of-sound (SOS). Although full-waveform inversion (FWI) is an effective method for accurate SOS reconstruction, it can be computationally challenging for large-scale problems. Deep learning-based image-to-image learned reconstruction (IILR) methods can offer computationally efficient alternatives. This study investigates the impact of the chosen input modalities on IILR methods for high-resolution SOS reconstruction in USCT. The selected modalities are traveltime tomography (TT) and reflection tomography (RT), which produce a low-resolution SOS map and a reflectivity map, respectively. These modalities have been chosen for their lower computational cost relative to FWI and their capacity to provide complementary information: TT offers a direct SOS measure, while RT reveals tissue boundary information. Systematic analyses were facilitated by employing a virtual USCT imaging system with anatomically realistic numerical breast phantoms. Within this testbed, a supervised convolutional neural network (CNN) was trained to map dual-channel (TT and RT images) to a high-resolution SOS map. Single-input CNNs were trained separately using inputs from each modality alone (TT or RT) for comparison. The accuracy of the methods was systematically assessed using normalized root mean squared error (NRMSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). For tumor detection performance, receiver operating characteristic analysis was employed. The dual-channel IILR method was also tested on clinical human breast data. Ensemble average of the NRMSE, SSIM, and PSNR evaluated on this clinical dataset were 0.2355, 0.8845, and 28.33 dB, respectively.

High-resolution mapping of grassland canopy cover in China through the integration of extensive drone imagery and satellite data

Canopy cover is a crucial indicator for assessing grassland health and ecosystem services. However, achieving accurate high-resolution estimates of grassland canopy cover at a large spatial scale remains challenging due to the limited spatial coverage of field measurements and the scale mismatch between field measurements and satellite imagery. In this study, we addressed these challenges by proposing a regression-based approach to estimate large-scale grassland canopy cover, leveraging the integration of drone imagery and multisource remote sensing data. Specifically, over 90,000 10 × 10 m drone image tiles were collected at 1,255 sites across China. All drone image tiles were classified into grass and non-grass pixels to generate ground-truth canopy cover estimates. These estimates were then temporally aligned with satellite imagery-derived features to build a random forest regression model to map the grassland canopy cover distribution of China. Our results revealed that a single classification model can effectively distinguish between grass and non-grass pixels in drone images collected across diverse grassland types and large spatial scales, with multilayer perceptron demonstrating superior classification accuracy compared to Canopeo, support vector machine, random forest, and pyramid scene parsing network. The integration of extensive drone imagery successfully addressed the scale-mismatch issue between traditional ground measurements and satellite imagery, contributing significantly to enhancing mapping accuracy. The national canopy cover map of China generated for the year 2021 exhibited a spatial pattern of increasing canopy cover from northwest to southeast, with an average value of 56 % and a standard deviation of 26 %. Moreover, it demonstrated high accuracy, with a coefficient of determination of 0.89 and a root-mean-squared error of 12.38 %. The resulting high-resolution canopy cover map of China holds great potential in advancing our comprehension of grassland ecosystem processes and advocating for the sustainable management of grassland resources.

Detection of sex-specific glutamate changes in subregions of hippocampus in an early-stage Alzheimer's disease mouse model using GluCEST MRI.

Regional glucose hypometabolism resulting in glutamate loss has been shown as one of the characteristics of Alzheimer's disease (AD). Because the impact of AD varies between the sexes, we utilized glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) for high-resolution spatial mapping of cerebral glutamate and investigated subregional changes in a sex-specific manner. Eight-month-old male and female AD mice harboring mutant amyloid precursor protein (APPNL-F/NL-F: n = 36) and wild-type (WT: n = 39) mice underwent GluCEST MRI, followed by proton magnetic resonance spectroscopy (1H-MRS) in hippocampus and thalamus/hypothalamus using 9.4T preclinical MR scanner. GluCEST measurements revealed significant (p≤0.02) glutamate loss in the entorhinal cortex (% change ± standard error: 8.73±2.12%), hippocampus (11.29±2.41%), and hippocampal fimbriae (19.15±2.95%) of male AD mice. A similar loss of hippocampal glutamate in male AD mice (11.22±2.33%; p = 0.01) was also observed in 1H-MRS. GluCEST MRI detected glutamate reductions in the fimbria and entorhinal cortex of male AD mice, which was not reported previously. Resilience in female AD mice against these changes indicates an intact status of cerebral energy metabolism. Glutamate levels were monitored in different brain regions of early-stage Alzheimer's disease (AD) and wild-type male and female mice using glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI). Male AD mice exhibited significant glutamate loss in the hippocampus, entorhinal cortex, and the fimbriae of the hippocampus. Interestingly, female AD mice did not have any glutamate loss in any brain region and should be investigated further to find the probable cause. These findings demonstrate previously unreported sex-specific glutamate changes in hippocampal sub-regions using high-resolution GluCEST MRI.

MSphere of Influence: When a sequencer is more than a sequencer.

Blake Billmyre uses functional genomics to help understand the biology of fungal pathogens, with an emphasis on evolution of virulence relevant traits and drug resistance. In this mSphere of Influence article, he reflects on how two papers (Liachko et al., "High-resolution mapping, characterization, and optimization of autonomously replicating sequences in yeast," Genome Research, 2013, and Guo et al., "Integration profiling of gene function with dense maps of transposon integration," Genetics, 2013) impacted his research trajectory and goals. These articles show the power of creative use of sequencing as a tool to drive understanding of fundamental biology.

Ultra-high-resolution mapping of myelin and g-ratio in a panel of Mbp enhancer-edited mouse strains using microstructural MRI

Non-invasive myelin water fraction (MWF) and g-ratio mapping using microstructural MRI have the potential to offer critical insights into brain microstructure and our understanding of neuroplasticity and neuroinflammation. By leveraging a unique panel of variably hypomyelinating mouse strains, we validated a high-resolution, model-free image reconstruction method for whole-brain MWF mapping. Further, by employing a bipolar gradient echo MRI sequence, we achieved high spatial resolution and robust mapping of MWF and g-ratio across the whole mouse brain. Our regional white matter-tract specific analyses demonstrated a graded decrease in MWF in white matter tracts which correlated strongly with myelin basic protein gene (Mbp) mRNA levels. Using these measures, we derived the first sensitive calibrations between MWF and Mbp mRNA in the mouse. Minimal changes in axonal density supported our hypothesis that observed MWF alterations stem from hypomyelination. Overall, our work strongly emphasizes the potential of non-invasive, MRI-derived MWF and g-ratio modeling for both preclinical model validation and ultimately translation to humans.

Irregular array optimization for beamforming with a polar coordinate-based partition coding approach

Abstract An innovative irregular array configuration optimization method for enhancing beamforming is introduced. This study presents partition coding to optimize sensor positioning and quantity of a non-uniform concentric circular array. This novel approach transcends traditional techniques by integrating structural partitioning and performance optimization to quantify the array’s geometry-performance correlation. Sensor candidate positions are mapped in polar coordinates, with each configuration translated into a sensor position matrix form. A significant innovation lies in the adaptation of the partition coding genetic algorithm to enhance the encoding of candidate positions and to refine crossover and mutation operations, underpinned by an elite retention strategy for selecting the optimal array configuration. Both simulation and experimental results substantiate the method’s effectiveness, achieving high-resolution acoustic mapping with commendably low computational complexity.

Optimizing Solar Potential Analysis in Cuba: A Methodology for High-Resolution Regional Mapping

The development of solar energy at a regional scale necessitates a thorough understanding of available resources. Cuba, facing prolonged economic, environmental, and energy crises, urgently needs to enhance its sustainability through solar energy. Although solar resource mapping is widespread, Cuba lacks extensive field measurements, often relying on models that may not be ideally suited for large regions, like Matanzas province. Spanning over 12,000 km² with nearly 150 km between its northern and southern extremes, Matanzas presents challenges for high-resolution solar mapping. This study introduces a methodology that integrates various methods and databases to achieve the maximum resolution in the resulting solar map. This approach is designed for large areas, where conventional high-resolution models fall short. By optimizing calculation times and parameterizing the entire surface latitudinally, a high-resolution solar resource map for Matanzas has been developed. This map significantly enhances the understanding of solar resources in Cuba and enables the proposal of new methodologies for analyzing solar potential in similarly large regions.