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This study conducts a bibliometric analysis of 735 research papers on geomorphological mapping published in English between 2000 and 2021 using the Web of Science database. The analysis focuses on key metrics such as annual publication rates, journal distribution, common keywords, and frequently cited papers. The results demonstrate sustained investment in geomorphological mapping research over the past two decades, driven by advancements in data analysis, GIS technologies, and cross-institutional and cross-country collaboration. While European universities and research centers lead the field, researchers from Latin America and Asia are also making noteworthy contributions. However, research concentration remains largely in Europe, particularly at low altitudes. The study highlights the vital importance of investment in geomorphological mapping research and the benefits of collaboration to advance understanding and knowledge production. It also emphasizes the need for greater geographic and cultural diversity among researchers to ensure a more comprehensive and inclusive approach to research in this field. [ABSTRACT FROM AUTHOR]

This document is an editorial from the journal "Geographies" titled "Perspectives on Advanced Technologies in Spatial Data Collection and Analysis." The editorial discusses the rapid changes that have occurred in the field of geographical information science and systems in recent decades. It highlights various advanced technologies that have contributed to these changes, such as volunteered geographic information (VGI), wearable devices, social media and networking apps, GIS cloud computing, mobile networks and communication techniques, GeoAI, and blockchain. The editorial also mentions the collection of new types of spatial and spatiotemporal data and invites contributions on topics including geospatial open-source software, big data analysis, GeoAI, and geovisual analytics. The authors acknowledge the authors of the papers in the special issue, the anonymous referees, and the editorial staff for their contributions. [Extracted from the article]

Geovisualization is an interdisciplinary field that combines cartography, geographic information science, computer science, psychology, and more. It involves representing spatial data in various ways, such as maps, animations, and interactive configurations, to explore relationships, patterns, and trends related to geographic entities and phenomena. This article presents eight contributions to the field, including topics such as urban planning, color in maps and art, spatial modeling, construction cost visualization, hydrological flow calculation, map-based data visualization, cartographic generalization, and historical cartographic data representation. The article highlights the challenges and opportunities in geovisualization and emphasizes its importance in conveying meaningful information in the era of big data and artificial intelligence. [Extracted from the article]

Township tourism has become more varied, offering a wider range of products, experiences and services. In this paper, we examine residents and stakeholders' opinions on township tourism in Langa, Cape Town, South Africa; an area characterised by crime, unemployment, housing backlogs and poverty. Using a qualitative approach, this paper reports on empirical evidence conducted with key tourism stakeholders to understand some of their perceptions regarding township tourism development in Langa. Concerns about safety, poor infrastructure and a lack of interaction between tourists and the local community are raised by Langa residents and community leaders. Their dissatisfaction with tour guides and tour routes serves as a reminder of the need for more inclusive practices. While business owners are aware of the potential of tourism in promoting cross-cultural dialogue, deepening understanding and creating priceless experiences, they are constrained by perceptions of crime, a lack of tourist exposure to local establishments and the exclusion from decision-making processes. Despite these difficulties that township tourism faces, it is crucial to promote ethical tourism practices that put emphasis on genuine encounters and local community empowerment. [ABSTRACT FROM AUTHOR]

This paper aims to outline a framework for reviewing the issues faced by tourism destination planning in the 21st century. This paper documents the use of tourism destination typologies as a framework for policy analysis and as a basis for decision making. The main research hypothesis of this study is that typologies based on, or primarily focused on, geographical dimensions have historically been the appropriate framework for strategic planning. This study proposes the use of a basic geographical typology, according to which destinations are categorized into urban, island, coastal, and mountainous. This paper refers to the evolution, key features, and challenges faced by each type of destination. Through a review of international best practices, this study maps out the fundamental objectives, developmental patterns, and strategies for each geographical type of destination, offering valuable insights for future research. Emphasis is given to contemporary trends in tourism planning in the first few decades of the 21st century. [ABSTRACT FROM AUTHOR]

Non-Representational Theory (NRT) emphasizes the significance of routine experience in shaping human geography. In doing so, the theory largely eschews traditional approaches that have offered area-based, longitudinal, and synoptic formalisms for geographic inquiry. Instead, NRT prioritizes the roles of individualized and often dynamic lived geographies as they unfold in the moment. To date, NRT has drawn significant inspiration from the synergies that it shares with philosophy, critical geography, and self-referential ethnography. These activities have been tremendous in advancing NRT as a concept, but the theory's strong ties to encounter and experience invariably call for practical exposition. Alas, applications of NRT to concrete examples at scales beyond small case studies often prove challenging, which we argue artificially constrains further development of the theory. In this paper, we examine some of the thorny problems that present in applying NRT in practical terms. Specifically, we identify ten traps that NRT can fall into when moving from theory to actuality. These traps include conundrums of small geographies, circularity in representation, cognitive traps, issues of mustering and grappling with detail, access issues, limitations with empiricism, problems of subjectivity, methodological challenges, thorny issues of translation, and the unwieldy nature of process dynamics. We briefly demonstrate a novel observational instrument that can sidestep some, but not all, of these traps. [ABSTRACT FROM AUTHOR]

Depopulation is caused by low fertility rates and out-migration, and it applies to countries, regions and smaller areas. Rural depopulation is defined as a sharp population decline that falls well below an adequate population size and indicates that an area has lost its demographic reproductive capacity. This paper discusses the socioeconomic and territorial aspects of rural depopulation, attempting to do justice to the spatial dimensions of the phenomenon. Greece exhibits all the symptoms of demographic transition, leading to labour shortages, declining economic productivity, and increasing demands on the health and welfare system. The study on rural depopulation in Greece focuses on the changes and dynamics observed at the municipal and regional levels. A typology has been developed to identify rural communities in Greece. The main source of demographic data for our study is the Greek censuses (1991, 2001, 2011, and 2021). Demographic and socioeconomic trends in Greece are interlinked and show different regional and local dynamics. Rural depopulation is closely related to the study of (international and internal) migration, even though the latter does not provide a permanent solution to depopulation. An empirical analysis has shown that there is a need to revitalise rural areas through socioeconomic improvements, infrastructure investments, and policies that directly impact rural communities. [ABSTRACT FROM AUTHOR]

This paper presents the development procedure and significance of a web mapping application designed for disseminating, exploring, and analyzing Kaupert's 19th-century Maps of Attica, Greece. The application facilitates historical and geographical study by providing access to high-resolution map images and overlaying multiple vector layers of geospatial data. The paper outlines the methods used to create the application, which includes the process of interpreting, digitizing, and organizing the original mapped data, georeferencing the historical cartographic sheets, and developing the web-based mapping application. The results of this work include a comprehensive and interactive digital reference tool for studying the ancient topography of Attica, as well as a framework for future research. Overall, this work highlights the potential of digital technologies to transform the way we approach and study historical maps and other cultural artifacts. [ABSTRACT FROM AUTHOR]

This paper considers whether existing approaches for quantifying variables in cellular automata (CA) modelling adequately incorporate all the relevant factors in typical actor decisions underpinning urban development. A survey of developers and planners is used to identify factors they incorporate to allow for or proceed with development, using South East Queensland as a reference region. Three types of decision factors are identified and ranked in order of importance: those that are already modelled in CA applications; those that are not modelled but are quantifiable; and those that are not (easily) quantifiable because they are subjective in nature. Factors identified in the second category include development height/scale, open space supply, and existing infrastructure capacity. Factors identified in the third category include political intent, community opposition, and lifestyle quality. Drawing on our analysis of these factors we suggest how and to what extent survey data might be used to address the challenges of incorporating actor variables into the CA modelling of urban change. The paper represents the first attempt to review what decision factors should be included in CA modelling, and how this might be enabled. [ABSTRACT FROM AUTHOR]

Web Geographic Information Systems (WebGISs) were widely used to monitor COVID-19 cases and deaths during the pandemic. Furthermore, geotechnologies were also very useful in education, public management, tourism, and other areas. Although there are WebGISs with a high level of sophistication, most are simple, consisting of geovisualizers of cases, deaths, and vaccinations. This study develops a WebGIS that offers information about age, comorbidities, and tests, which can be analyzed from specific points such as hospitals, main access roads, regions, or neighborhoods. Although it is not a highly sophisticated solution, the WebGIS developed in this study is especially useful for municipal governments in developing countries like Brazil that do not have patient health data in geographic databases. The WebGIS developed in this study offers public managers essential information for developing effective public policies to combat the COVID-19 pandemic and other epidemiological phenomena such as dengue and malaria. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, regions were affected by a combination of economic crises: weak demand and constrained supply. Several studies have sought to analyse the heterogeneous effects of supply and demand shocks on the labour market, economic growth, and the environment. This study has a different focus, estimating both direct and indirect effects of demand and supply shocks adopted during the pandemic in Brazil and Chile. Afterwards, the paper compares the degree of regional absorption (leakage) of income resulting from each of these shocks, applying an interregional input–output model for each country. The results of this study show that income absorption by the poorest regions is relatively greater in the case of a supply shock. It can be said, therefore, that this type of shock improves the retention of income generated in the poorest regions, favouring the development of these localities and the reduction in regional inequalities. The main reason for this result is that supply policies have restricted essential sectors to a lesser extent, and these sectors are generally less concentrated in large urban centres in both Brazil and Chile. In other words, much of the interregional leakage is driven by the demand for non-essential products, mainly in the richest urban economy centres. Finally, the geographical dimension of regional inequalities leads to the economic benefit of prosperous areas in the country when shocks occur in vulnerable regions, highlighting the centre–periphery pattern in both countries. [ABSTRACT FROM AUTHOR]

The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack of accurate in situ data. Street-level imagery represents a new potential source of in situ data that may aid crop type mapping, but it requires automated algorithms to recognize the features of interest. This paper aims to demonstrate a method for crop type (i.e., maize, wheat and others) recognition from street-level imagery based on a convolutional neural network using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery using the Picture Pile application. The classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for others. Given that wheat and maize are two of the most common food crops grown globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved global crop type monitoring. Challenges remain in addressing the noise aspect of street-level imagery (i.e., buildings, hedgerows, automobiles, etc.) and uncertainties due to differences in the time of day and location. Such an approach could also be applied to developing other in situ data sets from street-level imagery, e.g., for land use mapping or socioeconomic indicators. [ABSTRACT FROM AUTHOR]

An enumeration of spatial autocorrelation's (SA's) polyvalent forms occurred nearly three decades ago. Attempts to conceive and disseminate a clearer explanation of it employ metaphors seeking to better relate SA to a student's or spatial scientist's personal knowledge databank. However, not one of these uses the jigsaw puzzle metaphor appearing in this paper, which exploits an analogy between concrete visual content organization and abstract map patterns of attributes. It not only makes SA easier to understand, which furnishes a useful pedagogic tool for teaching novices and others about it, but also discloses that many georeferenced data should contain a positive–negative SA mixture. Empirical examples corroborate this mixture's existence, as well as the tendency for marked positive SA to characterize remotely sensed and moderate (net) positive SA to characterize socio-economic/demographic, georeferenced data. [ABSTRACT FROM AUTHOR]

This paper analyzes the performance of the open-source OpenDroneMap image processing software (ODM) across multiple platforms. We tested desktop and laptop computers as well as high-performance cloud computing and supercomputers. Multiple machine configurations (CPU cores and memory) were used. We used eBee S.O.D.A. drone image datasets from Namibia and northern Finland. For testing, we used the OpenDroneMap command line tool with default settings and the fast orthophoto option, which produced a good quality orthomosaic. We also used the "rerun-all option" to ensure that all jobs started from the same point. Our results show that ODM processing time is dependent upon the number of images, a high number of which can lead to high memory demands, with low memory leading to an excessively long processing time. Adding additional CPU cores is beneficial to ODM up to a certain limit. A 20-core machine seems optimal for a dataset of about 1000 images, although 10 cores will result only in slightly longer processing times. We did not find any indication of improvement when processing larger datasets using 40-core machines. For 1000 images, 64 GB memory seems to be sufficient, but for larger datasets of about 8000 images, higher memory of up to 256 GB is required for efficient processing. ODM can use GPU acceleration, at least in some processing stages, reducing processing time. In comparison to commercial software, ODM seems to be slower, but the created orthomosaics are of equal quality. [ABSTRACT FROM AUTHOR]

Most of the methods for landslide susceptibility assessment are based on mathematical relationships established between factors responsible for the triggering of the phenomenon, named the conditioning factors. These are usually derived from geographic data commonly handled through Geographical Information System (GIS) technology. According to the adopted methodology, after an initial phase conducted on the GIS platform, data need to be transferred to specific software, e.g., MATLAB, for analysis and elaboration. GIS-based risk management platforms are thus sometimes hybrid, requiring relatively complex adaptive procedures before exchanging data among different environments. This paper describes how MATLAB can be used to derive the most common landslide conditioning factors, by managing the geographic data in their typical formats: raster, vector or point data. Specifically, it is discussed how to build matrices of parameters, needed to assess susceptibility, by using grid cell mapping units, and mapping them bypassing GIS. An application of these preliminary operations to a study area affected by shallow landslides in the past is shown; results show how geodata can be managed as easily as in GIS, as well as being displayed in a fashionable way too. Moreover, it is discussed how raster resolution affects the processing time. The paper sets the future development of MATLAB as a fully implemented platform for landslide susceptibility, based on any available methods. [ABSTRACT FROM AUTHOR]

Automation in map production has created the need for modeling the map composition process. Generalization is the most critical process in map composition, with considerable impact on the quality of features portrayed on the maps. Modeling of the generalization process has been an area of research for several years in the international cartographic community. Constraint-based generalization modeling prevailed, and it is evolving to an agent model or to other optimization models. The generalization model presented in this paper is based on constraint-based modeling. It introduces the standardization of the semantic and cartographic generalization process together with an evaluation mechanism for the assessment of the quality of the resulting cartographic data considering simultaneously the preservation of the shape of the portrayed linear and area features. For cartographers, quality management is a key factor in creating an evidence-based, reliable product. To achieve this objective, cartographers, drawing on international experience, should implement a quality policy and adopt a quality management system (QMS) as an integral part of the map production process, starting with the quality assessment of the input data and finishing with the evaluation of the final product. [ABSTRACT FROM AUTHOR]

Inundation dynamics coupled with seasonal information is critical to study the wetland environment. Analyses based on remotely sensed data are the most effective means to monitor and investigate wetland inundation dynamics. For the first time, this study deployed an automated thresholding method to quantify and compare the annual inundation characteristics in dry and wet seasons in the Everglades, using Landsat imagery in Google Earth Engine (GEE). This research presents the long-term time series maps from 2002 to 2021, with a comprehensive spatiotemporal depiction of inundation. In this paper, we bridged the research gap of space-time analysis for multi-season inundation dynamics, which is urgently needed for the Everglades wetland. Within a GIS-based framework, we integrated statistical models, such as Mann–Kendall and Sen's Slope tests, to track the evolutionary trend of seasonal inundation dynamics. The spatiotemporal analyses highlight the significant differences in wet and dry seasons through time and space. The stationary or permanent inundation is more likely to be distributed along the coastal regions (Gulf of Mexico and Florida Bay) of the Everglades, presenting a warning regarding their vulnerability to sea level rise. [ABSTRACT FROM AUTHOR]

This paper aims to explore and evaluate aerial imagery and deep learning technology in pavement condition evaluation. A convolutional neural network (CNN) model, named PCIer, was designed to process aerial images and produce pavement condition index (PCI) estimations, which are classified into four scales of Good (PCI ≥ 70), Fair (50 ≤ PCI < 70), Poor (25 ≤ PCI < 50), and Very Poor (PCI < 25). In the experiment, the PCI datasets were retrieved from the published pavement condition report by the City of Sacramento, CA. Following the retrieved datasets, the authors also collected the corresponding aerial image datasets containing 100 images for each PCI grade from Google Earth. An 80% proportion of datasets were used for PCIer model training, and the remaining were used for testing. Comparisons showed using a 128-channel heatmap layer in the proposed PCIer model and saving the PCIer model with the best validation accuracy would yield the best performance, with a testing accuracy of 0.97, and a weighted average precision, recall, and F1-score of 0.98, 0.97, and 0.97, respectively. Moreover, future research recommendations are provided in the discussion for improving the effectiveness of pavement evaluation via aerial imagery and deep learning. [ABSTRACT FROM AUTHOR]

Rockfall hazard gains popularity nowadays among researchers in different scientific fields, decision-makers and urban planners. The assessment of rockfall hazard requires detection, mapping and estimating the maximum travel distance that rock boulders may reach, commonly known as "rockfall runout". This latter can change significantly under the effects of different triggering factors such as soil conditions, chemical, physical and geological rock properties. However, comparing and analyzing these different effects represents, to the best of our knowledge, one of the newest scientific challenges that need to be addressed. This paper presents a complete methodologic approach aiming to assess the rockfall hazard through runout estimation in three different conditions: (i) gravity, (ii) earthquakes, and (iii) the presence of moisture along the slope. The "Mtein" Village and its surrounding areas in the Mount Lebanon region were chosen as the study area because there have been numerous historic rockfalls and various-sized rocks, such as cobbles and boulders, scattered throughout the area. Thus, three-dimensional simulations were conducted using the Rockyfor3D software and aerial photos for the year 1999 to assess the rockfall runout, the energy curves, and the number of deposited rocks. The results reveal that earthquakes have the highest triggering effect on rockfall and that moisture has a damping effect on RFs by decreasing the kinetic energy. The study shows the importance of taking into consideration the influence of triggering factors as well as rock density on rockfall runout and hazard. [ABSTRACT FROM AUTHOR]

Township tourism has become more varied, offering a wider range of products, experiences and services. In this paper, we examine residents and stakeholders’ opinions on township tourism in Langa, Cape Town, South Africa; an area characterised by crime, unemployment, housing backlogs and poverty. Using a qualitative approach, this paper reports on empirical evidence conducted with key tourism stakeholders to understand some of their perceptions regarding township tourism development in Langa. Concerns about safety, poor infrastructure and a lack of interaction between tourists and the local community are raised by Langa residents and community leaders. Their dissatisfaction with tour guides and tour routes serves as a reminder of the need for more inclusive practices. While business owners are aware of the potential of tourism in promoting cross-cultural dialogue, deepening understanding and creating priceless experiences, they are constrained by perceptions of crime, a lack of tourist exposure to local establishments and the exclusion from decision-making processes. Despite these difficulties that township tourism faces, it is crucial to promote ethical tourism practices that put emphasis on genuine encounters and local community empowerment.

This paper aims to outline a framework for reviewing the issues faced by tourism destination planning in the 21st century. This paper documents the use of tourism destination typologies as a framework for policy analysis and as a basis for decision making. The main research hypothesis of this study is that typologies based on, or primarily focused on, geographical dimensions have historically been the appropriate framework for strategic planning. This study proposes the use of a basic geographical typology, according to which destinations are categorized into urban, island, coastal, and mountainous. This paper refers to the evolution, key features, and challenges faced by each type of destination. Through a review of international best practices, this study maps out the fundamental objectives, developmental patterns, and strategies for each geographical type of destination, offering valuable insights for future research. Emphasis is given to contemporary trends in tourism planning in the first few decades of the 21st century.

Many books, textbooks and papers have been published in which the classification of map projections is based on auxiliary (developable) surfaces and projections are divided into conic, cylindrical and azimuthal projections. We argue that such a classification of map projections is unacceptable and give many reasons for that. Many authors wrote in more detail about the classification of map projections, and our intention is to give a new refined and rectified insight into the classification of map projections. Our approach can be included in map projection publications of general and thematic cartography. Doing this, misconceptions and unnecessary insistence on conceptuality instead of reality will be avoided. [ABSTRACT FROM AUTHOR]

Recent research has extended conventional hydrological algorithms into a hexagonal grid and noted that hydrological modeling on a hexagonal mesh grid outperformed that on a rectangular grid. Among the hydrological products, flow routing grids are the base of many other hydrological simulations, such as flow accumulation, watershed delineation, and stream networks. However, most of the previous research adopted the D6 algorithm, which is analogous to the D8 algorithm over a rectangular grid, to produce flow routing. This paper explored another four methods regarding generating flow directions in a hexagonal grid, based on four algorithms of slope aspect computation. We also developed and visualized hexagonal-grid-based hydrological operations, including flow accumulation, watershed delineation, and hydrological indices computation. Experiments were carried out across multiple grid resolutions with various terrain roughness. The results showed that flow direction can vary among different approaches, and the impact of such variation can propagate to flow accumulation, watershed delineation, and hydrological indices production, which was reflected by the cell-wise comparison and visualization. This research is practical for hydrological analysis in hexagonal, hierarchical grids, such as Discrete Global Grid Systems, and the developed operations can be used in flood modeling in the real world. [ABSTRACT FROM AUTHOR]

This study conducts a bibliometric analysis of 735 research papers on geomorphological mapping published in English between 2000 and 2021 using the Web of Science database. The analysis focuses on key metrics such as annual publication rates, journal distribution, common keywords, and frequently cited papers. The results demonstrate sustained investment in geomorphological mapping research over the past two decades, driven by advancements in data analysis, GIS technologies, and cross-institutional and cross-country collaboration. While European universities and research centers lead the field, researchers from Latin America and Asia are also making noteworthy contributions. However, research concentration remains largely in Europe, particularly at low altitudes. The study highlights the vital importance of investment in geomorphological mapping research and the benefits of collaboration to advance understanding and knowledge production. It also emphasizes the need for greater geographic and cultural diversity among researchers to ensure a more comprehensive and inclusive approach to research in this field.

Color occupies a prominent place in the bibliography of cartography, as it is an important element in the formation of cartographic symbolization. Apart from the technical issues of its application to maps, color theory is one of the elements that connect maps with art. In this paper various cartographic trends and their origins are examined and correlated with the artistic periods in which they were developed in order to investigate and document the extent to which maps follow the artistic movements and, particularly in the art of painting, concerning the form and the content of the maps and whether color can be used as an identification element of the art trend and the corresponding period. The research spans from the end of the Middle Ages to the 21st century and is referred spatially in Western Europe, including Italy. The comparison of colors is made in both descriptive and quantitative terms through the commentary of hue, brightness, and saturation, as well as through plotting them in the color wheel, a process that allows an overview of the range and location of color sequences. Concluding, the paintings and maps that were selected and examined in detail support the effect of painting on maps, without implying that it is intentional. [ABSTRACT FROM AUTHOR]

Fire susceptibility modeling is crucial for sustaining and managing forests among many other valuable land resources. With 56% of its area covered by forests, Arkansas is known as the "natural state". About 1000 wildfires occurred and burned more than 10,000 acres each year during 1981–2018. In this paper, we use remote-sensing-based machine learning methods to address the natural and anthropogenic factors influencing wildfires and model fire susceptibility in Arkansas. Among the 15 explored variables, potential evapotranspiration, soil moisture, Palmer drought severity index, and dry season precipitation were recognized as the most significant factors contributing to the fire density. The obtained R-squared values are significant, with 0.99 for training the model and 0.92 for the validation. The results show that the Ouachita National Forest and the Ozark Forest, in west-central and west Arkansas, respectively, have the highest susceptibility to wildfires. The southern part of Arkansas has low-to-moderate fire susceptibility, while the eastern part of the state has the lowest fire susceptibility. These new results for Arkansas demonstrate the potency of remote-sensing-based random forest in predicting fire susceptibility at the state level that can be adapted to study fires in other states and help with fire preparedness to reduce loss and save the precious environment. [ABSTRACT FROM AUTHOR]

This paper presents the development procedure and significance of a web mapping application designed for disseminating, exploring, and analyzing Kaupert’s 19th-century Maps of Attica, Greece. The application facilitates historical and geographical study by providing access to high-resolution map images and overlaying multiple vector layers of geospatial data. The paper outlines the methods used to create the application, which includes the process of interpreting, digitizing, and organizing the original mapped data, georeferencing the historical cartographic sheets, and developing the web-based mapping application. The results of this work include a comprehensive and interactive digital reference tool for studying the ancient topography of Attica, as well as a framework for future research. Overall, this work highlights the potential of digital technologies to transform the way we approach and study historical maps and other cultural artifacts.

Unmanned Aerial Systems (UAS, UAV, or drones) have become an effective tool for applications in natural resources since the start of the 21st century. With their associated hardware and software technologies, UAS sensor data have provided high resolution and high accuracy results in a range of disciplines. Despite these achievements, only minimal progress has been made in (1) establishing standard operating practices and (2) communicating both the limitations and necessary next steps for future research. In this review of literature published between 2016 and 2022, UAS applications in forestry, freshwater ecosystems, grasslands and shrublands, and agriculture were synthesized to discuss the status and trends in UAS sensor data collection and processing. Two distinct conclusions were summarized from the over 120 UAS applications reviewed for this research. First, while each discipline exhibited similarities among their data collection and processing methods, best practices were not referenced in most instances. Second, there is still a considerable variability in the UAS sensor data methods described in UAS applications in natural resources, with fewer than half of the publications including an incomplete level of detail to replicate the study. If UAS are to increasingly provide data for important or complex challenges, they must be effectively utilized. [ABSTRACT FROM AUTHOR]

Non-Representational Theory (NRT) emphasizes the significance of routine experience in shaping human geography. In doing so, the theory largely eschews traditional approaches that have offered area-based, longitudinal, and synoptic formalisms for geographic inquiry. Instead, NRT prioritizes the roles of individualized and often dynamic lived geographies as they unfold in the moment. To date, NRT has drawn significant inspiration from the synergies that it shares with philosophy, critical geography, and self-referential ethnography. These activities have been tremendous in advancing NRT as a concept, but the theory’s strong ties to encounter and experience invariably call for practical exposition. Alas, applications of NRT to concrete examples at scales beyond small case studies often prove challenging, which we argue artificially constrains further development of the theory. In this paper, we examine some of the thorny problems that present in applying NRT in practical terms. Specifically, we identify ten traps that NRT can fall into when moving from theory to actuality. These traps include conundrums of small geographies, circularity in representation, cognitive traps, issues of mustering and grappling with detail, access issues, limitations with empiricism, problems of subjectivity, methodological challenges, thorny issues of translation, and the unwieldy nature of process dynamics. We briefly demonstrate a novel observational instrument that can sidestep some, but not all, of these traps.

This research assesses the environmental uses of Haleakalā Crater, and adjoining east Maui areas, by native Hawaiians during recent centuries, and evaluates the modern utilization of this volcanic depression. The study methods examine, and focus on, numerous historical and modern accounts and reports. Three historical periods were identified as follows: (1). A phase, from the ~1400s until contact with foreigners in 1778, characterized by the development of settlements, population expansion, the intensification of traditional agriculture, and the political consolidation of Maui. The construction of the Kiha-a-Pi'i-lani trail across the crater enhanced travel between distant island areas. (2). Following the collapse of Maui's human populations during the late 1700s, and until the 20th century, came a period distinguished by scientific exploration of the crater, and intentional or accidental introductions of animals, including goats, horses, cattle, and wild dogs. (3). After Haleakalā became a National Park in 1916, efforts to eradicate introduced animals and to encourage the reproduction of silverswords and other plants were initiated. Unfortunately, in retrospect, construction of a modern paved road, improvement of hiking trails, and building of facilities allowed swift access to the Park and substantially increased tourist numbers, up to ~2 million yearly visitors. Faced with such crowds, the long-term planning of Park resources appears to be a formidable task. [ABSTRACT FROM AUTHOR]

Accurate flood prediction models and effective flood preparedness rely on thoroughly understanding rainfall–runoff dynamics. Similarly, effective rainfall–runoff models account for multiple interrelated parameters for robust runoff prediction. Process-based physical models offer valuable insights into hydrological processes, but their effectiveness can be hindered by data limitations or difficulties in acquiring specific data. Motivated by the frequent flooding events and limited data availability in the East Branch DuPage watershed, Illinois, this study addresses a critical gap in research by investigating effective discharge prediction methods. In this study, two significant machine learning (ML) models, artificial neural network (ANN) and support vector machine (SVM), were employed for discharge prediction. Historical data spanning from 2006 to 2021 were utilized to assess the performance of the models. Hyperparameter tuning was performed on the models to optimize their performance, and root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R2), and the normalized root mean squared error (NRMSE) were used as evaluation metrics. Although both machine learning models demonstrated strong performance, the analysis revealed that the ANN model emerged as the more reliable option for predicting discharge in the watershed. Crucially, the ANN model surpassed the SVM model's performance, achieving superior accuracy in predicting peak discharge events within the study area. Our findings have the potential to assist decision-makers and communities in implementing more dependable flood mitigation strategies, particularly in regions where hydrology data are limited. [ABSTRACT FROM AUTHOR]

Highly diverse agroecosystems are increasingly of interest as the realization of farms' invaluable ecosystem services grows. Simultaneously, there has been an increased use of uncrewed aerial systems (UASs) in remote sensing, as drones offer a finer spatial resolution and faster revisit rate than traditional satellites. With the combined utility of UASs and the attention on agroecosystems, there is an opportunity to assess UAS practicality in highly biodiverse settings. In this study, we utilized UASs to collect fine-resolution 10-band multispectral imagery of coffee agroecosystems in Puerto Rico. We created land cover maps through a pixel-based supervised classification of each farm and assembled accuracy assessments for each classification. The average overall accuracy (53.9%), though relatively low, was expected for such a diverse landscape with fine-resolution data. To bolster our understanding of the classifications, we interviewed farmers to understand their thoughts on how these maps may be best used to support their land management. After sharing imagery and land cover classifications with farmers, we found that while the prints were often a point of pride or curiosity for farmers, integrating the maps into farm management was perceived as impractical. These findings highlight that while researchers and government agencies can increasingly apply remote sensing to estimate land cover classes and ecosystem services in diverse agroecosystems, further work is needed to make these products relevant to diversified smallholder farmers. [ABSTRACT FROM AUTHOR]

The Italian national territory is characterised by the widespread presence of cavities dating back to different periods, especially in urban areas. The lack of knowledge of the position of the entrances, planimetric developments and state of preservation contributes to accentuating the unknowns related to sinkhole risk, which are directly related to potential cavity collapses with the opening of surface chasms. To deepen knowledge with a view to risk mitigation, a method has been developed to employ surveys obtained from Unmanned Aerial Vehicles (UAVs) to locate entrances even in hard-to-access urban areas. These surveys, properly supported with GNSS stations, were then integrated with cavity surveys obtained from low-cost lidar mounted on iPhones. Comparisons were made with traditional surveying techniques to better understand the reliability of the surveys made with low-cost lidar. The 3D models obtained, combined with geomechanical surveys of the rock masses hosting the cavities, allowed the application of simplified and empirical methods for an initial stability assessment. This method was tested on a portion of the municipality of Grotte di Castro (Province of Viterbo—Italy). [ABSTRACT FROM AUTHOR]

Global concern over optimizing transboundary water resources for residents is hindered by the lack of observational data, particularly in ungauged basins, mainly due to inaccessibility or security issues. Remote sensing and GIS technology provide a practical solution for monitoring and managing water resources in such basins. This research evaluates surface water resources in the Qaretikan ungauged transboundary basin using satellite products for precipitation, temperature, and evapotranspiration from 2005 to 2014. The accuracy of these datasets was assessed using statistical measures. The water balance components, i.e., precipitation and evaporation, were utilized to calculate runoff over the basin using the Justin method. Downstream environmental flow was estimated using the Lyon method, and available water was determined. This study identified a potential annual storage water of 11.8 MCM in the Qaretikan basin. The Analytic Hierarchy Process (AHP) integrated expert opinions to prioritize water usage decisions based on proposed decision options. The results revealed greenhouse cultivation water allocation as the top priority among the identified options, highlighting its importance in sustainable water resource management within the basin. [ABSTRACT FROM AUTHOR]

Recent treatment of accommodation and arrival infrastructures for asylum seekers and refugees has fuelled international research on refugee reception policies in urban environments and on the consequences of related initiatives of the European Union and international organizations such as the UNHCR. Using Athens as a case study, this article provides empirical evidence to revive the theoretical treatment of the importance of arrival and accommodation infrastructures in urban areas in transition. We collected and compiled data from four sources: the 2011 population census, the 2018 ESTIA accommodation program and the UNHCR Site Management Support (SMS) Reports of Temporary Accommodation Sites and Reception and Identification Centres (RICs), and a primary survey of services for asylum seekers and refugees. After the geocoding of data, we calculate indices for key dimensions of the segregation of accommodated asylum seekers and foreign nationals. We discuss the findings, seeking to highlight how the location and the composition of accommodation infrastructures has been influenced by a wider process of urban change and adaptations to global forces, leading not only to the transformation of inner-city zones but also suburban and peri-urban areas. [ABSTRACT FROM AUTHOR]

Urban transformations change land use, permeability, and morphology of the areas involved in the evolution process; this, consequently, modifies the impact produced by the precipitation phenomena and increases the risk of flooding or uncontrolled runoff in different areas.The proposed watershed hydrologic approach enables us to consider the morphology of the territory together with the transformations implemented by human activities, and this allows us to evaluate the effects of each area on neighboring areas, emphasizes the hydrological roles of upper, intermediate, and lower parts, and reveals urban and non-urban connections. This elucidates hydromorphic complexities in urban transformations and assesses climate change adaptability. The suggested methodology has been implemented in the urban district of "Sasso Caveoso" within the city of Matera. This application facilitates a quantitative synthesis of the contextual response, allowing for an analysis across various scenarios and offering decision-support tools of practical utility. [ABSTRACT FROM AUTHOR]

This study proposes planning for children's independent mobility through geoinformation technologies by listening to children. This research assumes that children's values and expectations must be considered in city planning. A bibliographic review identified 15 indicators which make spaces safe and attractive for children to circulate and play. Thematic maps of the indicators were prepared and integrated by a multicriteria analysis by the weights of the evidence according to the hierarchical importance of each variable. The definition of the weights considered the opinions of the children and technicians. The consultation with children was carried out by mapping volunteers (VGI), a consultation on hierarchy, the geodesign of ideas for the area, and an artistic workshop. In the technical study, the query applied the Delphi method. It used the VGI—Volunteered Geographic Information—web-based platform, where children recorded places of topophilia and topophobia, while technicians mapped the presence of 15 indicators. The set of information was made available on a web-based platform called SDI—Spatial Data Infrastructure—in which there are resources for a geodesign workshop where ideas for the area were elaborated through negotiation and cocreation. The product is a transformational design for the area through urban design and the parameterization of its uses. [ABSTRACT FROM AUTHOR]

Approximately 30% of the private land in Texas, USA is under absentee ownership. Understanding who absentee landowners are and their land management behaviors is vital for the protection of privately owned landscapes and the ecosystem services that they support, including surface water quality. By focusing on absentee landowners with properties in five watersheds in Texas, we utilized the theory of place attachment to gain insights into absentee landowners' land management decisions and their involvement in water quality conservation programs, such as watershed protection plans (WPPs). By conducting a mail-out survey, we obtained 100 responses, which were analyzed using an exploratory factor analysis and a series of nonparametric assessments. The results revealed that, contrary to the term "absentee", the landowners in our study demonstrated strong feelings of place attachment and heightened land stewardship. Based on these findings, we suggest that instead of considering absentee landowners as obstacles to collaborative conservation initiatives, such as WPPs, natural resource practitioners should recognize and capitalize on the emotional attachment that these landowners have to their properties, thereby fostering their involvement. By demonstrating the owner–land relationship and its behavioral outcomes among absentee landowners, this study provides a novel contribution to the existing literature on place attachment in the context of private land management and conservation. [ABSTRACT FROM AUTHOR]

Understanding rain, ground, and surface water interactions in riparian zones is essential for hydrologic and environmental management. The novelty of this study lies in its revelation of isotopic shifts and consequent evaluation of hydrologic pathways and processes within the forested riparian zones of three distinct streams, valleys, and riparian systems in a carbonate-sandstone incipient karst landscape. We collected water samples from three southeastern Minnesota catchments: Trout Brook, Crystal Creek, and Bridge Creek. A Picarro L2130i was used to measure δ18O for oxygen, and δD for deuterium in units of ‰ (per mil). We estimated the global meteoric line for the study sites, built upon aquifer age dating, and explored aquifer transit time for the study sites using a simple seasonal amplitude model. The results showed small amplitudes for 2020 and 2021, suggesting that bedrock aquifer water was the primary source water with a mean core transit time greater than 10 years. All three catchments were different but had similar bedrock valley types and riparian sediment. The primary driving factor was the seasonal precipitation input mixing with existing water. In a normal to wetter year, the isotopic data showed larger amplitude shifts between seasons with trendlines that adjusted depending on the temperature of the new water additions. The proposed approach is valuable in revealing complex hydrologic processes and pathways and can contribute extensively to the planning and management of karst riparian systems. [ABSTRACT FROM AUTHOR]

We present a near real-time solution for 3D reconstruction from aerial images captured by consumer UAVs. Our core idea is to simplify the multi-view stereo problem into a series of two-view stereo matching problems. Our method applies to UAVs equipped with only one camera and does not require special stereo-capturing setups. We found that the neighboring two video frames taken by UAVs flying at a mid-to-high cruising altitude can be approximated as left and right views from a virtual stereo camera. By leveraging GPU-accelerated real-time stereo estimation, efficient PnP correspondence solving algorithms, and an extended Kalman filter, our system simultaneously predicts scene geometry and camera position/orientation from the virtual stereo cameras. Also, this method allows for the user selection of varying baseline lengths, which provides more flexibility given the trade-off between camera resolution, effective measuring distance, flight altitude, and mapping accuracy. Our method outputs dense point clouds at a constant speed of 25 frames per second and is validated on a variety of real-world datasets with satisfactory results. [ABSTRACT FROM AUTHOR]

In the literature on map projections, we regularly encounter the name standard parallel or standard parallels. However, it is obvious that a unique definition of a standard parallel is not universally accepted. To fully clarify the meaning of standard parallels, the author proposes the notion of equidistantly mapped parallels, which has not been common in the literature so far. Equidistantly mapped parallels can be in the direction of the parallel or in the direction of the meridian. Here, it is shown that every standard parallel is also an equidistantly mapped parallel, but that the reverse need not be true. If the parallel is mapped equidistantly in the direction of the parallel, then its length in the projection plane is equal to the length of that parallel on the sphere. The opposite does not have to be true, i.e., if the length of the image of the parallel in the projection plane is equal to the length of the parallel on the sphere, this does not mean that the parallel was mapped equidistantly. In addition to standard and equidistant parallels, the concept of parallels of true length also appears in the theory of map projections. They should also be distinguished from standard and equidistant parallels. [ABSTRACT FROM AUTHOR]

Depopulation is caused by low fertility rates and out-migration, and it applies to countries, regions and smaller areas. Rural depopulation is defined as a sharp population decline that falls well below an adequate population size and indicates that an area has lost its demographic reproductive capacity. This paper discusses the socioeconomic and territorial aspects of rural depopulation, attempting to do justice to the spatial dimensions of the phenomenon. Greece exhibits all the symptoms of demographic transition, leading to labour shortages, declining economic productivity, and increasing demands on the health and welfare system. The study on rural depopulation in Greece focuses on the changes and dynamics observed at the municipal and regional levels. A typology has been developed to identify rural communities in Greece. The main source of demographic data for our study is the Greek censuses (1991, 2001, 2011, and 2021). Demographic and socioeconomic trends in Greece are interlinked and show different regional and local dynamics. Rural depopulation is closely related to the study of (international and internal) migration, even though the latter does not provide a permanent solution to depopulation. An empirical analysis has shown that there is a need to revitalise rural areas through socioeconomic improvements, infrastructure investments, and policies that directly impact rural communities.

During the COVID-19 pandemic, regions were affected by a combination of economic crises: weak demand and constrained supply. Several studies have sought to analyse the heterogeneous effects of supply and demand shocks on the labour market, economic growth, and the environment. This study has a different focus, estimating both direct and indirect effects of demand and supply shocks adopted during the pandemic in Brazil and Chile. Afterwards, the paper compares the degree of regional absorption (leakage) of income resulting from each of these shocks, applying an interregional input–output model for each country. The results of this study show that income absorption by the poorest regions is relatively greater in the case of a supply shock. It can be said, therefore, that this type of shock improves the retention of income generated in the poorest regions, favouring the development of these localities and the reduction in regional inequalities. The main reason for this result is that supply policies have restricted essential sectors to a lesser extent, and these sectors are generally less concentrated in large urban centres in both Brazil and Chile. In other words, much of the interregional leakage is driven by the demand for non-essential products, mainly in the richest urban economy centres. Finally, the geographical dimension of regional inequalities leads to the economic benefit of prosperous areas in the country when shocks occur in vulnerable regions, highlighting the centre–periphery pattern in both countries.

The inefficiency of transporting goods contributes to reduced economic growth and environmental sustainability in a country. Autonomous trucks (ATs) are emerging as a solution, but the imbalance in the weight moved and ton-miles produced by long-haul and short-haul trucking creates a challenge in targeting initial deployments. This study offers a unique solution by presenting a robust method that combines data mining and geographic information systems (GISs) to identify the optimal routes for ATs based on a top-down approach to maximize business benefits. Demonstrated in a U.S. case study, this method revealed that despite accounting for only 16% of the weight moved, long-haul trucking produced 56% of the ton-miles, implying a high potential for ATs in this segment. The method identified eight key freight zones in five U.S. states that accounted for 27% of the long-haul weight and suggested optimal routes for initial AT deployment. Interstate 45 emerged as a pivotal route in the shortest paths among these freight zones. This suggests that stakeholders should seek to prioritize funding for infrastructure upgrades and maintenance along that route and the other routes identified. The findings will potentially benefit a broad range of stakeholders. Companies can strategically focus resources to achieve maximum market share, regulators can streamline policymaking to facilitate AT adoption while ensuring public safety, and transportation agencies can better plan infrastructure upgrades and maintenance. Users globally can apply the methodological framework as a reliable tool for decision-making about where to initially deploy ATs. [ABSTRACT FROM AUTHOR]

The Moesian Platform represents a major tectonic unit of the foreland of the Carpathians and Balkans, spanning across the southern part of Romania and the northern part of Bulgaria. Although the Moesian Platform is considered to be a stable tectonic unit, it has played a significant role in the geological history of the region, influencing the development of the surrounding Carpathian and Balkan mountain ranges, making it an area of interest for studying tectonic history, geological structures, and landscape evolution. In the southern part of the Moesian Platform in Romania, delineated to the north and to the east by the steep slopes of the Argeş River valley and to the south by the steep slopes of the Danube River valley, an elevated and W–E promontory-looking geomorphological feature identified by the local inhabitants as "hill" is distinct from the neighbouring flat relief of the Romanian plain. This study is the result of a comprehensive investigation into the geomorphological features and neotectonic structures within this region. An intriguing outcrop displaying a filled fault, cutting and displacing the Quaternary sedimentary formations of the recently named Argeş Promontory, shed light on recent tectonic activities that have influenced the landscape. By integrating field observations, geological, and tectonic data, as well as satellite geodetic data, our results contribute to a better understanding of the study area's regional geodynamics, emphasizing the significant role of tectonic activity in shaping the present-day landscape. [ABSTRACT FROM AUTHOR]

The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack of accurate in situ data. Street-level imagery represents a new potential source of in situ data that may aid crop type mapping, but it requires automated algorithms to recognize the features of interest. This paper aims to demonstrate a method for crop type (i.e., maize, wheat and others) recognition from street-level imagery based on a convolutional neural network using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery using the Picture Pile application. The classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for others. Given that wheat and maize are two of the most common food crops grown globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved global crop type monitoring. Challenges remain in addressing the noise aspect of street-level imagery (i.e., buildings, hedgerows, automobiles, etc.) and uncertainties due to differences in the time of day and location. Such an approach could also be applied to developing other in situ data sets from street-level imagery, e.g., for land use mapping or socioeconomic indicators.

An enumeration of spatial autocorrelation’s (SA’s) polyvalent forms occurred nearly three decades ago. Attempts to conceive and disseminate a clearer explanation of it employ metaphors seeking to better relate SA to a student’s or spatial scientist’s personal knowledge databank. However, not one of these uses the jigsaw puzzle metaphor appearing in this paper, which exploits an analogy between concrete visual content organization and abstract map patterns of attributes. It not only makes SA easier to understand, which furnishes a useful pedagogic tool for teaching novices and others about it, but also discloses that many georeferenced data should contain a positive–negative SA mixture. Empirical examples corroborate this mixture’s existence, as well as the tendency for marked positive SA to characterize remotely sensed and moderate (net) positive SA to characterize socio-economic/demographic, georeferenced data.

This paper analyzes the performance of the open-source OpenDroneMap image processing software (ODM) across multiple platforms. We tested desktop and laptop computers as well as high-performance cloud computing and supercomputers. Multiple machine configurations (CPU cores and memory) were used. We used eBee S.O.D.A. drone image datasets from Namibia and northern Finland. For testing, we used the OpenDroneMap command line tool with default settings and the fast orthophoto option, which produced a good quality orthomosaic. We also used the “rerun-all option” to ensure that all jobs started from the same point. Our results show that ODM processing time is dependent upon the number of images, a high number of which can lead to high memory demands, with low memory leading to an excessively long processing time. Adding additional CPU cores is beneficial to ODM up to a certain limit. A 20-core machine seems optimal for a dataset of about 1000 images, although 10 cores will result only in slightly longer processing times. We did not find any indication of improvement when processing larger datasets using 40-core machines. For 1000 images, 64 GB memory seems to be sufficient, but for larger datasets of about 8000 images, higher memory of up to 256 GB is required for efficient processing. ODM can use GPU acceleration, at least in some processing stages, reducing processing time. In comparison to commercial software, ODM seems to be slower, but the created orthomosaics are of equal quality.