Hasil untuk "Cartography"

Yu Qiu, Wen Wang, Zhansheng Ji et al.

The Joint Research Centre Monthly Water History (JRC MWH) dataset has been widely applied in global water resource studies but suffers from data gaps, anomalies, and poor timeliness. This study constructs a Near-Real-Time Long-Term Monthly Water Body Dataset (NMWBD) by fusing JRC MWH and Sentinel data, and applies it to reservoir monitoring in the Fenshui River Basin, southeastern China. Data gaps are processed with preliminary gap-filling using dynamic thresholds method, followed by fine-tuning gap-filling with a deep learning model which combines Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM). To address data anomalies, a sliding-window anomaly detection method is used, which are then corrected using adjacent normal months. To improve the timeliness of JRC MWH, Sentinel-1 and Sentinel-2 images are used to extract recent monthly water bodies with a threshold-based segmentation algorithm, using the SDWI and MNDWI indices in combination with Otsu’s method. Finally, the NMWBD, covering the period from 1990 to 2023, is constructed by concatenating processed historical JRC MWH data (1990–2021) with recent water body data extracted from Sentinel images (2019–2023), with the overlapping years (2019–2021) directly replaced by the latter. The dataset’s accuracy is validated through visually collected water body samples from Google Earth images, with an average Kappa coefficient of 0.85. Comparisons between ground-observed reservoir water levels and extracted water surface areas of reservoirs also demonstrate high consistency, with a Pearson correlation coefficient exceeding 0.85 and a coefficient of determination above 0.8. NMWBD is employed to identify reservoirs in Fenshui River Basin. Reservoirs larger than 0.1 km2 are fully identified, while smaller ones achieve a true positive rate of 0.91. The variations in extracted water surface area accurately reflect the long-term trends of reservoirs in the basin, and capture annual and seasonal fluctuations driven by both precipitation variations and reservoir operations.

Ngoc Quy Bui, Anh Quan Duong, Quoc Long Nguyen et al.

This article aims to develop an improved algorithm for classification of point cloud data. The primary component of this algorithm is determination of the classification thresholds for different geographical objects, which helps in the automatic classification of the LiDAR point cloud data. The algorithm was tested to classify the point cloud of three different areas of Ha Long city in Quang Ninh province. The results from the three areas show that for the ground points our algorithm is on average 6.4% more accurate than the traditional progressive TIN densification (PTD) algorithm. Further, with the proposed point cloud classification algorithms the average accuracy for asphalt roads is 87.77%, 98.09% for vegetation, and 96.86% for roof objects. The classified roof objects were further processed for house digitization, which provided an average accuracy of 92.07%. The whole dataset was used to develop 3D city models of the three areas (A1, A2 and A3 in Figure 7) in Hon Gai ward, Ha Long city with Level of Detail (LoD) 2.

Soner Uereyen, Igor Klein, Christina Eisfelder et al.

With temperatures in Central Asia (CA) increasing more than the global average, this region is one of the global hotspots affected by climate change. CA is mostly characterized by arid climate, which is why available water resources are of paramount importance for the societies, economies, and the environment. In this regard, quantifying changes on the land surface and controlling factors that influence land surface dynamics are of great interest to improve our understanding of climate change impacts in this region. Hence, this study analyzes multivariate time series covering climatic, hydrological and Earth observation (EO)-based land surface variables. The used EO time series characterize the land surface and include data on the normalized difference vegetation index (NDVI), surface water area (SWA), and snow cover area (SCA) between December 2002 to November 2021. To analyze these time series, we employ trend analyses and a causal discovery algorithm. Both analyses were carried out at multiple spatial and temporal scales. The results show that NDVI trends were mostly significantly negative in the Northwest and positive in the Northeast of CA in summer. In summer and autumn, the percentage of significant negative NDVI trends outweighed the positive trends. For SWA, the detected trends were mostly significant negative throughout all scales. Significant negative trends were retrieved for SCA across all seasons, except for autumn regionally. Particularly the Tian Shan and Pamir mountains show significant declines of SCA in winter and spring. The causal analyses revealed that the NDVI is mostly controlled by water availability in summer. In spring and autumn, temperature is the leading driver on the NDVI. Likewise, temperature is found to largely control SWA in spring and autumn. SCA is mostly negatively coupled to temperature during spring and autumn. A positive coupling between SCA and precipitation is identified in winter.

Fang Chen, Yao Sun, Lei Wang et al.

Efficient building damage assessment after disasters is vital for emergency response and loss evaluation, but the task is complicated by diverse building structures and complex environments. Traditional methods using Convolutional Neural Networks (CNNs) struggle to capture global contextual features, limiting damage categorization accuracy. To address this, we introduce the High-Resolution Transformer Architecture for Building Damage Assessment (HRTBDA), which enhances multi-scale feature extraction. A Cross-Attention-Based Spatial Fusion (CSF) module is proposed to utilize the attention mechanism, improving the model’s ability to identify detailed associations in damaged buildings. Additionally, we propose a deep convolution network matching optimization strategy that integrates a multilayer perceptron and expands the receptive field, enhancing global feature perception. HRTBDA’s performance was evaluated on two public datasets and compared with five recent frameworks. The model achieved an F1-score of 86.0% in building localization and 78.4% in damage assessment, with a 4.8% improvement in detecting minor damages. These results demonstrate HRTBDA’s potential for improving building damage assessment and highlight its significant advancements over existing methods.

Hongen Wang, Liyang Xiong, Guanghui Hu et al.

ABSTRACTDeep learning-based super-resolution is an essential technique for acquiring high-resolution digital elevation models (DEMs) by enhancing the spatial resolution of low-resolution DEMs. However, current deep learning-based approaches for DEM super-resolution lack comprehensiveness in terrain information reconstruction, resulting in the need to strengthen the rationality of terrain representation. Furthermore, the limited adaptability and extension potential of these approaches restrict their practical applicability and scope, hindering further advancement. As a solution, we introduce a broadly scalable detrending-based deep learning (DTDL) spatially explicit framework for DEM super-resolution. The framework aims to improve DEM reconstruction through data processing and augmentation. It employs detrending to distinguish between large-scale terrain trends and small-scale residuals in DEMs, thereby enhancing the neural network's capacity to learn terrain information. We integrate DTDL with classical super-resolution methods (SRCNN, EDSR, and SRGAN) and conduct experiments in the Alps, Himalayas, and Rockies. The experimental results indicate that the fusion of DTDL with deep learning-based methods enhances the accuracy of terrain reconstruction and the rationality of terrain feature representation, demonstrating strong compatibility and robustness.

Chengbin Wu, Yaohuan Huang, Haijun Yang et al.

Deep-learning-based object detection in UAS imagery is crucial for outfall surveys and basin environmental protection. Comprehensive UAS image datasets serve as a foundation for creating deep-learning-based outfall-detection models and evaluating outfall-detection algorithms. However, existing remote sensing deep learning datasets lack specific outfall data. This study introduces a benchmark UAS image dataset of outfalls, categorized into three main and seven subcategories. Over 10,000 labeled images were collected from UAS images with a 10 cm resolution for the Yangtze River, Yellow River, and other basins from 2019 to 2022, covering nearly all types of outfalls in China. Each sample was matched with digital surface model (DSM) data generated through photographometry or the DSM transfer method. Evaluation with seven widely used deep learning-based object detection algorithms demonstrated the dataset's viability, achieving an average precision ([Formula: see text]) of 64.6, surpassing performance on Microsoft Common Objects in Context. Further experiments indicated that DSMs attached to this dataset could benefit from geo-deep-learning-based object detection algorithms for outfalls, achieving an [Formula: see text] exceeding 70. This study presents a UAS benchmark image dataset for outfall surveys, potentially advancing the application of deep learning in environmental protection.

David Masereti Makori, Elfatih M. Abdel-Rahman, Nelly Ndungu et al.

Stingless/meliponine bees are eusocial insects whose polylactic nature enables interaction with a wide variety of wild plants and crops that enhance pollination and, hence, support ecosystem services. However, their true potential regarding pollination services and honey production is yet to be fully recognized. Worldwide, there are over 800 species of meliponine bees, with over 20 species documented on the African continent. Out of these, only 12 species have been well documented in Kenya. Moreover, interest on meliponine bees has increased amid climate change, agricultural intensification, and other anthropogenic effects. Generally, stingless bees are under-researched, with no previous documented evidence of their ecological niche (EN) distribution in most African countries. Hence, this study sought to establish the influence of bioclimatic, topographic, and vegetation phenology on their spatial distribution and change patterns. Stingless response variables from 490 sample points were collected and used in conjunction with 11 non-conflating features to build stingless ecological niche models. Six machine learning-based EN models were used to predict the distribution of seven stingless bees’ species combined. The results from the EN models showed that annual precipitation was the most influential variable to stingless bee distribution (contributing 43.09% logit), while potential evapotranspiration and temperature seasonality contributed 21.18% of the information needed to predict the spatial distribution of stingless bees. Vegetation phenology (21.36%) and topography (14.36%) had moderate effect on stingless bees’ distribution. On the other hand, high seasonality in precipitation and temperature indicated high stingless niche variability in the future (i.e. 2055). The performance of six EN algorithms used to predict distribution of stingless bees was found to be “excellent” for random forest (true skills statistics (TSS) = 0.91) and ranger (TSS = 0.90) and “good” for generalized additive models (TSS = 0.87), multivariate adaptive regression spline (TSS = 0.80), and boosted regression trees (TSS = 0.80), while they were “fair” for recursive portioning and regression trees (TSS = 0.79). These EN models could be utilized to inform stingless bee farming and insects pollinated crops by highlighting regions that provide highly suitable conditions for stingless bees. Additionally, the findings could be harnessed to increase both bee and agricultural productivity and forest conservation efforts through supplementary pollination services.

Michele Merlo

Edoardo Gellner never concealed the fact that he was a self-taught architect, although the references to the pragmatism of the Deutscher Werkbund and the theoretical teachings of the first Bauhaus are evident in his works. Drawing and photography are the tools through which Gellner educates his eye to look at the reality of things objectively and analytically. His method of analysis is based on a careful understanding of the territory and its matrices: from the comparison between military cartography and cadastral maps, Gellner draws a whole series of considerations on the historical, morphological, and social reasons that led to the development of such landscapes, and the study of centurial grids offers Gellner a counterproof to his theories. Thus, understanding the motivations of architecture but above all the origins of a landscape become the themes that Gellner deepens in an extensive series of studies for publications that were never completed that make these menabò real synthetic – or even artistic – visions of the landscape, as a tribute to the most perfect purovisibilist theory.

C. Fish

ABSTRACT Maps are a key way to communicate climate change. The goal of these maps is to make climate change relatable, tangible, and understandable. However, little research has assessed the content of these maps and the aspects of these maps which attract readers, reduce complexity, and make climate change tangible. One way to evaluate maps of climate change is through the concept of vividness, a term from the communication literature. This article examines the content and vividness of maps of climate change to answer the following: which media organizations publish these maps? What is the design and content of these maps? Did these maps convey climate change vividly? Using content analysis and multidimensional scaling (nMDS) this research showed that producers of climate change maps are often not the publishers of this same content. These maps primarily showed topics which were relevant to audiences in the United States. There was a wide variety of different cartographic designs used. And finally, maps were vivid when they employed the eight aspects of vividness presented in this paper: legend design, symbolization, layout, projections which were appropriate for the data, visual salience, visible change over time, color use which aligned with color connotations, and novel design styles.

F. Künstler

Polina Lemenkova

The study focused on the comparative analysis of the submarine geomorphology of three oceanic trenches: Hikurangi Trench (HkT), Puysegur Trench (PT) and Hjort Trench (HjT), New Zealand region, Pacific Ocean. HjT is characterized by an oblique subduction zone. Unique regional tectonic setting consist in two subduction zones: northern (Hikurangi margin) and southern (Puysegur margin), connected by oblique continental collision along the Alpine Fault, South Island. This cause variations in the geomorphic structure of the trenches. PT/HjT subduction is highly oblique (dextral) and directed southwards. Hikurangi subduction is directed northwestwards. South Island is caught in between by the “subduction scissor”. Methodology is based on GMT (The Generic Mapping Tools) for mapping, plotting and modelling. Mapping includes visualized geophysical, tectonic and geological settings of the trenches, based on sequential use of GMT modules. Data include GEBCO, ETOPO1, EGM96. Comparative histogram equalization of topographic grids (equalized, normalized, quadratic) was done by module ’grdhisteq’, automated cross-sectioning – by ’grdtrack’. Results shown that HjT has a symmetric shape form with comparative gradients on both western and eastern slopes. HkT has a trough-like flat wide bottom, steeper gradient slope on the North Island flank. PT has an asymmetric V-form with steep gradient on the eastern slopes and gentler western slope corresponding to the relatively gentle slope of a subducting plate and steeper slope of an upper one. HkT has shallower depths < 2,500 m, PT is <-6,000 m. The deepest values > 6,000 m for HjT. The surrounding relief of the HjT presents the most uneven terrain with gentle slope oceanward, and a steep slope on the eastern flank for PT, surrounded by complex submarine relief along the Macquarie Arc. Data distribution for the HkT demonstrates almost equal pattern for the depths from -600 m to ₋2,600 m. PT has a bimodal data distribution with 2 peaks: 1) -4,250 to -4,500 m (18%); 2) -2,250 to -3,000 m, < 7,5%. The second peak corresponds to the Macquarie Arc. Data distribution for HjT is classic bell-shaped with a clear peak at -3,250 to -3,500 m. The asymmetry of the trenches resulted in geomorphic shape of HkT, PT and HjT affected by geologic processes.

Chaowei Yang, Dexuan Sha, Qian Liu et al.

The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy, France, Germany, Brazil, Russia, and the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., India, Russia, and Brazil. In response to this national and global emergency, the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implementation strategies to rapidly respond to this crisis, for supporting research, saving lives, and protecting the health of global citizens. This perspective paper presents our collective view on the global health emergency and our effort in collecting, analyzing, and sharing relevant data on global policy and government responses, human mobility, environmental impact, socioeconomical impact; in developing research capabilities and mitigation measures with global scientists, promoting collaborative research on outbreak dynamics, and reflecting on the dynamic responses from human societies.

Dennis Edler, Adalbert Husar, Julian Keil et al.

Ayodeji Iyanu ABIDOYE, Caleb Olutayo OLUWADARE, David O BALOYE

In Nigeria, the spatial requirements of cadastral map for the purposes of land registration are based on 2D planimetric boundary coordinates without consideration for the elevation component of geometric space. Whereas, recent development in technology and practises in many countries requires the inclusion of elevation component into the cadastre. The specific objectives of this study are to determine elevation values for existing 2D cadastre of the study area from different data sources and to analyze those elevation values using statistical means. Data were sourced from both primary and secondary sources; secondary data include a 30m by 30m resolution Global Digital Elevation Model (GDEM), Shuttle Radar Topographic Mission Data (SRTM), 1:50,000 topographic map and existing Digital Elevation Model (DEM) of the study area. Ten ground control points were established at 250m grid with Global Positioning System in differential mode and elevation data were obtained accordingly. Elevation values of selected existing planimetric controls (33) were also determined from adopted data sources and were compared using both the standard deviation and the Root Mean Square Error (RMSE). The vertical accuracy obtained from Topographic map data, existing DTM,  ASTER data and SRTM data were ± 1.860, ± 3.450, ± 5.309 and ±4.573 respectively relative to elevation values obtained from GPS observation of corresponding selected existing 2D planimetric controls. The degree of association between elevation values obtained from adopted data sources was strong and positive as shown from the regression analysis. The study established that only topographic map elevation data would presently fit GPS elevation data for 3D cadastre implementation for the study.

Joe Gerlach

David Amezcua Gómez

En el presente artículo se plantea la inserción de la obra <em>Borderlands/La Frontera. The New Mestiza</em> dentro del marco teórico de la literatura ectópica propuesto por Tomás Albaladejo, planteando, además, una nueva posibilidad de este tipo de literatura. Por otro lado, proponemos que el concepto de literatura ectópica permite dilucidar la noción de <em>tercer país</em> planteada por Gloria Anzaldúa <em>como</em>metáfora de la escritura transfronteriza de la autora; una escritura que pretende desdibujar las fronteras de lo que ella ha denominado un confín contra natura y que por lo tanto introduce una nueva cartografía transcultural.<p class="PARABIBLIOGRAFA"> </p><p class="PARABIBLIOGRAFA"><strong>Palabras clave:</strong> Literatura ectópica. Gloria Anzaldúa. Tercer país. Metáfora. Cartografía transcultural.</p><p class="PARABIBLIOGRAFA"> </p><p class="PARABIBLIOGRAFA">Abstract</p><p class="PARABIBLIOGRAFA">This article deals with the insertion of <em>Borderlands/La Frontera. The New Mestiza </em>in the theoretical framework of ectopic literature proposed by Tomás Albaladejo. As a result, a new possibility of this sort of literature is introduced. On the other hand, it is suggested that the concept of ectopic literature elucidates the notion of Gloria Anzaldua’s <em>third country</em> as a metaphor of the author’s cross-border literature. Thus, her literature aims to blur what she has referred to as an unnatural boundary and introduces, as a consequence, a new transcultural cartography.</p><p class="PARABIBLIOGRAFA"><strong> </strong></p><p class="PARABIBLIOGRAFA"><strong>Key words</strong>: Ectopic literature. Gloria Anzaldúa. Third country. Metaphor. Transcultural cartography.</p>

F. Töpfer, W. Pillewizer

W. Kresse, D. Danko

Samir Awad-Núñez, Nicoletta González-Cancelas, Francisco Soler-Flores et al.

The global economic structure, with its decentralized production and the consequent increase in freight traffic all over the world, creates considerable problems and challenges for the freight transport sector. This situation has led shipping to become the most suitable and cheapest way to transport goods. Thus, ports are configured as nodes with critical importance in the logistics supply chain as a link between two transport systems, sea and land. Increase in activity at seaports is producing three undesirable effects: increasing road congestion, lack of open space in port installations and a significant environmental impact on seaports. These adverse effects can be mitigated by moving part of the activity inland. Implementation of dry ports is a possible solution and would also provide an opportunity to strengthen intermodal solutions as part of an integrated and more sustainable transport chain, acting as a link between road and railway networks. In this sense, implementation of dry ports allows the separation of the links of the transport chain, thus facilitating the shortest possible routes for the lowest capacity and most polluting means of transport. Thus, the decision of where to locate a dry port demands a thorough analysis of the whole logistics supply chain, with the objective of transferring the largest volume of goods possible from road to more energy efficient means of transport, like rail or short-sea shipping, that are less harmful to the environment. However, the decision of where to locate a dry port must also ensure the sustainability of the site. Thus, the main goal of this article is to research the variables influencing the sustainability of dry port location and how this sustainability can be evaluated. With this objective, in this paper we present a methodology for assessing the sustainability of locations by the use of Multi-Criteria Decision Analysis (MCDA) and Bayesian Networks (BNs). MCDA is used as a way to establish a scoring, whilst BNs were chosen to eliminate arbitrariness in setting the weightings using a technique that allows us to prioritize each variable according to the relationships established in the set of variables. In order to determine the relationships between all the variables involved in the decision, giving us the importance of each factor and variable, we built a K2 BN algorithm. To obtain the scores of each variable, we used a complete cartography analysed by ArcGIS. Recognising that setting the most appropriate location to place a dry port is a geographical multidisciplinary problem, with significant economic, social and environmental implications, we consider 41 variables (grouped into 17 factors) which respond to this need. As a case of study, the sustainability of all of the 10 existing dry ports in Spain has been evaluated. In this set of logistics platforms, we found that the most important variables for achieving sustainability are those related to environmental protection, so the sustainability of the locations requires a great respect for the natural environment and the urban environment in which they are framed.

Sheng-Wen Huang, Yun-Wei Hsu, Derek J. Smith et al.