Mapping Approach Research Articles

Improved Population Mapping for China Using the 3D Building, Nighttime Light, Points-of-Interest, and Land Use/Cover Data within a Multiscale Geographically Weighted Regression Model

Large-scale gridded population product datasets have become crucial sources of information for sustainable development initiatives. However, mainstream modeling approaches (e.g., dasymetric mapping based on Multiple Linear Regression or Random Forest Regression) do not consider the heterogeneity and multiscale characteristics of the spatial relationships between influencing factors and populations, which may seriously degrade the accuracy of the prediction results in some areas. This issue may be even more severe in large-scale gridded population products. Furthermore, the lack of detailed 3D human settlement data likewise poses a significant challenge to the accuracy of these data products. The emergence of the unprecedented Global Human Settlement Layer (GHSL) data package offers a possible solution to this long-standing challenge. Therefore, this study proposes a new Gridded Population Mapping (GPM) method that utilizes the Multiscale Geographically Weighted Regression (MGWR) model in conjunction with GHSL-3D Building, POI, nighttime light, and land use/cover datasets to disaggregate population data for third-level administrative units (districts and counties) in mainland China into 100 m grid cells. Compared to the WorldPop product, the new population map reduces the mean absolute error at the fourth-level administrative units (townships and streets) by 35%, 51%, and 13% in three test regions. The proposed mapping approach is poised to become a crucial reference for generating next-generation global demographic maps.

From Many-Body Ab Initio to Effective Excitonic Models: A Versatile Mapping Approach Including Environmental Embedding Effects.

We present an original multistate projective diabatization scheme based on Green's function formalisms that allows the systematic mapping of many-body ab initio calculations onto effective excitonic models. This method inherits the ability of the Bethe-Salpeter equation to describe Frenkel molecular excitons and intermolecular charge-transfer states equally well, as well as the possibility for an effective description of environmental effects in a QM/MM framework. The latter is found to be a crucial element in order to obtain accurate model parameters for condensed phases and to ensure their transferability to excitonic models for extended systems. The method is presented through a series of examples illustrating its quality, robustness, and internal consistency.

Destigmatizing Palliative Care among Young Adults-A Theoretical Intervention Mapping Approach.

Background: In medicine, stigmatization pertains to both afflicted individuals and diseases themselves but can also encompass entire medical fields. In regard to demographic change and the rising prevalence of oncological diseases, palliative care will become increasingly important. However, palliative care faces multiple stigmas. These include equating of palliative care with death and dying. A timely integration of palliative care would have the potential to alleviate symptom burden, diminish the risk of overtreatment, and thus save healthcare-related costs. Several interventions have been developed to destigmatize palliative care. However, they have mainly focused on the general public. Aim: The aim of this work is to develop a theoretical framework for an interventional campaign targeted at young adults to systematically destigmatize palliative care. Methods: The basis for the development of the campaign is a systematic review conducted by our working group that assessed the perception and knowledge of palliative care of young adults aged 18 to 24 years. To design a possible intervention, the Intervention Mapping approach was used. Results: The target group of young adults can be effectively reached in secondary schools, vocational schools, and universities. The target population should be able to discuss the content of palliative care and openly talk about death and dying. At the environmental level, palliative care should be more present in public spaces, and death and dying should be freed from taboos. Within an intervention with palliative care experts and patients serving as interventionists, these changes can be achieved by incorporating evidence-based methods of behavioral change. Conclusions: An early engagement with palliative care could contribute to the long-term reduction of stigmas and address the demographic shift effectively. A multimodal intervention approach comprising knowledge dissemination, exchange, and media presence provides an appropriate framework to counter the existing stigmatization of palliative care within the peer group of young adults.

A novel approach for population-receptive field mapping using high-performance computing

A novel approach for population-receptive field mapping using high-performance computing

A comparative evaluation of statistical and machine learning approaches for debris flow susceptibility zonation mapping in the Indian Himalayas

A comparative evaluation of statistical and machine learning approaches for debris flow susceptibility zonation mapping in the Indian Himalayas

Cross-domain recommender system with embedding- and mapping-based knowledge correlation

A knowledge transfer-based cross-domain recommender system is currently a research hotspot. Existing research has reached a high level of maturity in mining potential knowledge and establishing transfer mechanisms. However, most of them ignore the impact of the dissimilarity of potential knowledge on the transfer performance. Herein, a cross-domain recommender system based on knowledge correlation-induced the embedding and mapping approach is proposed, denoted by KCEM-CDRS. First, we propose a knowledge correlation measure, which captures the consistency of knowledge between the target and source domains to build the bridge for knowledge transfer. Second, we construct a joint matrix triple factorization model to solve the data sparsity in the target domain while introducing graph regularization to solve the problem of negative knowledge transfer. Results of extensive experiments on real Amazon metadata indicate that compared with three existing cross-domain recommendation methods, KCEM-CDRS shows performance improvements of 0.05–9.55 % and 0.02–2.63 % on mean absolute error and root mean square error, respectively. Additionally, the results of the ablation experiments indicate that consideration of the knowledge correlation between domains is beneficial for knowledge transfer when the density of the source domain is rich.

Subfield-level crop yield mapping without ground truth data: A scale transfer framework

Ongoing advances in satellite remote sensing data and machine learning methods have enabled crop yield estimation at various spatial and temporal resolutions. While yield mapping at broader scales (e.g., state or county level) has become common, mapping at finer scales (e.g., field or subfield) has been limited by the lack of ground truth data for model training and evaluation. Here we present a scale transfer framework, named Quantile loss Domain Adversarial Neural Networks (QDANN), that leverages knowledge from county-level datasets to map crop yields at the subfield level. Based on the strategy of unsupervised domain adaptation, QDANN is trained on labeled county-level data and unlabeled subfield-level data, with no requirement for yield information at the subfield level. We evaluate the proposed method applied to Landsat imagery and Gridmet weather data for maize, soybean, and winter wheat fields in the United States, using as reference data yield monitor records from roughly one million field-year observations. The model is compared with several process-based and machine learning-based benchmark approaches that train on simulated yield records or county-level data. QDANN-estimated yields achieved an R2 score (RMSE) of 48 % (2.29 t/ha), 32 % (0.85 t/ha), and 39 % (1.40 t/ha) for maize, soybean, and winter wheat in comparison with the ground-based yield measures, respectively. These performances are higher than benchmark approaches and are nearly as good as models trained on field-level data. When aggregated to the county level, the improvement achieved by QDANN is more pronounced and the R2 scores (RMSE) improved to 78 % (0.98 t/ha), 62 % (0.37 t/ha), and 53 % (1.00 t/ha) for maize, soybean, and winter wheat, respectively. This study demonstrates that the proposed scale transfer framework can serve as a reliable approach for yield mapping at the subfield level when there is no access to fine-scale yield information. Based on the QDANN model, we have generated and made publicly available 30-m annual yield maps for major crop-producing states in the U.S. since 2008.

Hindcasting and updating Landsat-based forest structure mapping across years to support forest management and planning

Forest vegetation mapping that integrates forest inventory data with multispectral remote sensing data provides valuable geospatial products for public land management agencies, but resource managers may require rapid updating of maps as new imagery becomes available (updating) or retrospective mapping for times prior to forest inventory plot measurement (hindcasting). While forest attribute mapping using Landsat multispectral imagery is common, the accuracy of applying models outside of reference epoch to support long-term forest monitoring is not normally quantified. We examine whether a Landsat-based mapping approach can support robust, temporally consistent multivariate mapping of forest structure and composition data in support of forest management planning and landscape analysis. Specifically, we ask: how accurate forest attribute mapping was when hindcasting or updating outside of a period of time when forest inventory plot data were available (reference epoch)? In the western Cascade Mountains of Oregon and California, USA, we used the gradient nearest neighbor approach to annually impute USDA Forest Inventory and Analysis (FIA) plot data (2001–2016) to all 30-m forested pixels based on temporally smoothed Landsat multispectral imagery (1986–2021), including basal area, canopy cover, quadratic mean diameter of dominant trees, stand height, and the density of large diameter trees. We made extrapolations from models fit to a 10-year reference epoch to both earlier periods (2001–2006 hindcast) and to later period (2011–2016 update) and quantified prediction accuracies relative to models based on the full data (2001–2016). To evaluate the influence of spatial scale on hindcasting and updating, we compared full and extrapolation model predictions at pixel-level (0.09 ha) and hexagon-level (780 ha).At the plot-level, we found no strong differences between the full and extrapolation model predictions for R2 and mean error nor among predicted vs. observed regression coefficients. At the pixel-level, average differences due to hindcasting and updating were near zero, though differences varied up to 20 % across pixels. At the hexagon-level, the range in map differences was small (+/- 5 %), but hindcasting resulted in lesser forest attribute predictions. We observed greater variability in pixel-level and hexagon-level prediction differences when hindcasting or updating was temporally further away from the reference period. Using 2001 hindcast and 2016 updated maps as a case study, we found that with hindcasting and updating map differences were spatially aggregated across the study region. Our results support Landsat-based hindcasting and updating of forest attribute mapping beyond the time period covered by forest plot data. Our results suggest aggregating data to coarse spatial resolutions may minimize differences due to hindcasting and updating. Further research is needed to identify the key drivers for prediction differences to improve the accuracy of both hindcasting and updating as a basis for forest monitoring.

ReSG: A Data Structure for Verification of Majority-based In-memory Computing on ReRAM Crossbars

Recent advancements in the fabrication of Resistive Random Access Memory (ReRAM) devices have led to the development of large-scale crossbar structures. In-memory computing architectures relying on ReRAM crossbars aim to mitigate the processor-memory bottleneck that exists with current complementary metal-oxide semiconductor technology. With this motivation, several synthesis and mapping approaches focusing on the realizations of Boolean functions in the ReRAM crossbars have been proposed earlier. Thus far, the verification of the designs realized on ReRAM crossbars is done either through manual inspection or using simulation-based approaches. Since manual inspections and simulation-based approaches are limited to smaller designs, they cannot be applied to the verification of complex designs on large-scale ReRAM crossbars. Motivated by this, we propose, for the first time, an automatic equivalence checking flow that determines the equivalence between the original function specification (e.g., Majority-inverter Graph ) and the crossbar micro-operations file formats. We consider two crossbar structures, zero-transistor, one-memristor (0T1R) and one-transistor, one-memristor (1T1R) to implement the micro-operations. While the micro-operations file format exists for 0T1R crossbar structures, no representations for micro-operations to be executed in 1T1R crossbars exist yet. In this work, we introduce the micro-operation file format for 1T1R crossbar structures to efficiently represent the micro-operations as ReRAM crossbar netlists. Afterwards, we introduce two intermediate data structures, ReRAM Sequence Graph for 0T1R crossbars (ReSG-0T1R) and for 1T1R crossbars (ReSG-1T1R) , that are derived from the 0T1R and 1T1R crossbar micro-operations file formats, respectively. These ReSGs are then translated into Boolean Satisfiability (SAT) formula, and then the verification is done by checking the generated SAT formulae against the golden functional specification (represented in Verilog) using Z3 Satisfiability solver. Experimental evaluations confirm the effectiveness of the proposed verification methodology on MCNC and ISCAS benchmarks.

Interactive Customer Journey Maps for Complex Services

Customer journey maps often assume linear and uniform customer experiences, limiting their effectiveness for complex services. In this article, an interactive customer journey mapping approach is proposed and evaluated in the context of a smart energy sharing platform project.

Backward induction-based deep image search.

Conditional image retrieval (CIR), which involves retrieving images by a query image along with user-specified conditions, is essential in computer vision research for efficient image search and automated image analysis. The existing approaches, such as composed image retrieval (CoIR) methods, have been actively studied. However, these methods face challenges as they require either a triplet dataset or richly annotated image-text pairs, which are expensive to obtain. In this work, we demonstrate that CIR at the image-level concept can be achieved using an inverse mapping approach that explores the model's inductive knowledge. Our proposed CIR method, called Backward Search, updates the query embedding to conform to the condition. Specifically, the embedding of the query image is updated by predicting the probability of the label and minimizing the difference from the condition label. This enables CIR with image-level concepts while preserving the context of the query. In this paper, we introduce the Backward Search method that enables single and multi-conditional image retrieval. Moreover, we efficiently reduce the computation time by distilling the knowledge. We conduct experiments using the WikiArt, aPY, and CUB benchmark datasets. The proposed method achieves an average mAP@10 of 0.541 on the datasets, demonstrating a marked improvement compared to the CoIR methods in our comparative experiments. Furthermore, by employing knowledge distillation with the Backward Search model as the teacher, the student model achieves a significant reduction in computation time, up to 160 times faster with only a slight decrease in performance. The implementation of our method is available at the following URL: https://github.com/dhlee-work/BackwardSearch.

Semantic Environment Atlas for Object-Goal Navigation

In this paper, we introduce the Semantic Environment Atlas (SEA), a novel mapping approach designed to enhance visual navigation capabilities of embodied agents. The SEA utilizes semantic graph maps that intricately delineate the relationships between places and objects, thereby enriching the navigational context. These maps are constructed from image observations and capture visual landmarks as sparsely encoded nodes within the environment. The SEA integrates multiple semantic maps from various environments, retaining a memory of place-object relationships, which proves invaluable for tasks such as visual localization and navigation. We developed navigation frameworks that effectively leverage the SEA, and we evaluated these frameworks through visual localization and object-goal navigation tasks. Our SEA-based localization framework significantly outperforms existing methods, accurately identifying locations from single query images. Experimental results in Habitat Savva et al. (2019)scenarios show that our method not only achieves a success rate of 39.0%—an improvement of 12.4% over the current state-of-the-art—but also maintains robustness under noisy odometry and actuation conditions, all while keeping computational costs low.

Remote and Proximal Sensors Data Fusion: Digital Twins in Irrigation Management Zoning.

The scientific field of precision agriculture employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impact. However, obtaining a high number of soil samples is challenging in order to make precision agriculture viable. There is a trade-off between the amount of data needed and the time and resources spent to obtain these data compared to the accuracy of the maps produced with more or fewer points. In the present study, the research was based on an exhaustive dataset of apparent electrical conductivity (aEC) containing 3906 points distributed along 26 transects with spacing between each of up to 40 m, measured by the proximal soil sensor EM38-MK2, for a grain-producing area of 72 ha in São Paulo, Brazil. A second sparse dataset was simulated, showing only four transects with a 400 m distance and, in the end, only 162 aEC points. The aEC map via ordinary kriging (OK) from the grid with 26 transects was considered the reference, and two other mapping approaches were used to map aEC via sparse grid: kriging with external drift (KED) and geographically weighted regression (GWR). These last two methods allow the increment of auxiliary variables, such as those obtained by remote sensors that present spatial resolution compatible with the pivot scale, such as data from the Landsat-8, Aster, and Sentinel-2 satellites, as well as ten terrain covariates derived from the Alos Palsar digital elevation model. The KED method, when used with the sparse dataset, showed a relatively good fit to the aEC data (R2 = 0.78), with moderate prediction accuracy (MAE = 1.26, RMSE = 1.62) and reasonable predictability (RPD = 1.76), outperforming the GWR method, which had the weakest performance (R2 = 0.57, MAE = 1.78, RMSE = 2.30, RPD = 0.81). The reference aEC map using the exhaustive dataset and OK showed the highest accuracy with an R2 of 0.97, no systematic bias (ME = 0), and excellent precision (RMSE = 0.56, RPD = 5.86). Management zones (MZs) derived from these maps were validated using soil texture data from clay samples measured at 0-10 cm depth in a grid of 72 points. The KED method demonstrated the highest potential for accurately defining MZs for irrigation, producing a map that closely resembled the reference MZ map, thereby providing reliable guidance for irrigation management.

Satellitome analysis on the pale-breasted thrush Turdus leucomelas (Passeriformes; Turdidae) uncovers the putative co-evolution of sex chromosomes and satellite DNAs

Do all birds' sex chromosomes follow the same canonical one-way direction of evolution? We combined cytogenetic and genomic approaches to analyze the process of the W chromosomal differentiation in two selected Passeriform species, named the Pale-breasted Thrush Turdus leucomelas and the Rufous-bellied thrush T. rufiventris. We characterized the full catalog of satellite DNAs (satellitome) of T. leucomelas, and the 10 TleSatDNA classes obtained together with 16 microsatellite motifs were in situ mapped in both species. Additionally, using Comparative Genomic Hybridization (CGH) assays, we investigated their intragenomic variations. The W chromosomes of both species did not accumulate higher amounts of both heterochromatin and repetitive sequences. However, while T. leucomelas showed a heterochromatin-poor W chromosome with a very complex evolutionary history, T. rufiventris showed a small and partially heterochromatic W chromosome that represents a differentiated version of its original autosomal complement (Z chromosome). The combined approach of CGH and sequential satDNA mapping suggest the occurrence of a former W-autosomal translocation event in T. leucomelas, which had an impact on the W chromosome in terms of sequence gains and losses. At the same time, an autosome, which is present in both males and females in a polymorphic state, lost sequences and integrated previously W-specific ones. This putative W-autosomal translocation, however, did not result in the emergence of a multiple-sex chromosome system. Instead, the generation of a neo-W chromosome suggests an unexpected evolutionary trajectory that deviates from the standard canonical model of sex chromosome evolution.

Designing a resilience-based intervention program for children with cancer and their families: a study protocol.

Advances in pediatric oncology have significantly increased survival rates, yet have introduced challenges in managing long-term treatment side effects. This study process introduces an interdisciplinary clinical intervention program rooted in the family resilience framework, aimed at improving well-being across the cancer trajectory for children and their families, especially those in Canadian communities far from specialized oncology centers with limited access to resources. Employing an intervention mapping approach, this program collaboratively involves patients, families, professionals, and researchers. It aims to identify vulnerability factors, establish a logic model of change, and devise comprehensive strategies that include professional interventions alongside self-management tools. These strategies, tailored to address biopsychosocial and spiritual challenges, are adapted to the unique contexts of communities distant from specialized cancer treatment centers. A mixed-methods approach will evaluate program effectiveness. Anticipated outcomes include the empowerment of families with self-management tools and professional support, designed to mitigate biopsychosocial and spiritual complications. By addressing the specific needs and limitations of these communities, the program strives to improve the overall health and well-being of both undergoing treatment and survivorship phases. By focusing on comprehensive care that includes both professional interventions and self-management, this initiative marks a significant shift toward a holistic, family-centered approach in pediatric oncology care for remote communities. It underlines the necessity of accessible interventions that confront immediate and long-term challenges, aiming to elevate the standard of care by emphasizing resilience, professional support, and family empowerment in underserved areas.

Spatial chemistry of citrus reveals molecules bactericidal to Candidatus Liberibacter asiaticus

Huanglongbing (HLB), associated with the psyllid-vectored phloem-limited bacterium, Candidatus Liberibacter asiaticus (CLas), is a disease threat to all citrus production worldwide. Currently, there are no sustainable curative or prophylactic treatments available. In this study, we utilized mass spectrometry (MS)-based metabolomics in combination with 3D molecular mapping to visualize complex chemistries within plant tissues to explore how these chemistries change in vivo in HLB-infected trees. We demonstrate how spatial information from molecular maps of branches and single leaves yields insight into the biology not accessible otherwise. In particular, we found evidence that flavonoid biosynthesis is disrupted in HLB-infected trees, and an increase in the polyamine, feruloylputrescine, is highly correlated with an increase in disease severity. Based on mechanistic details revealed by these molecular maps, followed by metabolic modeling, we formulated and tested the hypothesis that CLas infection either directly or indirectly converts the precursor compound, ferulic acid, to feruloylputrescine to suppress the antimicrobial effects of ferulic acid and biosynthetically downstream flavonoids. Using in vitro bioassays, we demonstrated that ferulic acid and bioflavonoids are indeed highly bactericidal to CLas, with the activity on par with a reference antibiotic, oxytetracycline, recently approved for HLB management. We propose these compounds should be evaluated as therapeutics alternatives to the antibiotics for HLB treatment. Overall, the utilized 3D metabolic mapping approach provides a promising methodological framework to identify pathogen-specific inhibitory compounds in planta for potential prophylactic or therapeutic applications.

Applicability of energy-theta mapping in resonant soft X-ray reflectometry to probe depth dependence of oxidation state and crystallographic environment in iron oxide multilayers

In the present paper we discuss applicability of the synchrotron method of resonant x-ray reflectometry to non-destructive depth profiling of multilayer heterostructures with low optical contrast between the sublayers. The two-dimensional mapping approach comprising evaluation and measurement of reflectance as a function of photon energy and grazing angle is shown to provide a convenient way to effectively utilize the enhancement of optical contrast between the sublayers that exists at the absorption edges of chemical elements due to particular spectral shape peculiarities related to oxidation state, crystallographic environment and magnetization. In the present study the evaluation of the resonant X-ray reflectivity maps is performed using a specially developed modeling and fitting software. Estimation of the photon energy / grazing angle combinations at which the reflectance is most sensitive to the minor changes in the physical properties of the sublayers is illustrated by the example of ferroic-on-semiconductor nanoscale heterostructures composed of nanoscale film of metallic iron oxidized from either top or bottom side. The subtle differences in resonant soft x-ray reflectance caused by variation of the oxide film thickness, oxidation state and crystallographic environment are discussed. The presented approach is applicable to a large class of heterostructures composed of sublayers that can be distinguished only by the differences in their near edge X-ray absorption fine structure.

Spatial multi-criteria approaches for estimating geogenic radon hazard index

The geogenic radon hazard index (GRHI) map plays a crucial role in evaluating radon exposure risks. The construction of this map requires a comprehensive analysis of radon levels in soil gas and some critical factors, such as uranium content in bedrock, soil permeability, and geological inhomogeneities. In this context, the spatial multi-criteria decision analysis is proposed to integrate the GRHI-based criteria for identifying the high-potential radon areas. In particular, the multivariate integration involves the fuzzy gamma operator and a hybrid multi-criteria decision-making technique, namely AHP-TOPSIS, which represents a novel approach in GRHI mapping. Thus, a comparison is provided through the definition of the GRHI map of an unexplored study area, that is the Apulia region, located in Southern Italy. In order to evaluate the output maps, high radon potential areas are identified based on some available indoor radon measurement data. The success-rate curve, as a valid evaluation metric, is employed for the performance assessment and comparison of these two methods. The results demonstrate that although both generated GRHI maps are closely correlated with high-potential radon zones in Apulia, the hybrid AHP-TOPSIS method is preferable in identifying areas with elevated radon potential.

QRAGE-Simultaneous multiparametric quantitative MRI of water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength.

To introduce quantitative rapid gradient-echo (QRAGE), a novel approach for the simultaneous mapping of multiple quantitative MRI parameters, including water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength. QRAGE leverages a newly developed multi-echo MPnRAGE sequence, facilitating the acquisition of 171 distinct contrast images across a range of inversion and TE points. To maintain a short acquisition time, we introduce MIRAGE2, a novel model-based reconstruction method that exploits prior knowledge of temporal signal evolution, represented as damped complex exponentials. MIRAGE2 minimizes local Block-Hankel and Casorati matrices. Parameter maps are derived from the reconstructed contrast images through postprocessing steps. We validate QRAGE through extensive simulations, phantom studies, and in vivo experiments, demonstrating its capability for high-precision imaging. In vivo brain measurements show the promising performance of QRAGE, with test-retest SDs and deviations from reference methods of < 0.8% for water content, < 17 ms for T1, and < 0.7 ms for T2*. QRAGE achieves whole-brain coverage at a 1-mm isotropic resolution in just 7 min and 15 s, comparable to the acquisition time of an MP2RAGE scan. In addition, QRAGE generates a contrast image akin to the UNI image produced by MP2RAGE. QRAGE is a new, successful approach for simultaneously mapping multiple MR parameters at ultrahigh field.

Unraveling mudstone compaction at microscale: A combined approach of nanoindentation mapping and machine learning data analysis

Compaction processes within mudstone samples across varying depths (723.5–3213.5 m) in the Vienna Basin were investigated, focusing on porosity changes and the resulting micromechanical response in the clay-rich, fine-grained fraction (“clay matrix”). A novel approach combining nanoindentation mapping with machine learning data analysis was developed to efficiently extract representative micromechanical parameters of the clay matrix. Emphasis was put on capturing the properties of the fine-grained, clay-dominated composite rather than individual mineral phases, enabling the analysis of compaction-induced strength changes at microscale. A significant enhancement in the mechanical strength of the clay matrix with increasing burial depth was observed. Reduced elastic modulus (Er) and hardness (H) increased from 6.8 ± 3.4 to 22.6 ± 7.5 GPa and from 0.2 ± 0.2 to 0.9 ± 0.2 GPa, respectively, over the depth interval of 2490 m. Moreover, a strong correlation between depth and porosity and consequently micromechanical properties of the clay matrix exists. This highlights the substantial influence of intra-clay porosity on mechanical properties, which follow a general compaction trend with depth. Broad ion beam-scanning electron microscopy (BIB-SEM) analysis was used for textural investigations at micro-to nanoscale and indicated that the reduction in porosity predominantly resulted from mechanical compaction rather than mineral diagenesis, which would have been noticeable by signs of significant dissolution or cementation. The correlation coefficient matrix between multiscale porosity measurements and nanoindentation affirmed that the porosity loss was closely linked to the enhanced mechanical properties of the fine-grained composite clay matrix, while other compositional variables like total clay mineral content showed weak correlations. Empirical mathematical equations were derived to describe the mechanical properties as generalized functions of depth and porosity. These may be used as geomechanical input for future basin analysis and geoenergy applications (e.g., seal rock studies in a geological storage context). This study introduces a new approach to unravel compaction processes in fine-grained rocks at microscale, emphasizing the interplay between intra-clay porosity and micromechanical changes during proceeding burial.