Hasil untuk "Cartography"

Ravi Suthar, Raveena, Krishnendra Shekhawat

A rectangular floorplan is a partition of a rectangle into smaller rectangles such that no four rectangles meet at a single point. Rectangular floorplans arise naturally in a variety of applications, including VLSI design, architectural layout, and cartography, where efficient and flexible spatial subdivisions are required. A central concept in this domain is that of area-universality: a floorplan (or more generally, a rectangular layout) is area-universal if, for any assignment of target areas to its constituent rectangles, there exists a combinatorially equivalent layout that realizes these areas. In this paper, we investigate the structural conditions under which an outerplanar graph admits an area-universal rectangular layout. We establish a necessary and sufficient condition for area-universality in this setting, thereby providing a complete characterization of admissible outerplanar graphs. Furthermore, we present an algorithmic construction that guarantees that the resulting layout is always area-universal.

Fabian Esser, Lukáš Gráf, Chandan Hati

We perform a systematic study of lepton-number-violating (LNV) dimension-9 operators in the Standard Model Effective Field Theory (SMEFT) that can mediate neutrinoless double beta decay ($0νββ$) at tree level, and map them to their possible tree-level ultraviolet completions. Using a diagram-based classification, we enumerate all such completions and isolate minimal two-particle models that avoid generating the dimension-5 Weinberg operator or dimension-7 LNV operators at tree level. We then chart how these minimal models populate the operator landscape and organise them by the loop order at which they radiatively induce lower-dimensional LNV operators, highlighting scenarios in which the tree-level dimension-9 contribution can compete with or dominate loop-suppressed neutrino-mass (dimension-5) effects. Representative one-loop and two-loop classes are matched onto the SMEFT, and their implications for neutrino masses, charged-lepton flavour violation, and the relative size of dimension-9 versus dimension-5 contributions to $0νββ$ are analysed, delineating regions of parameter space where upcoming experiments can be sensitive to genuinely short-range LNV dynamics.

Yanrong Chen, Zhiwen Shi, Anwar Eziz et al.

High-accuracy Digital Elevation Models (DEMs) are critical for hydrological and ecological applications in low-relief arid basins, yet Interferometric Synthetic Aperture Radar (InSAR)-derived DEMs suffer from significant altitudinal errors due to temporal decorrelation and phase unwrapping artifacts, particularly in flat terrains. To address these limitations, we developed a novel machine learning framework that synergizes Sentinel-1 InSAR, UAV photogrammetry, Sentinel-2 spectral indices, and ALOS topographic features to enhance DEM accuracy. The approach was validated in Northwest China’s Taitema Lake basin across 13 sample plots covering diverse arid surface types (dunes, wetlands, playas). Four algorithms – Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Polynomial Regression (PR) – were rigorously evaluated. Without topographic data, SVM achieved the highest accuracy (test-set R2 = 0.8564). Integrating terrain features with RF further improved performance (R2 = 0.8634, MAE = 1.0683 m), reducing errors from approximately [−10, 27] m to predominantly ±6 m. The RF-corrected DEM exhibited a 42.8% decrease in standard deviation (2.60 m → 1.49 m) and a substantial R2 increase (16.4% → 89.1%). Shapley Additive exPlanations (SHAP) interpretability analysis identified slope and near-infrared reflectance as dominant error-correction features. The corrected DEMs demonstrate enhanced terrain continuity, minimized elevation noise, and offer a scalable, efficient solution for InSAR post-processing in ecologically sensitive arid regions.

Tobias Kauer, Marian Dörk, Benjamin Bach

This work investigates personal perspectives in visualization annotations as devices for collective data-driven storytelling. Inspired by existing efforts in critical cartography, we show how people share personal memories in a visualization of COVID-19 data and how comments by other visualization readers influence the reading and understanding of visualizations. Analyzing interaction logs, reader surveys, visualization annotations, and interviews, we find that reader annotations help other viewers relate to other people's stories and reflect on their own experiences. Further, we found that annotations embedded directly into the visualization can serve as social traces guiding through a visualization and help readers contextualize their own stories. With that, they supersede the attention paid to data encodings and become the main focal point of the visualization.

Seohyeong Lee, Eunwon Kim, Hwaran Lee et al.

Human preference data plays a critical role in aligning large language models (LLMs) with human values. However, collecting such data is often expensive and inefficient, posing a significant scalability challenge. To address this, we introduce Alignment Data Map, a GPT-4o-assisted tool for analyzing and diagnosing preference data. Using GPT-4o as a proxy for LLM alignment, we compute alignment scores for LLM-generated responses to instructions from existing preference datasets. These scores are then used to construct an Alignment Data Map based on their mean and variance. Our experiments show that using only 33 percent of the data, specifically samples in the high-mean, low-variance region, achieves performance comparable to or better than using the entire dataset. This finding suggests that the Alignment Data Map can significantly improve data collection efficiency by identifying high-quality samples for LLM alignment without requiring explicit annotations. Moreover, the Alignment Data Map can diagnose existing preference datasets. Our analysis shows that it effectively detects low-impact or potentially misannotated samples. Source code is available online.

Konstantin Leyde, Tessa Baker, Wolfgang Enzi

The dark siren method exploits the complementarity between gravitational-wave binary coalescence signals and galaxy catalogs originating from the same regions of space. However, all galaxy catalogs are incomplete, i.e. they only include a subset of all galaxies, typically being biased towards the bright end of the luminosity distribution. This sub-selection systematically affects the dark siren inference of the Hubble constant $H_0$, so a completeness relation has to be introduced that accounts for the missing objects. In the literature it is standard to assume that the missing galaxies are uniformly distributed across the sky and that the galaxy magnitude distribution is known. In this work we develop a novel method which improves upon these assumptions and reconstructs the underlying true galaxy field, respecting the spatial correlation of galaxies on large scales. In our method the true magnitude distribution of galaxies is inferred alongside the spatial galaxy distribution. Our method results in an improved three-dimensional prior in redshift and sky position for the host galaxy of a GW event, which is expected to make the resulting $H_0$ posterior more robust. Building on our previous work, we make a number of improvements, and validate our method on simulated data based on the Millennium simulation. The inference results can be reproduced through our publicly available code base light.

M. Monmonier

Focusing on historical examples and the practises of modern cartography, J.B. Harley (1932-1991) offers an alternative to the dominant view that Western cartography since the Renaissance has been a progressive technological, scientific and objective trajectory of development. This traditional view asserts that maps produce an accurate relational model of terrain and, as such, epitomize representational modernism, which is rooted in the project of the Enlightenment; in sum, maps banish subjectivity from the image. Accordingly, cartographers have promoted a standard scientific model for their discipline, one in which a mirror of nature can be projected through geometry and measurement. Cartographers often mistakenly assess early maps by this modern yardstick, excising from the accepted canon of mapping not only maps from the premodern era but also those from other cultures that do not match Western notions of accuracy. In these essays, Harley draws on ideas in art history, literature, philosophy, and the study of visual culture to subvert the traditional, "positivist" model of cartography, replacing it with one that is grounded in an iconological and semiotic theory of the nature of maps. He defines a map as a "social construction" and argues that maps are not simple representations of reality but exert profound influences upon the way space is conceptualized and organized. A central theme is the way in which power - whether military, political, religious or economic - becomes inscribed on the land through cartography. In this reading of maps and map making, Harley undertakes a surprising journey into the nature of the social and political unconscious.

Wandong Jiang, Yuli Sun, Lin Lei et al.

Change detection (CD) is essential in remote sensing (RS) for natural resource monitoring, territorial planning, and disaster assessment. With the abundance of data collected by satellite, aircraft, and unmanned aerial vehicles, the utilization of multisource RS image CD (RSICD) enables the efficient acquisition of ground object change information and timely updates to existing databases. Although CD techniques have been developed and successfully applied for approximately six decades, a systematic and comprehensive review that addresses emerging trends, including multisource, data-driven, and large-scale artificial intelligence (AI) models, is lacking. Therefore, first, the development process of RSICD was reviewed. Second, the characteristics of multisource RS images were analyzed, and all publicly available RSICD data that we could gather were collected and organized. Third, RSICD methods were systematically classified and summarized on the basis of the detection framework, detection granularity, and data sources. Fourth, the suitability of specific data and CD methods for diverse applications and tasks was assessed. Finally, challenges, opportunities, and future directions for RSICD were discussed within the context of high-resolution imagery, multisource data, and large-scale AI models. This review can help researchers better understand this field, shed light on this topic, and inspire further RSICD research efforts.

Ibragimov Orif, Inamov Begzod, Alimakhamatova Shakhnoza

Soil erosion is a critical environmental issue affecting agricultural productivity and sustainability globally. In the Bekabad district of Uzbekistan, soil erosion, primarily driven by wind and water, poses significant threats to the fertility and stability of agricultural lands. This study employs the Revised Universal Soil Loss Equation (RUSLE) model within the Google Earth Engine (GEE) framework to map and evaluate soil erosion dynamics in Bekabad district over a three-year period (2016-2018). By integrating diverse datasets, including CHIRPS precipitation data, OpenLandMap soil properties, SRTM Digital Elevation Model (DEM) data, Sentinel-2 optical imagery, and MODIS land cover data, we conducted a comprehensive spatial and temporal analysis of soil erosion. The results reveal an overall increase in moderate and slight soil erosion classes, underscoring the dynamic nature of soil erosion processes in the district. These findings highlight the necessity for continuous monitoring and the implementation of effective soil conservation measures, such as vegetative cover, terracing, and contour farming, to mitigate erosion impacts and preserve soil resources.

Kaidan Shi, Yanan Su, Jinhao Xu et al.

Reservoirs play a critical role in terrestrial hydrological systems, but the contribution of small and medium-sized ones is rarely considered and recorded. Particularly in developing countries, there is a rapid increase of such reservoirs due to their quick construction. Accurately identifying these reservoirs is important for understanding social and economic development, but distinguishing them from other natural water bodies poses a significant challenge. Thus, we propose a method to identify reservoirs using high-resolution satellite images and deep learning algorithms. We trained models with various parameters and network structures, and You Only Look Once version 7 (YOLOv7) outperformed other algorithms and was selected to build the final model. The method was applied to a region in northwestern Iran, characterized by an abundance of reservoirs of various sizes. Evaluation results indicated that our method was highly accurate (mAP: 0.79, Recall: 0.76, Precision: 0.82). The YOLOv7 model was able to automatically identify 650 reservoirs in the entire study region, indicating that the proposed method can accurately detect reservoirs and has the potential for broader-scale surveys, even global applications.

Siqin Wang, Yukio Sadahiro

ABSTRACTEvaluating the inequity of healthcare accessibility across demographic groups in the post-COVID era is of critical importance for an aging society like Japan – it helps to achieve better social equity via distributing healthcare resources in health planning and policy making. Our study contributes to the first post-covid evaluation of multi-modal healthcare accessibility in Tokyo, Japan, the most populated metropolis in the world. A further novelty goes to the multi-dimensional examination of the inequity of healthcare accessibility (i.e. hospitals) by public transit, driving and walking – the horizontal inequity across urban space and the vertical inequity across three demographic groups (the young, adult and elderly) through network analysis, spatial accessibility analysis and inequity indexing. We find that low healthcare access areas mainly appear in the peri-urban space as well as regions less covered by public transit. Compared to the adult group, the elderly group experiences significant inequity of healthcare access particularly in the peri-urban areas where driving is the dominant transport mode to access healthcare facilities. We provide timely evidence to the Japanese government and health authorities to have a holistic and latest understanding of multi-modal healthcare access across different demographic groups in the post-COVID era.

Sidi Wu, Katharina Henggeler, Yizi Chen et al.

Maps are broadly relevant across various fields, serving as valuable tools for presenting spatial phenomena and communicating spatial knowledge. However, map-making is still largely confined to those with expertise in GIS and cartography due to the specialized software and complex workflow involved, from data processing to visualization. While generative AI has recently demonstrated its remarkable capability in creating various types of content and its wide accessibility to the general public, its potential in generating maps is yet to be fully realized. This paper highlights the key applications of generative AI in map-making, summarizes recent advancements in generative AI, identifies the specific technologies required and the challenges of using current methods, and provides a roadmap for developing a generative mapping system (GMS) to make map-making more accessible.

Dali Wang, Xiaochong Tong, Chenguang Dai et al.

The ubiquitous spatiotemporal information extracted from Internet texts limits its application in spatiotemporal association and analysis due to its unstructured nature and uncertainty. This study uses ST-Voxel modeling to solve the problem of structured modeling and the association of ubiquitous spatiotemporal information in natural language texts. It provides a new solution for associating ubiquitous spatiotemporal information on the Internet and discovering public opinion. The main contributions of this paper include: (1) It proposes a convolved method for ST-Voxel, which solves the voxel modeling problem of unstructured and uncertain spatiotemporal objects and spatiotemporal relation in natural language texts. Experiments show that this method can effectively model 5 types of spatiotemporal objects and 16 types of uncertain spatiotemporal relation founded in texts; (2) It realizes the unknown event discovery based on voxelized spatiotemporal information association. Experiments show that this method can effectively solve the aggregation of ubiquitous spatiotemporal information in multi-natural language texts, which is conducive to discovering spatiotemporal events. The selection of convolution parameters in voxel modeling is also discussed. A parameter selection method for balancing the discovery capability and discovery accuracy of spatiotemporal events is given.

Yuting Zhao, Jungho Im, Zhen Zhen et al.

Accurate quantification of individual tree parameters is vital for precise forest inventory and sustainable forest management. However, in dense forests, terrestrial laser scanning (TLS), which can provide accurate and detailed forest structural measurements, is limited to capturing the complete tree structure due to the lack of upper canopy views, resulting in an underestimation of tree height. Combining TLS with unmanned aerial vehicle laser scanning (ULS) is an effective way to overcome this limitation. Thus, it is vital to register multi-platform Light Detection and Ranging (LiDAR) data for various forestry applications. This study proposed three automated and nearly parameter-free optimized coarse-to-fine algorithms (i.e. FPFH-based optimized ICP (F-OICP), RANSAC-based optimized ICP (R-OICP), and NDT-based optimized ICP (N-OICP)) to accurately register TLS and ULS point data for individual tree crown delineation and parameters (diameter at breast height (DBH) and tree height) estimations in different forest types (i.e. coniferous, mixed broadleaf-coniferous, and broadleaf). Results showed that the proposed optimized algorithms had a good registration performance, with an average RMSE of about 8.3 cm for the transformation error; and obtained stable and high accuracies of individual tree crown delineation (ITCD) (F-score: 0.7), DBH (R2: 0.9, RMSE <1.85 cm), and tree height (R2: 0.8, RMSE <0.37 m) estimates for three forest types. F-OICP performed the best in tree height estimation, reducing the RMSE by 48%, 12%, and 12% compared to iterative closest point (ICP), R-OICP, and N-OICP, respectively. Stand type significantly impacted ITCD and individual tree parameter estimations. The ITCD and DBH estimation accuracy of coniferous forests were marginally higher than those of broadleaf forests (F-score: 0.78 vs. 0.78, DBH RMSE: 1.57 vs. 1.74), while those of mixed broadleaf-coniferous forests were the lowest (F-score: 0.71, DBH RMSE: 2.19). The accuracies of tree height estimates in coniferous forests were the highest (R2: 0.87, RMSE: 0.21 m), followed by mixed broadleaf-coniferous (R2: 0.84, RMSE: 0.37 m) and broadleaf (R2: 0.84, RMSE: 0.44 m) forests. This work developed automated, nearly parameter-free, and effective registration algorithms and recommended F-OICP to be the most appropriate for dense forests (i.e. natural secondary forests). The optimized registration algorithms facilitate the ability for the synergistic use of multi-platform LiDAR and offer appealing and promising approaches for future accurate quantification of individual tree parameters, efficient forest inventories, and sustainable forest management.

J. Crampton, J. Krygier

Miguel Ángel Maté-González, Jesús Rodríguez-Hernández, Cristina Sáez Blázquez et al.

This research presents a virtual tour performed on the <i>oppidum</i> of Ulaca, one of the most relevant archaeological sites of the Iberian Peninsula during the Late Iron Age (<i>ca</i>. 400–50 BC). Beyond the clear benefits of the tool to the interpretation, dissemination, and knowledge of the mentioned archaeological site and its surroundings, the novelty of this research is the implementation of the platform in alternative scenarios and purposes. In this way, the present work verifies how the access to multi-source and spatially geolocated information in the same tool (working as a geospatial database) allows the promotion of cross-sectional investigations in which different specialists intervene. This peculiarity is also considered useful to promote tourism with an interest beyond the purely historical/archaeological side. Likewise, the possibility of storing and managing a large amount of information in different formats facilitates the investigation in the contexts of excavations and archaeological or environmental works. In this sense, the use of this kind of tool for the study of cultural landscapes is especially novel. In order to better contextualize the potential of the virtual tour presented here, an analysis about the challenges and possibilities of implementing this tool in environments such as the Ulaca <i>oppidum</i> is performed. The selected site stands out for: (i) being in a unique geological, environmental and ecological context, allowing us to appreciate how human beings have modified the landscape over time; (ii) presenting numerous visible archaeological remains with certain conservation problems; and (iii) not having easy access for visitors.

IBRAHIM MWATHANE, Mwenda Makathimo, Robert Kibugi et al.

This paper was presented in the 2021 Conference on Land Policy in Africa held in Kigali, Rwanda, in November 2021. It is based on a three-year study by the Land Development and Governance Institute (LDGI), in partnership with the International Development Research Centre (IDRC), Canada, to test the efficacy of the application of Kenya's new Community Land Act. The study sites are in Isiolo and Marsabit Counties, both in the Arid and Semi-Arid (ASAL) Northern Kenya. The study results demonstrate the importance of adequate sensitisation of the key actors (government, political and community) at county level and the grass root communities, the use of participatory and inclusive processes to establish the community governance organs and fulfil the statutory requirements provided under this new law. The study also highlights the importance of the use of community champions to ensure the continuous sensitisation of community members, and to to galvanise the communities in the registration and management of their land. Through the study, communities were supported to develop basic tools to guide them in land use planning and investor negotiations. The land use planning guide developed will help the communities to liase with the county government to prepare a land use development plan which is expected to enhance the sustainable use of the community land, while the investor negotiation guide developed will be helpful during negotiations with investors interested in partnering with the communities for investments on their land. The use of the investor guides is expected to inform the preparation of mutually beneficial investor agreements as anticipated under the Community Land Act. It is expected that the lessons from the study, which include: community empowerment, use of participatory inclusive processes, ensuring gender equity in the composition of governance organs and in decision making processes, embracing the youth, use of champions and avoiding the negative impacts of the adjudication of community land will be useful to state and non-state implementers of the new law, and may be used to inform the scaling up implementation countrywide. It is also expected that gaps identified in the new law, such as the management of the inheritance rights of children married outside the community, and those divorced, will inform law review.

Luis Carlos Rivera Monroy, Leonhard Rist, Martin Eberhardt et al.

Histopathology imaging is crucial for the diagnosis and treatment of skin diseases. For this reason, computer-assisted approaches have gained popularity and shown promising results in tasks such as segmentation and classification of skin disorders. However, collecting essential data and sufficiently high-quality annotations is a challenge. This work describes a pipeline that uses suspected melanoma samples that have been characterized using Multi-Epitope-Ligand Cartography (MELC). This cellular-level tissue characterisation is then represented as a graph and used to train a graph neural network. This imaging technology, combined with the methodology proposed in this work, achieves a classification accuracy of 87%, outperforming existing approaches by 10%.

Stefan Felsner, Andrew Nathenson, Csaba D. Tóth

A \emph{generic rectangular layout} (for short, \emph{layout}) is a subdivision of an axis-aligned rectangle into axis-aligned rectangles, no four of which have a point in common. Such layouts are used in data visualization and in cartography. The contacts between the rectangles represent semantic or geographic relations. A layout is weakly (strongly) \emph{aspect ratio universal} if any assignment of aspect ratios to rectangles can be realized by a weakly (strongly) equivalent layout. We give combinatorial characterizations for weakly and strongly aspect ratio universal layouts. Furthermore, we describe a quadratic-time algorithm that decides whether a given graph is the dual graph of a strongly aspect ratio universal layout, and finds such a layout if one exists.

X. Chen, S. Chen, T. Xu et al.

Traditional online map tiles, widely used on the Internet such as Google Map and Baidu Map, are rendered from vector data. Timely updating online map tiles from vector data, of which the generating is time-consuming, is a difficult mission. It is a shortcut to generate map tiles in time from remote sensing images, which can be acquired timely without vector data. However, this mission used to be challenging or even impossible. Inspired by image-to-image translation (img2img) techniques based on generative adversarial networks (GAN), we proposed a semi-supervised Generation of styled map Tiles based on Generative Adversarial Network (SMAPGAN) model to generate styled map tiles directly from remote sensing images. In this model, we designed a semi-supervised learning strategy to pre-train SMAPGAN on rich unpaired samples and fine-tune it on limited paired samples in reality. We also designed image gradient L1 loss and image gradient structure loss to generate a styled map tile with global topological relationships and detailed edge curves of objects, which are important in cartography. Moreover, we proposed edge structural similarity index (ESSI) as a metric to evaluate the quality of topological consistency between generated map tiles and ground truths. Experimental results present that SMAPGAN outperforms state-of-the-art (SOTA) works according to mean squared error, structural similarity index, and ESSI. Also, SMAPGAN won more approval than SOTA in the human perceptual test on the visual realism of cartography. Our work shows that SMAPGAN is potentially a new paradigm to produce styled map tiles. Our implementation of the SMAPGAN is available at https://github.com/imcsq/SMAPGAN.