Hasil untuk "Cartography"

Christian Bär

T. Horimoto, Y. Kawaoka

Jacek Malczewski, C. Rinner

The past twenty years have witnessed the emergence of alternative GIS (alt.gis) practices that are quite different from conventional GIS activities. Intellectually tied to the critical cartography pioneered by J.B. Harley in the late 1980s and early 1990s, alt.gis has evolved from early discussions on GIS & Society, critical GIS, PPGIS, feminist GIS, map stories/geonarratives, deep maps to qualitative GIS, neogeography, crowdsourcing, volunteered geographic information (VGI), geoweb, post-colonial computing, geogames, geodesign, and spatial humanities. This paper develops a preliminary framework to loosely knit together the diverse intellectual threads for Alt.gis. This paper argues that Daniel Pink’s framework for the six senses of the whole new mind (design, story, symphony, empathy, play, and meaning) captures the six major Alt.gis practices remarkably well. Mainstream GIS development has been predominantly concentrating on automated cartography/map-making, spatial modeling, geo-computation, and database development with the goal for efficiency, which tend to be closely associated with the left-side/slow thinking capabilities of the human brain. In contrast, Alt.gis focuses more on geo-narratives, qualitative/mixed methods, story-telling, and synthesis with the goal for achieving equity and social justice, which tend to be more closely associated with the right-side/fast thinking capabilities of the human brain. Evidently, Alt.gis has enabled GIS users to transcend what Heidegger called the enframing nature of technology and has led them explore new territories with greater sensitivities. In this sense, Alt.gis has become an enabling technology that continues to empower GIS users in their quest for a better, more equitable and sustainable world.

Shuaichen Yan, Xiao Hu, Jiayang Sun et al.

The explosive growth of the low-altitude economy, driven by eVTOLs and UAVs, demands a unified digital infrastructure to ensure safety and scalability. However, the current aviation vertical references are dangerously fragmented: manned aviation relies on barometric pressure, cartography uses Mean Sea Level (MSL), and obstacle avoidance depends on Above Ground Level (AGL). This fragmentation creates significant ambiguity for autonomous systems and hinders cross-stakeholder interoperability. In this article, we propose Height Above Ellipsoid (HAE) as the standardized vertical reference for lower airspace. Unlike legacy systems prone to environmental drift and inconsistent datums, HAE provides a globally consistent, GNSS-native, and mathematically stable reference. We present a pragmatic bidirectional transformation framework to bridge HAE with legacy systems and demonstrate its efficacy through (1) real-world implementation in Shenzhen's partitioned airspace management, and (2) a probabilistic risk assessment driven by empirical flight logs from the PX4 ecosystem. Results show that transitioning to HAE reduces the required vertical separation minimum, effectively increasing dynamic airspace capacity while maintaining a target safety level. This work offers a roadmap for transitioning from analog height keeping to a digital-native vertical standard.

Jarosław Czochański

W 2015 r. nastąpiły w polskim systemie prawnym zmiany stanowiące efekt opóźnionego wdrożenia zapisów dokumentu Europejskiej Konwencji Krajobrazowej z 2000 r., wprowadzające m.in. wykonanie audytów krajobrazowych na poziomie województw. Zmiany te wprowadziła Ustawa z dnia 24 kwietnia 2015 r. o zmianie niektórych ustaw w związku ze wzmocnieniem narzędzi ochrony krajobrazu – zwana powszechnie „ustawą krajobrazową”. Na potrzeby tego działania opracowana została nowa, odrębna metodyka i procedura badawcza, oparta na doświadczeniach badań krajobrazowych i kulturowych w Polsce, po raz pierwszy przyjęta w postaci aktu prawa – tj. Rozporządzenia Rady Ministrów. Na zakres audytu krajobrazowego składa się identyfikacja cech przyrodniczych i kulturowych krajobrazu, wyróżnienie jednostek krajobrazowych stanowiących jednolite typologicznie jednostki niższego rzędu od mikroregionów, wykonanie ich badania i ustrukturyzowanej charakterystyki, ocena walorów i zagrożeń oraz – na ich podstawie, wyznaczenie tzw. krajobrazów priorytetowych, wyróżniających się najwyższymi walorami pod względem przyrodniczym, kulturowym i fizjonomicznym. Dla tych krajobrazów następuje opracowanie wniosków i rekomendacji, służących ochronie ich walorów i zrównoważonemu wykorzystaniu ich przestrzeni. Mimo relatywnie długiego okresu przygotowania wymienionego rozporządzenia (ukazało się ono dopiero w 2019 r.) nie ustrzeżono się błędów merytorycznych, polegających na niepełnym zdefiniowaniu zróżnicowania typologicznego krajobrazów Polski, nieprecyzyjnym wskazaniu metod ich wyznaczania i budzącej zastrzeżenia procedurze wyznaczania krajobrazów priorytetowych. Cała metodyka jest na tyle skomplikowana i szczegółowa, że prace nad audytem krajobrazowym, do końca 2023 r. zakończyły jedynie dwa województwa. Pomimo przygotowania dobrych założeń merytorycznych w sferze naukowej, z doświadczeń wykonywania audytów w województwach dobitnie wynika nieprecyzyjne określenie metod wyznaczania i typologizacji jednostek krajobrazowych, błędne sformułowanie sformalizowanych metod służących wyznaczeniu krajobrazów priorytetowych oraz – w rezultacie – uzyskanie zróżnicowanych, niejednorodnych w skali kraju i niezadowalających wyników. Ze względu na bardzo szeroki zakres problemów przeprowadzenia audytu krajobrazowego w artykule skupiono się na ocenie jego pierwszej, zakończonej już części, podziału przestrzeni na jednostki krajobrazowe wg przyjętych kryteriów podziału i typologii oraz ocenie błędów i rozpoznanych problemów wdrażania procedur badawczych. Wnioskiem z tej oceny jest twierdzenie o potrzebie weryfikacji założeń metodycznych audytu, praktycznie na każdym etapie wprowadzonej procedury. Artykuł nie odwołuje się do analogicznych doświadczeń zagranicznych i ma charakter dyskusyjny, skupiając się na wykazaniu błędów i problemów, wymagających zdaniem autora naprawy.

Sagar Bharadwaj, Srinivasan Seshan, Anthony Rowe

The emergence of the Spatial Web -- the Web where content is tied to real-world locations has the potential to improve and enable many applications such as augmented reality, navigation, robotics, and more. The Spatial Web is missing a key ingredient that is impeding its growth -- a spatial naming system to resolve real-world locations to names. Today's spatial naming systems are digital maps such as Google and Apple maps. These maps and the location-based services provided on top of these maps are primarily controlled by a few large corporations and mostly cover outdoor public spaces. Emerging classes of applications, such as persistent world-scale augmented reality, require detailed maps of both outdoor and indoor spaces. Existing centralized mapping infrastructures are proving insufficient for such applications because of the scale of cartography efforts required and the privacy of indoor map data. In this paper, we present a case for a federated spatial naming system, or in other words, a federated mapping infrastructure. This enables disparate parties to manage and serve their own maps of physical regions and unlocks scalability of map management, isolation and privacy of maps. Map-related services such as address-to-location mapping, location-based search, and routing needs re-architecting to work on federated maps. We discuss some essential services and practicalities of enabling these services.

Miguel Ureña Pliego, Rubén Martínez Marín, Nianfang Shi et al.

This study explores the integration of machine learning into urban aerial image analysis, with a focus on identifying infrastructure surfaces for cars and pedestrians and analyzing historical trends. It emphasizes the transition from convolutional architectures to transformer-based pre-trained models, underscoring their potential in global geospatial analysis. A workflow is presented for automatically generating geospatial datasets, enabling the creation of semantic segmentation datasets from various sources, including WMS/WMTS links, vectorial cartography, and OpenStreetMap (OSM) overpass-turbo requests. The developed code allows a fast dataset generation process for training machine learning models using openly available data without manual labelling. Using aerial imagery and vectorial data from the respective geographical offices of Madrid and Vienna, two datasets were generated for car and pedestrian surface detection. A transformer-based model was trained and evaluated for each city, demonstrating good accuracy values. The historical trend analysis involved applying the trained model to earlier images predating the availability of vectorial data 10 to 20 years, successfully identifying temporal trends in infrastructure for pedestrians and cars across different city areas. This technique is applicable for municipal governments to gather valuable data at a minimal cost.

Tingying He, Jason Dykes, Petra Isenberg et al.

We present a new comprehensive theory for explaining, exploring, and using pattern as a visual variable in visualization. Although patterns have long been used for data encoding and continue to be valuable today, their conceptual foundations are precarious: the concepts and terminology used across the research literature and in practice are inconsistent, making it challenging to use patterns effectively and to conduct research to inform their use. To address this problem, we conduct a comprehensive cross-disciplinary literature review that clarifies ambiguities around the use of "pattern" and "texture". As a result, we offer a new consistent treatment of pattern as a composite visual variable composed of structured groups of graphic primitives that can serve as marks for encoding data individually and collectively. This new and widely applicable formulation opens a sizable design space for the visual variable pattern, which we formalize as a new system comprising three sets of variables: the spatial arrangement of primitives, the appearance relationships among primitives, and the retinal visual variables that characterize individual primitives. We show how our pattern system relates to existing visualization theory and highlight opportunities for visualization design. We further explore patterns based on complex spatial arrangements, demonstrating explanatory power and connecting our conceptualization to broader theory on maps and cartography. An author version and additional materials are available on OSF: osf.io/z7ae2.

Chenxing Sun, Yongyang Xu, Xuwei Xu et al.

Multi-scale tile maps are essential for geographic information services, serving as fundamental outcomes of surveying and cartographic workflows. While existing image generation networks can produce map-like outputs from remote sensing imagery, their emphasis on replicating texture rather than preserving geospatial features limits cartographic validity. Current approaches face two fundamental challenges: inadequate integration of cartographic generalization principles with dynamic multi-scale generation and spatial discontinuities arising from tile-wise generation. To address these limitations, we propose a scale-aware cartographic generation framework (SCGM) that leverages conditional guided diffusion and a multi-scale cascade architecture. The framework introduces three key innovations: a scale modality encoding mechanism to formalize map generalization relationships, a scale-driven conditional encoder for robust feature fusion, and a cascade reference mechanism ensuring cross-scale visual consistency. By hierarchically constraining large-scale map synthesis with small-scale structural priors, SCGM effectively mitigates edge artifacts while maintaining geographic fidelity. Comprehensive evaluations on cartographic benchmarks confirm the framework's ability to generate seamless multi-scale tile maps with enhanced spatial coherence and generalization-aware representation, demonstrating significant potential for emergency mapping and automated cartography applications.

Joshua Könen, Heiko Röglin, Tarek Stuck

Dynamic programming over tree decompositions is a common technique in parameterized algorithms. In this paper, we study whether this technique can also be applied to compute Pareto sets of multiobjective optimization problems. We first derive an algorithm to compute the Pareto set for the multicriteria s-t cut problem and show how this result can be applied to a polygon aggregation problem arising in cartography that has recently been introduced by Rottmann et al. (GIScience 2021). We also show how to apply these techniques to also compute the Pareto set of the multiobjective minimum spanning tree problem and for the multiobjective TSP. The running time of our algorithms is $O(f(w)\cdot\mathrm{poly}(n,p_{\text{max}}))$, where $f$ is some function in the treewidth $w$, $n$ is the input size, and $p_{\text{max}}$ is an upper bound on the size of the Pareto sets of the subproblems that occur in the dynamic program. Finally, we present an experimental evaluation of computing Pareto sets on real-world instances of polygon aggregation problems. For this matter we devised a task-specific data structure that allows for efficient storage and modification of large sets of Pareto-optimal solutions. Throughout the implementation process, we incorporated several improved strategies and heuristics that significantly reduced both runtime and memory usage, enabling us to solve instances with treewidth of up to 22 within reasonable amount of time. Moreover, we conducted a preprocessing study to compare different tree decompositions in terms of their estimated overall runtime.

Caleb Ziems, William Held, Jane Yu et al.

To serve global users safely and productively, LLMs need culture-specific knowledge that might not be learned during pre-training. How do we find such knowledge that is (1) salient to in-group users, but (2) unknown to LLMs? The most common solutions are single-initiative: either researchers define challenging questions that users passively answer (traditional annotation), or users actively produce data that researchers structure as benchmarks (knowledge extraction). The process would benefit from mixed-initiative collaboration, where users guide the process to meaningfully reflect their cultures, and LLMs steer the process towards more challenging questions that meet the researcher's goals. We propose a mixed-initiative methodology called CultureCartography. Here, an LLM initializes annotation with questions for which it has low-confidence answers, making explicit both its prior knowledge and the gaps therein. This allows a human respondent to fill these gaps and steer the model towards salient topics through direct edits. We implement this methodology as a tool called CultureExplorer. Compared to a baseline where humans answer LLM-proposed questions, we find that CultureExplorer more effectively produces knowledge that leading models like DeepSeek R1 and GPT-4o are missing, even with web search. Fine-tuning on this data boosts the accuracy of Llama-3.1-8B by up to 19.2% on related culture benchmarks.

Shayan Vassef, Amirhossein Dabiriaghdam, Mohammadreza Bakhtiari et al.

This work investigates the impact of multi-task, multi-lingual, and multi-source learning approaches on the robustness and performance of pretrained language models. To enhance this analysis, we introduce Subsets of Interest (SOI), a novel categorization framework that identifies six distinct learning behavior patterns during training, including forgettable examples, unlearned examples, and always correct examples. Through SOI transition heatmaps and dataset cartography visualization, we analyze how examples shift between these categories when transitioning from single-setting to multi-setting configurations. We perform comprehensive experiments across three parallel comparisons: multi-task vs. single-task learning using English tasks (entailment, paraphrase, sentiment), multi-source vs. single-source learning using sentiment analysis datasets, and multi-lingual vs. single-lingual learning using intent classification in French, English, and Persian. Our results demonstrate that multi-source learning consistently improves out-of-distribution performance by up to 7%, while multi-task learning shows mixed results with notable gains in similar task combinations. We further introduce a two-stage fine-tuning approach where the second stage leverages SOI-based subset selection to achieve additional performance improvements. These findings provide new insights into training dynamics and offer practical approaches for optimizing multi-setting language model performance.

Johan Linåker, Cailean Osborne, Jennifer Ding et al.

The proliferation of open large language models (LLMs) is fostering a vibrant ecosystem of research and innovation in artificial intelligence (AI). However, the methods of collaboration used to develop open LLMs both before and after their public release have not yet been comprehensively studied, limiting our understanding of how open LLM projects are initiated, organized, and governed as well as what opportunities there are to foster this ecosystem even further. We address this gap through an exploratory analysis of open collaboration throughout the development and reuse lifecycle of open LLMs, drawing on semi-structured interviews with the developers of 14 open LLMs from grassroots projects, research institutes, startups, and Big Tech companies in North America, Europe, Africa, and Asia. We make three key contributions to research and practice. First, collaboration in open LLM projects extends far beyond the LLMs themselves, encompassing datasets, benchmarks, open source frameworks, leaderboards, knowledge sharing and discussion forums, and compute partnerships, among others. Second, open LLM developers have a variety of social, economic, and technological motivations, from democratizing AI access and promoting open science to building regional ecosystems and expanding language representation. Third, the sampled open LLM projects exhibit five distinct organizational models, ranging from single company projects to non-profit-sponsored grassroots projects, which vary in their centralization of control and community engagement strategies used throughout the open LLM lifecycle. We conclude with practical recommendations for stakeholders seeking to support the global community building a more open future for AI.

Vahid Isazade, Abdul Baser Qasimi, Abdulla Al Kafy et al.

Flood events are the most sophisticated and damaging natural hazard compared to other natural catastrophes. Every year, this hazard causes human-financial losses and damage to croplands in different locations worldwide. This research employs a combination of artificial neural networks and geographic information systems (GIS) to simulate flood-vulnerable locations in the Samangan Province of Afghanistan. First, flood-influencing factors, such as soil, slope layer, elevation, flow direction, and land use/cover, were evaluated as influential factors in simulating flood-prone areas. These factors were imported into GIS software. The Fishnet command was used to partition the information layers. Furthermore, each layer was converted into points, and this data was fed into the perceptron neural network along with the educational data obtained from Google Earth. In the perceptron neural network, the input layers have five neurons and 16 nodes, and the outputs showed that elevation had the lowest possible weight (R2 = 0.713) and flow direction had the highest weight (R2 = 0.913). This study demonstrated that combining GIS and artificial neural networks results in acceptable performance for simulating and modeling flood susceptible areas in different geographical locations and significantly helps prevent or reduce flood hazards.

Oluibukun Gbenga Ajayi, Ifeanyi Jonathan Nwadialor

This research seeks to assess the effect of different selected feature descriptors on the accuracy of an automatic image registration scheme. Three different feature descriptors were selected based on their peculiar characteristics, and implemented in the process of developing the image registration scheme. These feature descriptors (Modified Harris and Stephens corner detector (MHCD), the Scale Invariant Feature Transform (SIFT) and the Speeded Up Robust Feature (SURF)) were used to automatically extract the conjugate points common to the overlapping image pairs used for the registration. Random Sampling Consensus (RANSAC) algorithm was used to exclude outliers and to fit the matched correspondences, Sum of Absolute Differences (SAD) which is a correlation-based feature matching metric was used for the feature match, while projective transformation function was used for the computation of the transformation matrix (T). The obtained overall result proved that the SURF algorithm outperforms the other two feature descriptors with an accuracy measure of -0.0009 pixels, while SIFT with a cumulative signed distance of 0.0328 pixels also proved to be more accurate than MHCD with a cumulative signed distance of 0.0457 pixels. The findings affirmed the importance of choosing the right feature descriptor in the overall accuracy of an automatic image registration scheme.

Ziyu Yan, Bin Sun, Zhihai Gao et al.

As the most extensive temperate grassland in the world, the Eurasian Steppe provides various ecological services that support the environment and human well-being. However, grassland degradation has become a serious environmental issue. Most of the traditional degradation assessments ignore the sensitivity of grassland ecosystems to climatic conditions. In response, our study introduces a new comprehensive identification framework that integrates vegetation growth and climate change, using a novel long-term monitoring methodology to detect grassland degradation and improvement. The framework quantifies the area and degree of degradation and improvement in the Eurasian Steppe using long time-series data from 2000 − 2020. Then, the driving factors of grassland change were analyzed using a quantitative model. Our findings reveal a clear trend of improvement in the Eurasian Steppe was identified, with the improved area being 4.72 times larger than the degraded area (221.4 × 104 and 46.92 × 104 km2, respectively). The Tibetan Plateau and Loess Plateau led to the improvement. Simultaneously, the area surrounding the northern Caspian Sea has been severely degraded. The three areas correspond to frigid humid and semi-humid grassland, temperate humid and semi-humid grassland, and temperate arid and semi-arid grassland, respectively. Globally, the combined effects of climate change and human activities dominated the observed grassland degradation and improvement, accounting for 77.13% and 89.64%, respectively. Our method provides a robust tool for detecting grassland degradation and improvement across large scales, offering scientific support for achieving the United Nations’ Sustainable Development Goals (SDGs), particularly land degradation neutrality (LDN).

Tawny Sit, David H. Weinberg, Adam Wheeler et al.

Stellar abundance measurements are subject to systematic errors that induce extra scatter and artificial correlations in elemental abundance patterns. We derive empirical calibration offsets to remove systematic trends with surface gravity $\log(g)$ in 17 elemental abundances of 288,789 evolved stars from the SDSS APOGEE survey. We fit these corrected abundances as the sum of a prompt process tracing core-collapse supernovae and a delayed process tracing Type Ia supernovae, thus recasting each star's measurements into the amplitudes $A_{\text{cc}}$ and $A_{\text{Ia}}$ and the element-by-element residuals from this two-parameter fit. As a first application of this catalog, which is $8\times$ larger than that of previous analyses that used a restricted $\log(g)$ range, we examine the median residual abundances of 14 open clusters, nine globular clusters, and four dwarf satellite galaxies. Relative to field Milky Way disk stars, the open clusters younger than 2 Gyr show $\approx 0.1-0.2$ dex enhancements of the neutron-capture element Ce, and the two clusters younger than 0.5 Gyr also show elevated levels of C+N, Na, S, and Cu. Globular clusters show elevated median abundances of C+N, Na, Al, and Ce, and correlated abundance residuals that follow previously known trends. The four dwarf satellites show similar residual abundance patterns despite their different star formation histories, with $\approx 0.2-0.3$ dex depletions in C+N, Na, and Al and $\approx 0.1$ dex depletions in Ni, V, Mn, and Co. We provide our catalog of corrected APOGEE abundances, two-process amplitudes, and residual abundances, which will be valuable for future studies of abundance patterns in different stellar populations and of additional enrichment processes that affect galactic chemical evolution.

Nina Haket, Ryan Daniels

Conceptual Engineers want to make words better. However, they often underestimate how varied our usage of words is. In this paper, we take the first steps in exploring the contextual nuances of words by creating conceptual landscapes -- 2D surfaces representing the pragmatic usage of words -- that conceptual engineers can use to inform their projects. We use the spoken component of the British National Corpus and BERT to create contextualised word embeddings, and use Gaussian Mixture Models, a selection of metrics, and qualitative analysis to visualise and numerically represent lexical landscapes. Such an approach has not yet been used in the conceptual engineering literature and provides a detailed examination of how different words manifest in various contexts that is potentially useful to conceptual engineering projects. Our findings highlight the inherent complexity of conceptual engineering, revealing that each word exhibits a unique and intricate landscape. Conceptual Engineers cannot, therefore, use a one-size-fits-all approach when improving words -- a task that may be practically intractable at scale.

Quintino Francesco Lotito, Federico Musciotto, Alberto Montresor et al.

Many networks can be characterised by the presence of communities, which are groups of units that are closely linked. Identifying these communities can be crucial for understanding the system's overall function. Recently, hypergraphs have emerged as a fundamental tool for modelling systems where interactions are not limited to pairs but may involve an arbitrary number of nodes. In this study, we adopt a dual approach to community detection and extend the concept of link communities to hypergraphs. This extension allows us to extract informative clusters of highly related hyperedges. We analyze the dendrograms obtained by applying hierarchical clustering to distance matrices among hyperedges across a variety of real-world data, showing that hyperlink communities naturally highlight the hierarchical and multiscale structure of higher-order networks. Moreover, hyperlink communities enable us to extract overlapping memberships from nodes, overcoming limitations of traditional hard clustering methods. Finally, we introduce higher-order network cartography as a practical tool for categorizing nodes into different structural roles based on their interaction patterns and community participation. This approach aids in identifying different types of individuals in a variety of real-world social systems. Our work contributes to a better understanding of the structural organization of real-world higher-order systems.

S. Schäfer