Multi-scale Maps Research Articles

A Database-based Automatic Cartography Method

Abstract. At present, GIS data, which contains a large amount of semantic information, has become the main data source for map cartography. In this paper, based on the existing multi-scale database, we study a database-based automatic cartography method to meet the needs of multi-scale map cartography, and replace the traditional process by the intelligent processing of software to shorten the cycle of cartography, improve the productivity of map cartography, and at the same time, reduce the error rate caused by manual work, and improve the overall quality of map production.

Accurate 3D LiDAR SLAM System Based on Hash Multi-Scale Map and Bidirectional Matching Algorithm.

Simultaneous localization and mapping (SLAM) is a hot research area that is widely required in many robotics applications. In SLAM technology, it is essential to explore an accurate and efficient map model to represent the environment and develop the corresponding data association methods needed to achieve reliable matching from measurements to maps. These two key elements impact the working stability of the SLAM system, especially in complex scenarios. However, previous literature has not fully addressed the problems of efficient mapping and accurate data association. In this article, we propose a novel hash multi-scale (H-MS) map to ensure query efficiency with accurate modeling. In the proposed map, the inserted map point will simultaneously participate in modeling voxels of different scales in a voxel group, enabling the map to represent objects of different scales in the environment effectively. Meanwhile, the root node of the voxel group is saved to a hash table for efficient access. Secondly, considering the one-to-many (1 ×103 order of magnitude) high computational data association problem caused by maintaining multi-scale voxel landmarks simultaneously in the H-MS map, we further propose a bidirectional matching algorithm (MSBM). This algorithm utilizes forward-reverse-forward projection to balance the efficiency and accuracy problem. The proposed H-MS map and MSBM algorithm are integrated into a completed LiDAR SLAM (HMS-SLAM) system. Finally, we validated the proposed map model, matching algorithm, and integrated system on the public KITTI dataset. The experimental results show that, compared with the ikd tree map, the H-MS map model has higher insertion and deletion efficiency, both having O(1) time complexity. The computational efficiency and accuracy of the MSBM algorithm are better than that of the small-scale priority matching algorithm, and the computing speed of the MSBM achieves 49 ms/time under a single CPU thread. In addition, the HMS-SLAM system built in this article has also reached excellent performance in terms of mapping accuracy and memory usage.

Abstract 2348: A global multiscale map of protein assemblies from integration of protein interactions and images

Abstract Cells regulate growth and function through a hierarchical structure of subcellular protein assemblies, in which alterations can result in cellular dysfunction leading to disease such as cancer. Much of this structure remains uncharted, resulting in recent efforts to map subcellular organization at different physical scales. Here, we report a global architectural map of human cancer cell protein assemblies spanning 10−9 to 10−5 nm, based on integration of near-proteome-wide affinity purification mass spectrometry-based protein interactions and immunofluorescent imaging in U-2 OS osteosarcoma cancer cells. The U-2 OS multi-scale integrated cell map places >5000 proteins into 270 distinct subcellular protein assemblies across biological scales, representing approximately half of expressed proteins. There are known organelles and protein complexes recovered in the map, as well as 152 putative assemblies, such as a putative interferon signaling complex consisting of a serine protease and STAT transcription factors. The map also incorporates 128 previously uncharacterized proteins into protein assemblies, such as C18orf21 association with a canonical RNAse complex. Protein subsystems in the U-2 OS map were evaluated for the potential for downstream integrative structural modeling, where available structural data (e.g. crosslinking, Protein Data Bank structures, AlphaFold predictions) are combined in a Bayesian framework to model the physical structures of protein complexes. Using this framework, we created an integrated structural model of new proteins interacting with the Rag-Ragulator complex, which regulates MAPK and mTOR signaling. In summary, the global proteome architecture map provides a resource for cellular biology discovery and the systematic determination of protein functions and structures. Citation Format: Leah V. Schaffer, Mengzhou Hu, Edward L. Huttlin, Gege Qian, Abantika Pal, Neelesh Soni, Andrew Latham, Laura Pontano Vaites, Trang Le, Yue Qin, Christopher Churas, Dexter Pratt, Ignacia Echeverria, Andrej Sali, J Wade Harper, Steven P. Gygi, Emma Lundberg, Trey Ideker. A global multiscale map of protein assemblies from integration of protein interactions and images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2348.

Abstract 6543: A map of osteosarcoma cell architecture reveals convergence of pediatric cancer mutations on protein assemblies

Abstract Pediatric tumors are characterized by a low incidence of somatic mutations, which occur in different genomic patterns from adult cancers. For these reasons, interpreting the functional impact of mutations in pediatric cancer patients has remained difficult. One approach to interpreting rare coding mutations is to analyze the convergence of these mutations on larger structural and functional units, such as protein complexes, super assemblies, biomolecular condensates, and organelles. Towards this aim, we created a cancer cell map in the osteosarcoma cell line based on proteome-wide integration of protein interactions and immunofluorescent imaging in U-2 OS pediatric bone cancer cells. This multiscale integrated cell map of U-2 OS cell organizes 5,254 proteins into 270 protein assemblies. Using this multiscale cell map, we analyze 772 genomes from 18 cancer types reported in the DKFZ pan-pediatric cancer study to identify 25 protein assemblies in the map under mutational pressure. This analysis reveals mutated protein assemblies involved with chromatin maintenance and remodeling as well as cell integrity and mobility. For example, we identify a Collagen Biosynthesis and Remodeling Complex, as mutated in 9 cancer types and 15 patients in the dataset, indicating extracellular matrix remodeling is associated with pediatric cancer progression. We also identify a putative biological condensate, which we call the Nuclear Transcription Suppression Complex (NTSC). This protein assembly is found mutated in 20 patients across 10 cancer types, implicating intrinsically disordered proteins in development of pediatric cancer. Overall, this work illustrates how a global multiscale map of cell architecture enables a mechanistic understanding of clinical patient genomes. Citation Format: Mengzhou Hu, Leah V. Schaffer, Edward L. Huttlin, Gege Qian, Andrew Latham, Abantika Pal, Laura Pontano Vaites, Trang Le, Yue Qin, Christopher Churas, Dexter Pratt, Robin Bachelder, Peter Zage, Ignacia Echeverria, Andrej Šali, J. Wade Harper, Steven P. Gygi, Emma Lundberg, Trey Ideker. A map of osteosarcoma cell architecture reveals convergence of pediatric cancer mutations on protein assemblies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6543.

Multi-typology and multi-scale maps of the in-use building material stock in China

China's rapid urban expansion and population migration have led to a substantial increase in building material stocks (MS). Understanding these stocks is crucial for effective planning and managing the built environment. This study analyzed in-use building MS in China from 2000 to 2020 at different spatial scales and resolutions and employed typological data as a bridge addressing information gaps in non-residential buildings and the spatial scale discontinuity issue in MS analysis. At the macro scale, MS in China increased from 70 gigatons (Gt) in 2000 to 133 Gt in 2020. Non-residential and non-urban MS played substantial roles, constituting 41.8 % and 47.9 % in 2020, respectively. In highly urbanized regions, the proportion of residential building MS decreased due to the prevalence of more functional buildings. Some southern provinces had MS from rural areas even exceeding 50 %, emphasizing the importance of considering rural areas in construction resource management. At a high-resolution scale using GIS data, differences emerged between the analysis and statistical data. These differences, especially in estimating residential and commercial building MS, may be attributed to the mixed-use of building types in Chinese cities or unclear delineations of land use types. We advocate for further research to explore these variations, establishing consistent MS methods across different spatial scales and providing decision-makers with effective data support and analytical tools.

Testing temporal transferability of remote sensing models for large area monitoring

Applying remote sensing models outside the temporal range of their training data, referred to as temporal model transfer, has become common practice for large area monitoring projects that extrapolate models for hindcasting or forecasting to time periods lacking reference data. However, the development of appropriate validation methods for temporal transfer has lagged behind its rapid adoption. Breaking temporal transfer's assumption of temporal consistency in both remote sensing and reference data and their relationship to each other could lead to biased pixel-level predictions and small area estimators, compromising the operational validity of large area monitoring products. Few studies using temporal transfer have evaluated its effects on model accuracy at the pixel/plot level, and the propensity for biased small area estimators and trends resulting from temporal transfer remains unexplored. We present a framework for evaluating temporal transferability by combining temporal cross-validation with a multiscale map assessment to aid in identifying where and when biased model predictions could scale to small area estimates and affect predicted trends.This validation framework is demonstrated in a case study of annual percent tree canopy cover mapping in Michigan. We tested and compared temporal transferability of random forest models of canopy cover derived from 2010 to 2016 systematic dot-grid photo-interpretations at Forest Inventory and Analysis plots with Landsat spectral indices fit with the LandTrendr temporal segmentation algorithm serving as the primary predictor variables. The temporal cross-validation error (mean RMSE = 13.9% cover) was higher than the common validation approach of considering all time periods of testing data together (RMSE = 13.6% cover) and lower than models trained and tested within the same year (mean RMSE = 14.2% cover). However, the bias of model predictions and small area estimators for individual years was higher with temporal transfer models than when applying models within the same year as their training data. We also evaluated how training models using different temporal subsets and with and without LandTrendr fitting affected predictions of Michigan's 1984–2020 predicted annual mean cover. The mean cover from LandTrendr-based models followed expected and consistent trends and had less difference between models trained with different temporal subsets (max difference = 5.8% cover). While those from Landsat had high interannual variations and greater difference between temporal models (max difference = 11.2% cover). The results of this case study demonstrate that evaluation of temporal transferability is necessary for establishing the operational validity of large area monitoring products, even when using time series algorithms that improve temporal consistency.

Partitioning building groups at multiple scales based on image segmentation

The partitioning of building groups is a prerequisite for map generalization. Existing methods center on the spatial relationships between individual buildings, neglecting the interrelations among building groups. As such, these methods prove suitable only for identifying building groups at a single scale. In this research, we introduce a novel methodology for partitioning building groups across multiple scales by employing image segmentation, thus enabling the simultaneous recognition of building groups of all scales within a recognition unit. First, image segmentation and merging approaches were employed to extract merged image regions, representing the spatial relationships of building groups. Then, the process commenced with the construction of a graphical representation encompassing buildings and merged regions, wherein the edges were assigned weights indicative of the closeness between groups of buildings. Finally, the graph underwent a progressive segmentation process, and a scale set of building groups was established. Based on the experimental findings, the proposed approach can proficiently partition building groups across various scales. This is demonstrated by the accuracy rate surpassing 90% in the complex experimental area. One can effortlessly derive building groups from the scale set by using a predetermined scale parameter. These findings may serve as a fundamental basis for the implementation of multiscale map generalization and the linkage updating of multiscale spatial data.

More is less - Adding zoom levels in multi-scale maps to reduce the need for zooming interactions

When you zoom in or out of current multi-scale cartographic applications, it is common to feel lost and disoriented for a few seconds because dimensions and map symbols have changed. To make the exploration of these multi-scale maps more fluid, one option is to design maps where the transformations due to scale change are more progressive. This paper proposes to use cartographic generalization techniques to design these multi-scale maps with additional intermediate scales to improve progressiveness. These more progressive maps are tested in a user study with a task requiring multiple zooms in and out. The users perform better with the progressive maps, and in particular, the total quantity of required zooming is reduced compared to maps without additional intermediate scales. However, the survey is not fully conclusive on task performance due to the complexity of such a survey with real maps. This difficulty in assessing how well progressive map generalisation reduces disorientation is discussed and guidelines are proposed to design further studies.

Quantitative expressions of spatial similarity between road networks in multiscale map spaces

ABSTRACT Spatial similarity plays a critical role in the perception and cognition in capturing information from maps; it can be used as a constraint to automate map generalization. Although measuring similarities seems natural to humans, it can be challenging to quantify them. This is especially true when it comes to calculating spatial similarity degrees between groups of spatial objects at varying scales and quantitatively expressing the relations between spatial similarity and change of map scale in multiscale map spaces. Taking road networks as an example, this paper proposes an approach to measuring spatial similarity between a road network at a large scale and its generalized counterpart at a smaller scale. By fitting a power function to three typical types of road networks, this paper provides a formula for expressing the change in spatial similarity as the map scale changes. The proposed quantitative method lays a foundation for using spatial similarity as a constraint during road network generalization.

A similarity calculation model of road network and its application in map generalization quality evaluation

ABSTRACT As an important part of maps, road networks have always been the focus of research in map generalization. However, the fully automated quality evaluation of road network generalization has not been implemented. This paper proposes a framework of Sample-Map database to evaluate road network generalization quality using spatial similarity measures. The similarity calculation model considers spatial distribution, topological relations, and distance relations of road networks. A Sample-Maps database is created as an evaluation set for different road network types. The method is tested on multi-scale maps from Shenzhen, China, and validated by psychological cognition experiments and professional cartographers’ judgment.

Quantitative Relations between Morphostructural Similarity Degree and Map Scale Change in Contour Clusters in Multi-Scale Map Space

This paper aims to propose a new approach to calculate the quantitative relations between morphostructural similarity degree and map scale change in multi-scale contour clusters for automatic contour generalization. Terrain lines were extracted by pre-processing of unclosed contour lines, and an indirect quantitative expression method of morphostructural similarity relation was proposed based on terrain line hierarchical trees. Thirteen groups of multi-scale contour clusters with different drainage areas of loess geomorphy were employed to explore the changing regularity of morphostructural similarity indices with map scale. Finally, the quantitative relations between morphostructural similarity degree and map scale change were calculated using 52 groups of points. The results show that power function is the best fit among the candidate functions, and the quantitative relations between the morphostructural similarity degree and map scale change can be expressed using the same power function, which facilitates the automation of contour generalization.

Where do people look at during multi-scale map tasks?

Abstract. In order to design better pan-scalar maps, i.e. interactive, zoomable, multi-scale maps, we need to understand how they are perceived, understood, processed, manipulated by the users. This paper reports an experiment that uses an eye-tracker to analyse the gaze behaviour of users zooming and panning into a pan-scalar map. The gaze data from the experiment shows how people look at landmarks to locate the new map view after a zoom. We also identified different types of behaviours during a zoom when people stare at the mouse cursor, or during a pan where the gaze follows a landmark while the map translates.

A multi-scale map of protein assemblies in the DNA damage response.

The DNA damage response (DDR) ensures error-free DNA replication and transcription and is disrupted in numerous diseases. An ongoing challenge is to determine the proteins orchestrating DDR and their organization into complexes, including constitutive interactions and those responding to genomic insult. Here, we use multi-conditional network analysis to systematically map DDR assemblies at multiple scales. Affinity purifications of 21 DDR proteins, with/without genotoxin exposure, are combined with multi-omics data to reveal a hierarchical organization of 605 proteins into 109 assemblies. The map captures canonical repair mechanisms and proposes new DDR-associated proteins extending to stress, transport, and chromatin functions. We find that protein assemblies closely align with genetic dependencies in processing specific genotoxins and that proteins in multiple assemblies typically act in multiple genotoxin responses. Follow-up by DDR functional readouts newly implicates 12 assembly members in double-strand-break repair. The DNA damage response assemblies map is available for interactive visualization and query (ccmi.org/ddram/).

Where Am I Now? Modelling Disorientation in Pan-Scalar Maps

Disorientation is a common feeling for all users of zoomable multi-scale maps, even for those with good orientation and spatial skills. We make the assumption that this problem is mainly due to the desert fog effect, documented in human–computer interaction within multi-scale zoomable environments. Starting with a collection of reported experiences of disorientation, this paper explores this notion from the spatial cognition, philosophical and human–computer interaction perspectives and proposes a model of disorientation in the exploration of multi-scale maps. We argue that disorientation is a problem of reconciliation between the current map view and the mental map of the user, where landmarks visible on the map or memorised in the mental map play a key role. The causes for failed reconciliation are discussed and illustrated by our collected experiences of disorientation.

PHANGS–JWST First Results: The Dust Filament Network of NGC 628 and Its Relation to Star Formation Activity

PHANGS–JWST mid-infrared (MIR) imaging of nearby spiral galaxies has revealed ubiquitous filaments of dust emission in intricate detail. We present a pilot study to systematically map the dust filament network (DFN) at multiple scales between 25 and 400 pc in NGC 628. MIRI images at 7.7, 10, 11.3, and 21 μm of NGC 628 are used to generate maps of the filaments in emission, while PHANGS–HST B-band imaging yields maps of dust attenuation features. We quantify the correspondence between filaments traced by MIR thermal continuum/polycyclic aromatic hydrocarbon (PAH) emission and filaments detected via extinction/scattering of visible light; the fraction of MIR flux contained in the DFN; and the fraction of H ii regions, young star clusters, and associations within the DFN. We examine the dependence of these quantities on the physical scale at which the DFN is extracted. With our highest-resolution DFN maps (25 pc filament width), we find that filaments in emission and attenuation are cospatial in 40% of sight lines, often exhibiting detailed morphological agreement; that ∼30% of the MIR flux is associated with the DFN; and that 75%–80% of the star formation in H ii regions and 60% of the mass in star clusters younger than 5 Myr are contained within the DFN. However, the DFN at this scale is anticorrelated with looser associations of stars younger than 5 Myr identified using PHANGS–HST near-UV imaging. We discuss the impact of these findings on studies of star formation and the interstellar medium, and the broad range of new investigations enabled by multiscale maps of the DFN.

Deep learning for anchor detection in multi-scale maps

Deep learning for anchor detection in multi-scale maps

Visual Inspection Method for Metal Rolls Based on Multi-Scale Spatial Location Feature

Metal rolls in a non-ferrous-metal manufacturing workshop manifest the characteristics of symmetry, multiple scales and mutual covering, which poses great challenges for metal roll detection. To solve this problem, firstly, an efficient attention mechanism algorithm named ECLAM (efficient capture location attendant model) is proposed for capturing spatial position features efficiently, to obtain complete location information for metal rolls in a complex environment. ECLAM can improve the ability to extract the spatial features of backbone networks and reduce the influence of the non-critical background. In addition, in order to give feature maps a larger receptive field and improve the weight of location information in multi-scale feature maps, a nonlinear feature fusion module named LFFM (location feature fusion module) is used to fuse two adjacent feature images. Finally, a multi-scale object detection network named L-MSNet (location-based multi-scale object detection network) based on the combination of ECLAM and LFFM is proposed and used to accurately detect multi-scale metal rolls. In the experiments, multi-scale metal roll images are collected from an actual non-ferrous-metal manufacturing workshop. On this basis, a pixel-level image dataset is constructed. Comparative experiments show that, compared with other object detection methods, L-MSNet can detect multi-scale metal rolls more accurately. The average accuracy is improved by 2% to 5%, and the average accuracy of small and medium-sized objects is also significantly improved by 3% to 6%.

Multi-Prior Twin Least-Square Network for Anomaly Detection of Hyperspectral Imagery

Anomaly detection of hyperspectral imagery (HSI) identifies the very few samples that do not conform to an intricate background without priors. Despite the extensive success of hyperspectral interpretation techniques based on generative adversarial networks (GANs), applying trained GAN models to hyperspectral anomaly detection remains promising but challenging. Previous generative models can accurately learn the complex background distribution of HSI and typically convert the high-dimensional data back to the latent space to extract features to detect anomalies. However, both background modeling and feature-extraction methods can be improved to become ideal in terms of the modeling power and reconstruction consistency capability. In this work, we present a multi-prior-based network (MPN) to incorporate the well-trained GANs as effective priors to a general anomaly-detection task. In particular, we introduce multi-scale covariance maps (MCMs) of precise second-order statistics to construct multi-scale priors. The MCM strategy implicitly bridges the spectral- and spatial-specific information and fully represents multi-scale, enhanced information. Thus, we reliably and adaptively estimate the HSI label to alleviate the problem of insufficient priors. Moreover, the twin least-square loss is imposed to improve the generative ability and training stability in feature and image domains, as well as to overcome the gradient vanishing problem. Last but not least, the network, enforced with a new anomaly rejection loss, establishes a pure and discriminative background estimation.

Map-based localization for intelligent vehicles from bi-sensor data fusion

Accurate localization is a challenging task for intelligent vehicles (IVs). Map-based localization methods are promising as they have no cumulative errors and high accuracy. Most methods are based on a single sensor, such as a camera or laser rangefinder (LRF). However, these sensors have disadvantages. Thus, this study proposes a map-based localization method from bi-sensor data fusion. We proposed a method using a camera, LRF, differential Global Positioning System, Inertial Navigation System to generate a multi-scale map. The multi-map consists of scenario nodes, and each node encodes scenario features, road 3D structure, and trajectories. Based on the multi-scale map, we propose an accurate localization method that includes coarse, node-level, and metric localizations. A bitopological localization model is set up for coarse localization. In this model, we use previous feature matching results to predict the current position. The features include image and light detection and ranging (LIDAR) data, and each of them set up one topological model. The two models make up a bitopological localization model. In node-level localization, we extract the LIDAR and image features and match them with the multi-scale map. The closest node is found by K-nearest neighbors from multiple feature spaces. In this step, we keep the image feature extraction in mind and propose a novel LIDAR feature extraction method called the LIDAR-image feature. In metric localization, we solve the perspective n-points problem between the current position and the closest node to compute the pose of IVs. In the experiments, our method has been tested on open road and campus. Each route covers different times and weather conditions. The proposed method can achieve less than 0.20 m mean localization errors. Compared with existing map-based methods, our method utilizes bi-sensors to improve localization performance.

Web Mapping For Forest Environmental Services of Protected Areas

Forest environmental services (FES) is a key solution to balance conservation and local development. For in-situ conservation, only a small number of Vietnam’s protected areas that have FES activities and revenue from their PFES has been low. Increasing awareness and sharing information and knowledge of protected areas’ PFES to promote PFES policy is needed. Among sharing information media, web map has recently been paid attention to. This study aims to develop a multi-scale web map of protected areas’ FES. Study areas include CatTien National Park (NP) (detailed scale), NPs of Southeast Vietnam, and social-economic zones (generalized scales). Input data include provincial reports on PFES projects, provincial PFES plans, and GIS data on provincial/NP’s forest resources and management and protection of the forest. Based on Law on Forestry 2017, Decree 156/NĐ-CP, and related regulations, GIS analysis was carried out to calculate K-Coefficient; supplied and paid FES areas; the amount of payment; and socio-economic and environmental impacts of PFES. The web map, which is developed and published via ArcGIS Platform, is a series of 53/79 expected multi-scale maps with interactive tools. The result shows that web mapping of protected areas’ FES is feasible. Although there is a limitation of data collection, the web map result is acceptable for the purpose of sharing information and raising people’s awareness.