Hybrid Mapping Research Articles

D3L-SLAM: A Comprehensive Hybrid Simultaneous Location and Mapping System with Deep Keypoint, Deep Depth, Deep Pose, and Line Detection

Robust localization and mapping are crucial for autonomous systems, but traditional handcrafted feature-based visual SLAM often struggles in challenging, textureless environments. Additionally, monocular SLAM lacks scale-aware depth perception, making accurate scene scale estimation difficult. To address these issues, we propose D3L-SLAM, a novel monocular SLAM system that integrates deep keypoints, deep depth estimates, deep pose priors, and a line detector. By leveraging deep keypoints, which are more resilient to lighting variations, our system improves the robustness of visual SLAM. We further enhance perception in low-texture areas by incorporating line features in the front-end and mitigate scale degradation with learned depth estimates. Additionally, point-line feature constraints optimize pose estimation and mapping through a tightly coupled point-line bundle adjustment (BA). The learned pose estimates refine the feature matching process during tracking, leading to more accurate localization and mapping. Experimental results on public and self-collected datasets show that D3L-SLAM significantly outperforms both traditional and learning-based visual SLAM methods in localization accuracy.

Stereo and LiDAR Loosely Coupled SLAM Constrained Ground Detection.

In many robotic applications, creating a map is crucial, and 3D maps provide a method for estimating the positions of other objects or obstacles. Most of the previous research processes 3D point clouds through projection-based or voxel-based models, but both approaches have certain limitations. This paper proposes a hybrid localization and mapping method using stereo vision and LiDAR. Unlike the traditional single-sensor systems, we construct a pose optimization model by matching ground information between LiDAR maps and visual images. We use stereo vision to extract ground information and fuse it with LiDAR tensor voting data to establish coplanarity constraints. Pose optimization is achieved through a graph-based optimization algorithm and a local window optimization method. The proposed method is evaluated using the KITTI dataset and compared against the ORB-SLAM3, F-LOAM, LOAM, and LeGO-LOAM methods. Additionally, we generate 3D point cloud maps for the corresponding sequences and high-definition point cloud maps of the streets in sequence 00. The experimental results demonstrate significant improvements in trajectory accuracy and robustness, enabling the construction of clear, dense 3D maps.

Effect of primary copper metabolism disturbance on elemental, protein, and lipid composition of the organs in Jackson toxic milk mouse.

Toxic milk (txJ) is an autosomal recessive mutation in the Atp7b gene in the C3H/HeJ strain, observed at The Jackson Laboratory in Maine, USA. TxJ mice exhibit symptoms similar to those of human Wilson's disease (WD). The study aimed to verify organ involvement in a mouse model of WD. TxJ mice and control animals were sacrificed at 2, 4, 8, and 14months of age. Total X-ray Fluorescence Spectroscopy (TXRF) was used to determine the elemental concentration in organs. Tissue chemical composition was measured by Fourier Transform Infrared Spectroscopy (FTIR). Additionally, hybrid mapping of FTIR and microXRF was performed. Elevated concentrations of Cu were observed in the liver, striatum, eye, heart, and duodenum of txJ mice across age groups. In the striatum of the oldest txJ mice, there was lower lipid content and a higher fraction of saturated fats. The secondary structure of striatum proteins was disturbed in txJ mice. In the livers of txJ mice, higher concentrations of saturated fats and disturbances in the secondary structure of proteins were observed. The concentration of neurofilaments was significantly higher in txJ serum. The distribution of Cu deposits in brains was uniform with no prevalence in any anatomic structure in either group, but significant protein structure changes were observed exclusively in the striatum of txJ. In this txJ animal model of WD, pathologic copper accumulation occurs in the duodenum, heart, and eye tissues. Increased copper concentration in the liver and brain results in increased saturated fat content and disturbances in secondary protein structure, leading to hepatic injury and neurodegeneration.

A new global hybrid map of annual herbaceous cropland at a 500 m resolution for the year 2019

Abstract The global spatial extent of croplands is a crucial input to global and regional agricultural monitoring and modeling systems. Although many new remotely-sensed products are now appearing due to recent advances in the spatial and temporal resolution of satellite sensors, there are still issues with these products that are related to the definition of cropland used and the accuracies of these maps, particularly when examined spatially. To address the needs of the agricultural monitoring community, here we have created a hybrid map of global cropland extent at a 500 m resolution by fusing two of the latest high resolution remotely-sensed cropland products: the European Space Agency’s WorldCereal and the cropland layer from the University of Maryland. We aggregated the two products to a common resolution of 500 m to produce percentage cropland and compared them spatially, calculating two kinds of disagreement: density disagreement, where the two maps differ by more than 80%, and absence-presence of cropland disagreement, where one map indicates the presence of cropland while the other does not. Based on these disagreements, we selected continuous areas of disagreement, referred to in the paper as hotspots of disagreement, for manual correction by experts using the Geo-Wiki land cover application. The hybrid map was then validated using a stratified random sample based on the disagreement layer, where the sample was visually interpreted by a different set of experts using Geo-Wiki. The results show that the hybrid product improves upon the overall accuracy statistics in the areas where the underlying cropland layer from the University of Maryland was improved with the WorldCereal product, but more importantly, it represents an improved spatially explicit cropland mask for early warning and food security assessment purposes.

Multi-omic characterization of allele-specific regulatory variation in hybrid pigs

Hybrid mapping is a powerful approach to efficiently identify and characterize genes regulated through mechanisms in cis. In this study, using reciprocal crosses of the phenotypically divergent Duroc and Lulai pig breeds, we perform a comprehensive multi-omic characterization of regulatory variation across the brain, liver, muscle, and placenta through four developmental stages. We produce one of the largest multi-omic datasets in pigs to date, including 16 whole genome sequenced individuals, as well as 48 whole genome bisulfite sequencing, 168 ATAC-Seq and 168 RNA-Seq samples. We develop a read count-based method to reliably assess allele-specific methylation, chromatin accessibility, and RNA expression. We show that tissue specificity was much stronger than developmental stage specificity in all of DNA methylation, chromatin accessibility, and gene expression. We identify 573 genes showing allele specific expression, including those influenced by parent-of-origin as well as allele genotype effects. We integrate methylation, chromatin accessibility, and gene expression data to show that allele specific expression can be explained in great part by allele specific methylation and/or chromatin accessibility. This study provides a comprehensive characterization of regulatory variation across multiple tissues and developmental stages in pigs.

Viscosity approximation method for attractive points of widely more generalized hybrid mappings

Viscosity approximation method for attractive points of widely more generalized hybrid mappings

USV Path Planning in a Hybrid Map Using a Genetic Algorithm with a Feedback Mechanism

Unmanned surface vehicles (USVs) often operate in real-world environments with long voyage distances and complex routes. The use of a single-grid map model presents challenges, such as the high computational costs for high-resolution maps and loss of environmental information for low-resolution maps. This article proposes an environmental modeling method using a hybrid map that combines topology units and grids. The approach involves calibrating key nodes based on the watershed skeleton line, constructing a topology map using these nodes, decomposing the original map into unit maps, converting each unit map into a grid map, and creating a hybrid map environment model that comprises topology maps, unit map sets, and grid map sets. Then, the article introduces an improved genetic algorithm, called Genetic Algorithm with Feedback (FGA), to address path planning in hybrid maps. Experimental results demonstrate that FGA has better computational efficiency than other algorithms in similar experimental environments. In hybrid maps, path planning with FGA reduces the path lengths and time consumption, and the paths are more logical, smooth, and continuous. These findings contribute to enhancing the quality of path planning and the practical value of USVs.

Revitalization of regular landscape compositions of the park-monument of landscape art «Sharivskyi»

Introduction. The perception of the landscape as a space for living has led to the formation and development of a special group of anthropogenic landscapes, namely garden and park landscapes, which are characterized by a wide range of functions from utilitarian to aesthetic and spiritual. Currently, approximately 30% of cultural heritage sites are in disrepair, and 80% require serious restoration work. Therefore, the revitalization of the landscape structure of historical garden and park landscapes is an urgent scientific problem and meets two sustainable development goals (goal 11 Sustainable cities and communities and goal 15 - Life on land). The purpose of article. To substantiate the concept of revitalization of regular landscape compositions of the park-monument of landscape gardening art of national importance "Sharivskyi" and to provide a graphic 3D model of architectural, planning and volumetric-spatial solutions of the proposed concept of revitalization of regular compositions of the studied landscape. Research methods. Method of historical analysis: with a focus on the period of the highest embodiment of the artistic compositional intent of the restored space, when the estate belonged to the successful sugar producer Leopold Koenig. The graphic presentation of architectural, planning, and three-dimensional solutions of the proposed revitalization concepts for the studied landscapes was achieved through the use of QGIS 3.28.3-Firenze 2022-08-31, Sketchup, and Realtime Landscaping Architect software tools. Main findings. In order to preserve the original idea and characteristics of the landscape compositions, the concept of revitalization of the White Garden was proposed, which was suggested by the "Genius loci" of the historical landscape of Sharivsky Park. Using the QGIS geographic information system, OSM standard map and Google Satellite Hybrid map, the 3D authentic relief of regular landscape compositions of the Sharivsky Palace and Park Complex was modeled. Using the Sketchup 2020 software tool, a graphical representation of the architectural, planning, and three-dimensional solutions of the studied historical landscape was made. The Sketchup 2020 software tool was also used for the graphic 3D reconstruction of numerous architectural details that adorn the palace, including balustrades, windows, doors, and towers decorated with ornaments. The dendrological plan was developed using species and cultivars of ornamental plants with identified historical and aesthetic value to ensure the continuity of new and preserved plant elements of landscape compositions. Scientific novelty and practical value. The proposed vision of preserving the sustainable features of the place, the selection of plants with identified historical and substantiated aesthetic value can be used in the complex reconstruction of regular garden and park landscapes of the Sharivskyi Park, a monument of landscape gardening art of national importance.

Cartography of teneurin and latrophilin expression reveals spatiotemporal axis heterogeneity in the mouse hippocampus during development.

Synaptic adhesion molecules (SAMs) are evolutionarily conserved proteins that play an important role in the form and function of neuronal synapses. Teneurins (Tenms) and latrophilins (Lphns) are well-known cell adhesion molecules that form a transsynaptic complex. Recent studies suggest that Tenm3 and Lphn2 (gene symbol Adgrl2) are involved in hippocampal circuit assembly via their topographical expression. However, it is not known whether other teneurins and latrophilins display similar topographically restricted expression patterns during embryonic and postnatal development. Here, we reveal the cartography of all teneurin (Tenm1-4) and latrophilin (Lphn1-3 [Adgrl1-3]) paralog expression in the mouse hippocampus across prenatal and postnatal development as monitored by large-scale single-molecule RNA in situ hybridization mapping. Our results identify a striking heterogeneity in teneurin and latrophilin expression along the spatiotemporal axis of the hippocampus. Tenm2 and Tenm4 expression levels peak at the neonatal stage when compared to Tenm1 and Tenm3, while Tenm1 expression is restricted to the postnatal pyramidal cell layer. Tenm4 expression in the dentate gyrus (DG) exhibits an opposing topographical expression pattern in the embryonic and neonatal hippocampus. Our findings were validated by analyses of multiple RNA-seq datasets at bulk, single-cell, and spatial levels. Thus, our study presents a comprehensive spatiotemporal map of Tenm and Lphn expression in the hippocampus, showcasing their diverse expression patterns across developmental stages in distinct spatial axes.

An Independent Hybrid Imaging of Sgr A* from the Data in EHT 2017 Observations

Abstract We propose that the ring structure found by the Event Horizon Telescope Collaboration (EHTC) as the black hole shadow of Sgr A* is an artifact by the bumpy PSF (Point Spread Function) of the EHT2017. The imaging using sparse u-v data requires detailed scrutiny of the PSF. The estimated shadow diameter ($\mathrm{48.7\pm 7~\mu \rm as}$) is equal to the spacing between the main beam and the first sidelobe of the PSF ($\mathrm{49.09~\mu \rm as}$), which immediately suggests a potential problem in the deconvolution of the PSF. We show that the ring image can be derived from non-ring simulated datasets (noise only; point source) with a narrow Field-of-View (FOV) and an assumed self-calibration suggesting the EHT2017’s u-v coverage is insufficient for reliable imaging. The EHTC analysis, based on calibrations with assumptions about the source’s size and properties, selected the final image by prioritizing appearance rate of the similar structure from a large imaging parameter space over data consistency. Our independent analysis with the conventional hybrid mapping reveals an elongated east-west structure, consistent with previous observations. We believe it to be more reliable than the EHTC image, due to half the residuals in normalized visibility amplitude. The eastern half is brighter, possibly due to a Doppler boost from the rapid rotating disk. We hypothesize our image shows a portion of the accretion disk about 2 to a few RS away from the black hole, rotating with nearly $\mathrm{60~\%}$ of the speed of light viewed from an angle of 40 − 45○.

Trajectory planning of a free-floating dual-arm space robot with minimal base disturbance in obstacle environments

This paper addresses the issue of trajectory planning for a free-floating dual-arm space robot, considering the constraints of collision avoidance while simultaneously minimizing the base position and attitude disturbance during on-orbit tasks. One arm is designated as the mission arm, tracking a prescribed path to complete specific goals, while the other serves as the balance arm, compensating for any base attitude disturbance. A collision-free path planning approach is presented for the mission arm, which utilizes a hybrid map, improved artificial potential field (IAPF) and adaptive coupling guidance method. The hybrid map in the mission arm joint space contains the coupling relationship between the deviation of the base position and the motion of the dual arms, along with obstacles transformed from Cartesian space. Then, to address the problem of the goals nonreachable with obstacles nearby (GNRON) of the artificial potential field (APF), a novel repulsive potential field is incorporated into the IAPF. To minimize the base position disturbance, a coupling guidance method is presented that enables the mission arm to move in areas with lower coupling factors on the hybrid map. Finally, simulations are conducted to demonstrate the effectiveness and superiority of the proposed hybrid map and coordinated trajectory planning approach.

LAEA: A 2D LiDAR-Assisted UAV Exploration Algorithm for Unknown Environments

In UAV autonomous exploration, large frontier clusters are commonly associated with high information gain and are visited first. In contrast, small and isolated frontier clusters with fewer frontiers are associated with smaller information gain and are thus explored with low priority. However, these small and isolated frontier clusters are often in close proximity to UAVs and surrounded by explored areas, which could result in back-and-forth flights that lower exploration efficiency. This paper proposes LAEA, a LiDAR-assisted and depth camera-dominated UAV exploration algorithm that aims to improve UAV autonomous exploration efficiency. A hybrid map is obtained that characterizes rich environmental profile information in real time, enabling us to detect small and isolated frontier clusters that can lead to repeated visits to explored areas. An environmental information gain optimization strategy is incorporated such that frontier clusters with larger unexplored areas behind them, as well as small and isolated frontier clusters close to the UAV, are assigned higher weights to prioritize their visit order. An optimized flight trajectory is generated to cover unexplored frontier clusters in the immediate vicinity of the UAV while flying to the next target. A comprehensive comparison between the proposed algorithm and state-of-the-art algorithms was conducted via a simulation study, which showed that our algorithm exhibits superior exploration efficiency in various environments. Experiments were also carried out to verify the feasibility of the proposed approach in real-world scenarios.

Cross-channel color image encryption through 2D hyperchaotic hybrid map of optimization test functions

The security of chaos-based Image Encryption (IE) algorithms inherently depends on the permutation and diffusion strategy of the algorithm and the dynamic performance of chaotic maps. However, the existing studies suffer from low disordering capability, poor dynamic performance, and lack of sensitivity of the chaotic maps. In order to address these limitations, a Cross-Channel Color IE (CCC-IE) algorithm is presented, which has high shuffling ability with diagonal permutation and bi-directional sequential diffusion through a novel 2D hyperchaotic hybrid map of optimization test functions. The proposed 2D hyperchaotic map, namely 2D-RG map, is designed by hybridizing the Rastrigin and Griewank functions. Their chaotic nature, such as high complexity and fluctuation, makes them suitable for design of chaotic map. The 2D-RG map-based CCC-IE algorithm diagonally scrambles the pixels of the image across RGB channels and sequentially manipulates the pixel values in bi-direction for improving resistance of the algorithm to the cyberthreats. The dynamic performance of the 2D-RG map and the reliability of the CCC-IE algorithm are separately investigated and validated through a detailed comparison with the state of the art. The results manifest that the CCC-IE not only visually but also numerically is the more reliable algorithm thanks to the superior chaotic characteristics of the 2D-RG map.

RFWBS Model: Multilevel Hybrid Mapping Solution Framework for Designing Neurorehabilitation Physiotherapy Devices

Healthcare services and rehabilitation equipment have entered a phase of rapid development driven by user requirements. However, the development of corresponding design models for rehabilitation equipment is lacking. A general framework and development process are urgently needed for neurorehabilitation physiotherapy equipment. To address problems such as inadequate knowledge representation in the design process and difficulties in modeling the functional structure of the product, we combined a decomposition topology model of neurorehabilitation physiotherapy equipment with the modular design method. We proposed a general model for the design of neurorehabilitation physiotherapy equipment comprising the following modules: requirements (R), function (F), principle workspace (W), behavior (B), and structure (S), i.e., the RFWBS model. Using the neurorehabilitation robotic glove as an example for design practice, in terms of kinematics, the mechanism is analyzed by establishing a Lagrangian coordinate system for resolution. The mechanism has three degrees of freedom and can achieve the natural flexion and extension angles of each finger joint. In terms of dynamics, during the entire finger extension, the angular acceleration is almost zero, and the average angular velocity is approximately 30~50°/s. This indicates that the mechanism is suitable for wearable use, validating the scientific and effective nature of the RFWBS expanded model.

Delayed Bilateral Teleoperation of Mobile Manipulators With Hybrid Mapping: Rate/Nonlinear-Position Modes

Delayed Bilateral Teleoperation of Mobile Manipulators With Hybrid Mapping: Rate/Nonlinear-Position Modes

A High-Quality Hybrid Mapping Model Based on Averaging Dense Sampling Parameters

Navigation map generation based on remote sensing images is crucial in fields such as autonomous driving and geographic surveying. Style transfer is an effective method for obtaining a navigation map of the current environment. However, there is lack of robustness of the map-style transfer model, resulting in unsatisfactory quality of the generated navigation maps. To address these challenges, we average the parameters of generators sampled from different iterations with a dense sampling strategy in the Generative Adversarial Network (CycleGAN). The results demonstrate that the training efficiency of our method on the MNIST and generation quality on the Google Map dataset are significantly superior to traditional style transfer methods. Moreover, it performs well in multi-environment hybrid mapping. Our method improves the generalization ability of the model and converts existing navigation maps to other styles of maps precisely. It can better adapt to different types of urban layout and road planning, bringing innovative solutions for traffic management and navigation systems.

Mixed Multi-Chaos Quantum Image Encryption Scheme Based on Quantum Cellular Automata (QCA)

The advent of quantum computers could enable the resolution of complex computational problems that conventional cryptographic protocols find challenging. As a result, the formidable computing capabilities of quantum computers may render all present-day cryptographic schemes that rely on computational complexity ineffectual. Inspired by these possibilities, the primary purpose of this paper is to suggest a quantum image encryption scheme based on quantum cellular automata with mixed multi-chaos hybrid maps and a hyperchaotic system with quantum operations. To achieve desirable encryption outcomes, we designed an encryption scheme involving two main operations: (1) pixel-level diffusion and (2) pixel-level permutation. Initially, the secret keys generated using the hyperchaotic system were closely tied to the original image. During the first phase, the establishment of correlations among the image pixels, in addition to the three chaotic sequences obtained from the hyperchaotic system, was achieved with the application of a quantum-state superposition and measurement principle, wherein the color information of a pixel is described using a single qubit. Therefore, the three channels of the plain image were subjected to quantum operations, which involve Hadamard transformation and the quantum-controlled NOT gate, before the diffusion of each color channel with the hyperchaotic system. Subsequently, a quantum ternary Toffoli gate was used to perform the diffusion operation. Next, the appropriate measurement was performed on the three diffused channels. To attain the confusion phase, a blend of mixed multi-chaos hybrid maps and a two-dimensional quantum cellular automaton was used to produce random and chaotic sequence keys. Subsequently, the circular shift was utilized to additionally shuffle the rows and columns of the three diffused components, in order to alter the positions of their pixel values, which significantly contributes to the permutation process. Lastly, the three encoding channels, R, G, and B, were merged to acquire the encrypted image. The experimental findings and security analyses established that the designed quantum image encryption scheme possesses excellent encryption efficiency, a high degree of security, and the ability to effectively withstand a diverse variety of statistical attacks.

A multi-step approach to evaluate the sustainable use of groundwater resources for human consumption and agriculture

The rapid decline in both quality and availability of freshwater resources on our planet necessitates their thorough assessment to ensure sustainable usage. The growing demand for water in industrial, agricultural, and domestic sectors poses significant challenges to managing both surface and groundwater resources. This study tests and proposes a hybrid evaluation approach to determine Groundwater Quality Indices (GQIs) for irrigation (IRRI), seawater intrusion (SWI), and potability (POT), finalized to the spatial distribution of groundwater suitability involving water quality indicator along with hydrogeological and socio-economic factors. Mean Decrease Accuracy (MDA) and Information Gain Ratio (IGR) were used to state the importance of chosen factors such as level of groundwater above the sea, thickness of the aquifer, land cover, distance from coastline, silt soil content, recharge, distance from river and lagoons, depth to water table from ground, distance from agricultural wells, hydraulic conductivity, and lithology for each quality index, separately. The results of both methods showed that recharge is the most important parameter for GQIIRRI and GQIPOT, while the distance from the coastline and the rivers, are the most important for GQISWI. The spatial modelling of GQIIRRI and GQIPOT in the study area has been achieved applying three machine learning (ML) algorithms: the Boosted Regression Tree (BRT), the Random Forest (RF), and the Support Vector Machine (SVM). Validation results showed that RF has the highest prediction for GQIIRRI, while the SVM model has the highest prediction for the GQIPOT index. It is worth to mention that the future utilization and testing of new algorithms could produce even better results. Finally, GQIIRRI and GQIPOT were combined and compared using two combine and overlay methods to prepare a hybrid map of multi-GQIs. The results showed that 69% of the study area is suitable for irrigation and potable use, due to both geogenic and anthropogenic activities which contribute to make some water resources unsuitable for either use. Specifically, the northern, western, and eastern portions of the study area are in the “very high and high quality” classes while the southern portion shows “very low and low quality” classes. In conclusion, the developed map and approach can serve as a practical guide for enhancing groundwater management, identifying suitable areas for various uses and pinpointing regions requiring improved management practices.

Precise Short-Term Small-Area Sunshine Forecasting for Optimal Seedbed Scheduling in Plant Factories

Photosynthesis is one of the key issues for vertical cultivation in plant factories, and efficient natural sunlight utilization requires predicting the light falling on each seedbed in a real-time manner. However, public weather services neither provide sunshine data nor meet spatial resolution requirement. Facing these short-term and small-area weather forecasting challenges, we propose a cross-scale approach to infer seedbed-sized areas of sunshine from the city-level public weather services, and then design a seedbed rotation scheduling system for optimal natural sunlight utilization. First, an end-edge-cloud coordinated computing architecture was employed to concurrently aggregate the multi-scale data from weather satellites to sunshine sensors in the plant factory. Second, the small area of sunshine deterministically depends on the meteorological data given a fixed environment, and this correlation was described by a hybrid mapping model, which combined the long short-term memory (LSTM) and gradient boosting decision tree (GBDT) algorithms to form the LSTM-GBDT hybrid prediction algorithm (LGHPA). By training the LGHPA with historical local sensory sunshine and the city-scale meteorological data, the hourly sunshine on a seedbed can be predicted from the public weather forecasting service. Finally, a dynamic seedbed scheduling scheme was constructed to provide uniform solar energy absorption according to the one-hour-ahead radiation estimation. Experiment results show that the hourly sunshine prediction error was less than 18.44% over a seasonal period and the deviation for different solar absorption by seedbeds with rotation capability is less than 7.1%. Consequently, it was demonstrated that the application of short-term, small-area sunshine forecasting improved the performance of seedbed rotation for uniformly absorbed solar radiation. The proposed method verifies the feasibility of precisely predicting small-area sunshine down to the seedbed scale by leveraging a model-based approach and a cloud-edge-end merged cybernetic computing paradigm.

A Geologic Map of Vesta Produced Using a Hybrid Method for Incorporating Spectroscopic and Morphologic Data

We have constructed a global geologic map of the minor planet Vesta at 1:300,000-scale using Dawn spacecraft imaging, spectroscopic, topographic, and elemental data. In this effort, we used a mapping method that requires creating two maps independently: the first map uses morphology and topography to define map units, while the second map relies on multispectral data (“color”) to define units. The two are then combined into a hybrid product that retains the maximum amount of unique information from both maps in a readable format. This effort has revealed that for bodies where cratering is the dominant unit-forming process, and where there is not a close correlation between morphological feature types and multispectral signal, a hybrid mapping method better retains unique information carried by multispectral data during the mapping process than traditional morphology-based methods alone. Conversely, relying too heavily on color data risks placing too much emphasis on information drawn from the top few microns of the surface. To ensure both consistency and retention of unique information, we created a decision tree for determining which data would be primary in choosing where to draw unit boundaries. Also due to the significant amount of information borne by spectral data, we repurposed traditional mapping nomenclature so that subscripts carry color information. We recommend using this mapping methodology on bodies where (a) morphologic feature boundaries are commonly subtle, gradational, or both, and (b) spectral data carries a significant amount of unique data for identifying, characterizing, and interpreting geologic units.