Hasil untuk "Cartography"

David Weimer

This issue of the Bibliography includes items published from 2015 to 2018. The form of entries reflects the order and punctuation conventions of ISBD (M) : International Standard Bibliographic Description for Monographic Publications, rev. ed. (s.l. : International Federation of Library Associations and Institutions, 2002. — http://www.ifla.org/VII/s13/pubs/isbd_m0602.pdf), but with modifications to accommodate articles in journals and collective works. Some English translations or paraphrases of titles have been supplied. The abbreviation ‘ill.’ alone implies illustration(s) of a cartographic nature; where both ‘ill.’ and ‘maps’ occur together illustrations of a general and of a cartographic nature are included.

Weihao Shen, Ailong Ma, Zhuo Zheng et al.

Referring remote-sensing image segmentation (RRSIS) aims to accurately localize and delineate ground targets within remote-sensing imagery conditioned on natural language expressions. This task fundamentally relies on the effective fusion of visual and language modalities, typically implemented through multimodal encoders and task-specific decoders. Existing frameworks struggle to achieve precise alignment between remote-sensing image visual features and natural language referring expressions, as the target results of referring image segmentation tasks focus on tiny objects in local areas of interest, while the spatial scale of remote-sensing images is complex and variable and referring expressions are complex natural languages, making it difficult to perform effective visual language alignment. We propose the spiral selective scan visual state space model (RSRefMa), which enhances the alignment of visual and language features from the perspective of enhancing global contextual multimodal understanding of remote-sensing images and natural language referring expressions. Specifically, we propose a state space model based on spiral scanning to effectively capture globally representative visual features, thereby enhancing the model’s capability for global visual context modeling. To enrich and diversify referring expressions, we leverage the advanced contextual understanding and language reasoning capabilities of large language models. This augmentation leads to semantically rich and diverse expressions, strengthening the model’s comprehension of both visual and language modalities and ensuring more precise cross-modal alignment. Furthermore, to address the difficulty of fine-grained segmentation of tiny, weak geo-objects in remote-sensing image, we integrate dual multi-scale visual prompts enabling a comprehensive representation of foreground and background features for accurate boundary segmentation. This design enhances the alignment between visual and language features, thereby enabling more precise target localization. Extensive experiments on two referring remote-sensing image segmentation datasets demonstrate that our proposed RSRefMa method has superior understanding and segmentation performance compared to previous state-of-the-art methods.

Rob Kitchin

Xinghua Cheng, Zhilin Li

Multispectral remote sensing images (MRSI) contain rich information about geographical objects and phenomena, such as land use and land cover. To extract such information, classification is normally carried out to yield land use and land cover maps (LULCM). A lot of techniques have been developed for classification, yet such a fundamental problem has not been solved as mathematical models for predicting the upper and lower limits of land cover classification accuracy with a given MRSI. This study aims to tackle this key problem by considering classification as an explicit information transfer process from images to maps and then build a mathematical model (Boltzmann-entropy-based) for the process with Shannon’s information theory and Crooks’ Thermodynamic Fluctuation as theoretical foundation. The model is designed to predict both upper and lower limits of classification accuracy instead of a definite value and is expressed in terms of Boltzmann entropies of MRSI and LULCM, total number of classes, and two basic parameters defined by prior knowledge. Verification experiments are carried out with 1091 images and three well-established classifiers (support vector machine, random forests, and K-nearest neighbors). The results demonstrate that (i) the values of information in MRSI and LULCM are strongly correlated, and (ii) the Boltzmann-entropy-based model can predict both upper and lower limits of classification accuracy. This study provides a novel perspective for understanding land cover classification and opens the door for the establishment of new theories in remote sensing.

Zeyuan Zhang, Wei Feng, Min Zhong et al.

High-resolution (HR) remote sensing images contain valuable information, and drone imagery typically offers higher resolution than satellite images. However, UAVs face challenges in cost and resource consumption when covering large areas. Deep learning techniques have been widely used for super-resolution (SR) reconstruction, but existing algorithms based on bicubic interpolation perform poorly, especially in heterogeneous remote sensing images. Although both generative adversarial networks (GANs) and transformers show potential in image super-resolution, few studies combine these two techniques. To address these challenges, we propose a blind super-resolution framework that integrates the strengths of both Transformer and GAN, aiming to improve model adaptability to multi-source remote sensing data. The key contributions include: (1) a random mixed degradation modeling method, analyzing the impact of real paired data and synthetic degraded data, and revealing the influence of time differences on reconstruction quality; (2) introducing transformer into the generator network to enhance modeling of long-range spatial correlations; (3) designing a color consistency constraint algorithm based on feature space alignment to improve model generalization. Experimental results show that the proposed method achieves significant performance improvements in heterogeneous remote sensing image reconstruction.

Tony Sampaio, Jorge Rocha, Cláudia M. Viana et al.

Various tests can be used to assess whether the spatial distribution pattern of a set of points is random, dispersed, or clustered. These tests typically compare the expected and observed distances among points, assuming no barriers. However, what is deemed ``random'’ in point spatial patterns may be influenced by socio-environmental factors such as wetlands or transportation networks. This tool introduces a sequence of spatial analysis procedure and a statistical testing to evaluate the correlation between observed point patterns and potential spatial determinants (polygons). If a determinant influences the observed point pattern, the classification of the distribution as random must be reconsidered. We implemented this algorithm in Python as a QGIS script with two main steps: the first handles overlay operations and preliminary calculations for the chi-square goodness-of-fit test with and without Bonferroni correction in the second step. • A detailed step-by-step procedure for analyzing randomness in point patterns in a processing toolbox Python script for integration into the open-source software QGIS. • Automated scripts for structuring data, calculating expected and observed values, and applying the chi-square goodness-of-fit test for statistical analysis. • Advanced spatial analysis using chi-square goodness of fit coupled with and without Bonferroni correction, providing deeper insight into the study of the effect of spatial phenomena on the distribution of point events.

Ana María Faggi

La “Guía introductoria a los Sistemas de Información Geográfica en QGIS: herramientas básicas y aplicaciones ambientales” es un trabajo oportuno y accesible para la formación ambiental y territorial. Mientras el uso de tecnologías geoespaciales es un recurso cada vez presente en la gestión pública, la educación y la investigación, es limitado el material en español que combine claridad didáctica, enfoque ambiental y uso de herramientas informáticas libres. En ese sentido, esta guía ofrece una propuesta pensada tanto para personas que se inician con los Sistemas de Información Geográfica (SIG) como para quienes ya han tenido algún contacto con estos programas y buscan profundizar su práctica desde una perspectiva aplicada. ....

B. Archinal, M. A’Hearn, E. Bowell et al.

S. Martínez, Á. Peñas, T. D. González et al.

J. Mennis

Abstract Aggregated demographic datasets are associated with analytical and cartographic problems due to the arbitrary nature of areal unit partitioning. This article describes a methodology for generating a surface-based representation of population that mitigates these problems. This methodology uses dasymetric mapping and incorporates areal weighting and empirical sampling techniques to assess the relationship between categorical ancillary data and population distribution. As a demonstration, a 100-meter-resolution population surface is generated from U.S. Census block group data for the southeast Pennsylvania region. Remote-sensing-derived urban land-cover data serve as ancillary data in the dasymetric mapping. *The author would like to thank Cory Eicher and Alan MacEacren for many helpful conversations regarding dasymetric mapping and Barbara Buttenfield for her constructive comments on a previous draft of this article.

Liyuan Liu, Zhicheng Xu, Shenjun Yao et al.

Recent studies on the accessibility of sports facilities have rarely considered the specific attributes of the facilities, limiting their ability to define service potential, and have often neglected the critical aspect of equitable access. This study proposed a novel approach based on remote sensing images to optimize the spatial accessibility of outdoor sports facilities. Using Shanghai, China, as the study area, the study identified four types of sports facilities using a deep learning object detection method, which allowed their service capacities (areas) to be measured more precisely. A greedy heuristic algorithm was then developed based on a "trade-off" strategy that seeks to optimize facility access by reconciling the objectives of enhancing access and ensuring equality and by weighing the benefits of utilizing existing resources (school facilities) against the necessity of developing new ones. The object detection method achieved precision and recall rates of 88% and 96%, respectively, and the optimization efforts resulted in a 73% increase in accessibility while also significantly reducing the Gini coefficient from 0.58 to 0.34. The proposed algorithm outperformed the random selection and all-school-opening strategies. The results indicated that the methodology can effectively create refined datasets for outdoor sports facilities and enhance their accessibility.

Quan Duan, Ronggao Liu, Jilong Chen et al.

Burned area (BA) plays a pivotal role in fire management and the assessment of fire impact on earth-atmosphere system. Threshold-based segmentation from a single image is an efficient and operational method for detecting BA. However, the great diversity of fire conditions necessitates an adaptive threshold that considers environmental variations. This paper presents a maximum curvature segmentation method to capture the adaptative thresholds. The spectral contrasts in near-infrared (NIR) and shortwave infrared (SWIR) bands were utilized to distinguish BA. The decreased NIR threshold was employed to obtain the burned candidates, and the increased SWIR threshold was then applied to confirm the candidates. Experiments were conducted in different biomes, covering the boreal forest, tropical forest, savanna, and Mediterranean, and different seasons including growing and non-growing seasons. The thresholds changed in each tile, indicating the algorithm adapted the spatial and temporal variations. Comparison with the Burned Area Reference Database was performed at different biomes, resulting in overall dice coefficient (DC), omission error (OE), commission error (CE), and relative Bias (relB) being 0.86, 0.18, 0.10, and −0.08, respectively. The algorithm provides an avenue for adaptive detection of burned areas, and the single-image based approach can provide real-time burned information for wildfire management systems.

Zhaoyin Liu, Yangyang Li, Andrea Law et al.

In the wake of the COVID-19 pandemic, global efforts to mitigate the spread of the virus have led to widespread lockdowns and movement restrictions. Earlier studies have reported a notable positive correlation between nitrogen dioxide (NO2) levels and mobility during the initial 2020 lockdowns. However, our explorative investigation in Southeast Asia observed that, despite their similar spatial distribution, NO2 does not consistently align with mobility patterns. This observation indicates the existence of additional influential factors apart from mobility contributing to NO2 variation, necessitating a more comprehensive examination. Subsequently, we developed a trained Multi-Layer Perceptron (MLP) model and leveraged SHapley Additive exPlanations (SHAP) values. Our analysis extends beyond mobility to encompass diverse potential factors such as travel modes and meteorological factors. The model results suggest that, while as expected mobility has a strong impact on NO2 column density, a more accurate prediction requires considering different travel modes (i.e. driving and walking). Furthermore, the study reveals that spatio-temporal heterogeneity and meteorological factors also play roles in shaping NO2 level. These findings emphasize the importance of integrating such multifaceted considerations in future NO2 study for a more accurate and holistic understanding.

Sarah Kaplan

J. Blackard, D. Dean

Yang Zheng, Hao Deng, Jingjie Wu et al.

Geographical information systems (GIS) are essential tools for mineral prospectivity modeling (MPM). Three-dimensional (3D) MPM is able to learn the association between geological evidence and mineralization in shallow zones and thereby build a prospectivity model for deep zones, making it a desirable technique to target deep-seated orebodies. However, existing 3D MPM methods directly generalize the model learned in shallow zones to the deep zones without attention to model transferability caused by the different metallogenic mechanisms between the two zones. In this study, we aim to robustly transfer the prospectivity model learned from shallow zones to deep zones. We cast the 3D MPM as a domain adaptation problem, which is an important realm of transfer learning. Because the metallogenic mechanism can be closely associated with spatial locations, we specifically focus on domain adaption concerning the spatial locations that are ignored by conventional domain adaptation methods. To measure the spatial-associated domain discrepancy, we propose a novel spatial-associated maximum mean discrepancy (SAMMD), which compares the joint distributions of features and spatial locations across domains. Based on the SAMMD criterion, a deep neural network, referred to as the spatial-associated domain adaptation network, is devised to learn cross-domain but mineralization-indicative features for building prospectivity model that is transferable to deep zones. A case study of the world-class Sanshandao gold deposit, in eastern China, was carried out to validate the effectiveness of the proposed methods. The results show that compared with other leading MPM methods and other domain adaption variants, the proposed method has superior prediction accuracy and targeting efficiency, demonstrating the effectiveness and robustness of the proposed method in targeting deep-seated orebodies in areas with different metallogenic mechanisms and no labeled data.

J. Corner

R. Rose-Redwood, N. Barnd, A. Lucchesi et al.

ABSTRACT:For over five centuries, cartographic map-making has played a pivotal role as a political technology of empire-building, settler colonialism, and the dispossession of Indigenous lands. Yet Indigenous peoples themselves have long engaged in their own mapping practices to share ancestral knowledge, challenge colonial rule, and reclaim Indigenous “place-worlds.” Although there is now a sizable body of scholarly literature on the mapping of empire, this special issue on “Decolonizing the Map” aims to recenter Indigenous mappings and decolonial cartographies as spatial practices of world-making. In this introductory article, we provide an overview of the theory and praxis of decolonial mapping and outline the key themes of the contributions to the present special issue. Drawing upon insights from this edited collection, we conclude that decolonial mapping requires a recentering of Indigenous geographical knowledge, respect for Indigenous protocols, and the active participation of Indigenous peoples in the mapping process itself if the project of decolonizing the map is to truly move beyond the colonial cartographic frame.RÉSUMÉ:Depuis plus de cinq siècles, la cartographie joue un rôle primordial à titre de technologie politique dans la constitution d’empires, le colonialisme de peuplement et la dépossession des peuples autochtones de leurs terres. Pourtant, les peuples autochtones ont eux-mêmes une longue tradition de pratiques cartographiques visant le partage des connaissances ancestrales, la contestation du pouvoir colonial et la récupération des « univers de lieux » autochtones. Bien qu’il existe maintenant un corpus de littérature assez substantiel sur la cartographie des empires, le numéro spécial ici proposé sur la « décolonisation de la cartographie » a pour but de recentrer les cartographies autochtones et les cartographies décoloniales comme pratiques spatiales de construction du monde. Les auteurs du présent article d’introduction donnent un aperçu de la théorie et de la pratique de la cartographie décoloniale et une description des principaux thèmes abordés dans les articles qui composent ce numéro. Des idées exprimées dans ce recueil, ils concluent que la cartographie décoloniale exige un recentrage des connaissances géographiques autochtones, le respect des protocoles autochtones, et la participation active des peuples autochtones au processus cartographique, si tant est que le projet de décolonisation de la cartographie doive véritablement dépasser le cadre de la cartographie coloniale.

Marius Hogräfer, M. Heitzler, Hans-Jörg Schulz

Cartographic maps have been shown to provide cognitive benefits when interpreting data in relation to a geographic location. In visualization, the term map‐like describes techniques that incorporate characteristics of cartographic maps in their representation of abstract data. However, the field of map‐like visualization is vast and currently lacks a clear classification of the existing techniques. Moreover, choosing the right technique to support a particular visualization task is further complicated, as techniques are scattered across different domains, with each considering different characteristics as map‐like. In this paper, we give an overview of the literature on map‐like visualization and provide a hierarchical classification of existing techniques along two general perspectives: imitation and schematization of cartographic maps. Each perspective is further divided into four principal categories that group common map‐like techniques along the visual primitives they affect. We further discuss this classification from a task‐centered view and highlight open research questions.

Derek H. Alderman, Joshua F. J. Inwood, Ethan Bottone

Abstract Responding to recent work in critical cartographic studies and Black Geographies, the purpose of this paper is to offer a conceptual framework and a set of evocative cartographic engagements that can inform geography as it recovers the seldom discussed history of counter-mapping within the African American Freedom Struggle. Black resistant cartographies stretch what constitutes a map, the political work performed by maps, and the practices, spaces, and political-affective dimensions of mapping. We offer an extended illustration of the conventional and unconventional mapping behind USA anti-lynching campaigns of the late 19th and early 20th centuries, highlighting the knowledge production practices of the NAACP and the Tuskegee Institute’s Monroe Work, and the embodied counter-mapping of journalist/activist Ida B. Wells. Recognizing that civil rights struggles are long, always unfolding, and relationally tied over time and space, we link this look from the past to contemporary, ongoing resistant cartographical practices as scholars/activists continue to challenge racialized violence and advance transitional justice, including the noted memory-work of the Equal Justice Initiative. An understanding of African American traditions of counter-mapping is about more than simply inserting the Black experience into our dominant ideas about cartography or even resistant mapping. Black geographies has much to teach cartography and geographers about what people of color engaged in antiracist struggles define as geographic knowledge and mapping practices on their own terms—hopefully provoking a broader and more inclusive definition of the discipline itself.