Geospatial Datasets Research Articles

Identifying the essential influencing factors of landslide susceptibility models based on hybrid-optimized machine learning with different grid resolutions: a case of Sino-Pakistani Karakorum Highway.

This study attempts to explore the essential influencing factors of landslides and explores the effects of different datasets on landslide susceptibility mapping (LSM) at six grid resolutions (i.e., 10 m, 30 m, 300 m, 1000 m, 2000 m, and 3000 m). Firstly, the geospatial dataset of 21 influencing factors was extracted from 1847 historical landslide InSAR (Interferometric Synthetic Aperture Radar) points, which were taken as a sample for the Sino-Pakistani Karakorum Highway. Secondly, Spearman correlation coefficient (SCC), random forest feature selection (RFFS), and their combinations (SCC-RFFS) were selected at different grid resolutions to identify the essential influencing factors from the 21 original factors. A random division into training set (70%) and test set (30%) was performed. Then, the LSM models for the original influencing factors and the selected influencing factors were constructed separately using machine learning models. Finally, the reasonableness of the essential influencing factors was verified by comparing the accuracy of the models under different grid resolutions. The results show that (1) relief degree of land surface (RDLS), SPI, and rainfall have significant effects on landslide occurrence. (2) The primary elements (i.e., RDLS, slop, rainfall) are less affected by the grid resolution, while the secondary elements (TWI) are more affected by the grid resolution. (3) At 30 m, the SCC-RFFS-RF model can get the highest landslide susceptibility model accuracy. The prediction will also provide scientific guidance for the allocation of land resources on a regional and global scale, and minimize the human and economic costs along the highway, while ensuring safe highway operations.

Developing a spatially explicit global oil and gas infrastructure database for characterizing methane emission sources at high resolution

Abstract. Reducing oil and gas methane emissions is crucially important for limiting the rate of human-induced climate warming. As the capacity of multi-scale measurements of global oil and gas methane emissions has advanced in recent years, including the emerging ecosystem of satellite and airborne remote sensing platforms, a clear need for an openly accessible and regularly updated global inventory of oil and gas infrastructure has emerged as an important tool for characterizing and tracking methane emission sources. In this study, we develop a spatially explicit database of global oil and gas infrastructure, focusing on the acquisition, curation, and integration of public-domain geospatial datasets reported by official government sources and by industry, academic research institutions, and other non-government entities. We focus on the major oil and gas facility types that are key sources of measured methane emissions, including production wells, offshore production platforms, natural gas compressor stations, processing facilities, liquefied natural gas facilities, crude oil refineries, and pipelines. The first version of this global geospatial database (Oil and Gas Infrastructure Mapping database, OGIM_v1) contains a total of ∼ 6 million features, including 2.6 million point locations of major oil and gas facility types and over 2.6×106 km of pipelines globally. For each facility record, we include key attributes – such as facility type, operational status, oil and gas production and capacity information, operator names, and installation dates – which enable detailed methane source assessment and attribution analytics. Using the OGIM database, we demonstrate facility-level source attribution for multiple airborne remote-sensing-detected methane point sources from the Permian Basin, which is the largest oil-producing basin in the United States. In addition to source attribution, we present other major applications of this oil and gas infrastructure database in relation to methane emission assessment, including the development of an improved bottom-up methane emission inventory at high resolution (1 km × 1 km). We also discuss the tracking of changes in basin-level oil and gas activity and the development of policy-relevant analytics and insights for targeted methane mitigation. This work and the OGIM database, which we anticipate updating on a regular cadence, help fulfill a crucial oil and gas geospatial data need, in support of the assessment, attribution, and mitigation of global oil and gas methane emissions at high resolution. OGIM_v1 is publicly available at https://doi.org/10.5281/zenodo.7466757 (Omara et al., 2022a).

Groundwater-dependent ecosystems at risk – global hotspot analysis and implications

Many land-based ecosystems are dependent on groundwater and could be threatened by human groundwater abstraction. One key challenge for the description of associated impacts is the initial localisation of groundwater-dependent ecosystems (GDEs). This usually requires a mixture of extensive site-specific data collection and the use of geospatial datasets and remote sensing techniques. To date, no study has succeeded in identifying different types of GDEs in parallel worldwide. The main objective of this work is to perform a global screening analysis to identify GDE potentials rather than GDE locations. In addition, potential risks to GDEs from groundwater abstraction shall be identified. We defined nine key indicators that capture GDE potentials and associated risks on a global grid of 0.5° spatial resolution. Groundwater-dependent streams, wetlands and vegetation were covered, and a GDE index was formulated incorporating the following three aspects: the extent of groundwater use per GDE type, GDE diversity and GDE presence by land cover. The results show that GDE potentials are widely distributed across the globe, but with different distribution patterns depending on the type of ecosystem. The highest overall potential for GDEs is found in tropical regions, followed by arid and temperate climates. The GDE potentials were validated against regional studies, which showed a trend of increasing matching characteristics towards higher GDE potentials, but also inconsistencies upon closer analysis. Thus, the results can be used as first-order estimates only, which would need to be explored in the context of more site-specific analyses. Identified risks to GDEs from groundwater abstraction are more geographically limited and concentrated in the US and Mexico, the Iberian Peninsula and the Maghreb, as well as Central, South and East Asia. The derived findings on GDEs and associated risks can be useful for prioritising future research and can be integrated into sustainability-related tools such as the water footprint.

Use of High-Resolution Land Cover Maps to Support the Maintenance of the NWI Geospatial Dataset: A Case Study in a Coastal New Orleans Region

The National Wetlands Inventory (NWI) is the most comprehensive wetland geospatial dataset in the United States. However, it can be time-consuming and costly to maintain. This study introduces automated algorithms and methods to support NWI maintenance. Through a wall-to-wall comparison between NWI and Coastal Change Analysis Program (C-CAP) datasets, a pixel-level difference product was generated at 1 m resolution. Building upon this, supplementary attributes describing wetland changes were incorporated into each NWI polygon. Additionally, new water polygons were extracted from C-CAP data, and regional statistics regarding wetland changes were computed for HUC12 watersheds. The 1 m difference product can indicate specific wetland change locations, such as wetland loss to impervious surfaces, the gain of open water bodies from uplands, and the conversion of drier vegetated wetlands to open water. The supplementary attributes can indicate the amount and percentage of wetland loss or water regime change for NWI polygons. Extracted new water polygons can serve as preliminary materials for generating NWI standard-compliant products, expediating NWI maintenance processes while reducing costs. Regional statistics of wetland change can help target watersheds with the most significant changes for maintenance, thereby reducing work areas. The approaches we present hold significant value in supporting NWI maintenance.

Availability of Fundamental Geospatial Datasets for monitoring the Sustainable Development Goals in Africa

Availability of Fundamental Geospatial Datasets for monitoring the Sustainable Development Goals in Africa

Urban Green Development and Resilient Cities: A First Insight into Urban Forest Planning in Italy

The research proposes an application of a modification of the 3–30–300 rule to identify areas that require Urban Forestry implementation in small and medium-sized Roman and/or medieval urban areas. The selected case study is that of Asti in Piedmont, Italy. An open source, cross-platform desktop geographic information system is used to process geospatial datasets via qualitative analyses of electoral sections (or wards). An analysis of the number and distribution of trees around each building is performed, in addition to the calculation of tree canopy cover and distance between buildings and green spaces. Findings reveal that 64 out of 70 wards have an average of at least three trees per building and sufficient green areas of at least 0.5 hectares within 300 m of the buildings. Additionally, the tree canopy cover ranges from approximately 0.6% (lowest) to about 55% (highest) for the electoral sections. Lastly, findings suggest that the highly built-up urban fabric in these areas may significantly affect the availability and quality of green spaces. In conclusion, the case study proves the benefits of applying the 3–30–300 rule to small and medium-sized urban areas using an integrated assessment approach based on nature-based solutions and ecosystem services.

Building the case for a bottom-up approach to SDI stakeholder collaboration – experiences from South Africa

Abstract. Inclusive governance remains a challenge for the South African Spatial Data Infrastructure (SASDI). This is evidenced through the limited participation of sub-national government in SASDI and the challenges they experience with access to fundamental geospatial data. The purpose of this paper is to make recommendations for enhancing the role of sub-national government and other users in SASDI. Semi-structured interviews were conducted with representatives from municipalities and the municipal land use application process was used as a case study. The interviews confirmed that municipalities are striving to operate efficiently, but that limited access to fundamental geospatial data constrains them. Based on the results, it is recommended that access to geospatial data be improved through an integrated approach to stakeholder collaboration, coordinated by the SASDI. Such collaboration would proactively pursue inclusive governance and could significantly improve access to fundamental geospatial datasets.

Impact of adaptive water-saving and phaseout strategies on water withdrawal in China's coal-fired power industry under carbon constraints

Coal-fired power generation occupies a dominant position in China's power sector and is known for its high water requirement. Limited research exists on how water withdrawal for coal-fired power generation responds to policy and technical factors, as well as spatial variations in the future. To address this gap, this study utilizes a geospatial dataset at the coal-fired power unit level and an improved calculation method for water withdrawal to investigate the evolving trend of water withdrawal in coal-fired power generation, considering the influences of carbon emission reduction, adaptive water-saving measures, and phaseout strategies. The results demonstrate that the improved water withdrawal calculation method yields superior simulation outcomes compared to existing approaches. The study reveals substantial variations in coal-fired power generation under different carbon emission reduction scenarios. Despite the business-as-usual (BAU) scenario where coal-fired power generation continues to dominate the power sector, implementing various adaptive water-saving scenarios results in national water withdrawal estimates ranging from 42.9 to 59.4 billion m3 in 2050. In contrast, water withdrawal is reduced by 37.6% to 42.9% under the 2.0℃ target scenario and by 86.5% to 97.5% under the 1.5℃ target scenario. Technological advancements play a crucial role in reducing water withdrawal for coal-fired power generation, particularly in northwest China. The transformation of cooling systems significantly decreases water withdrawal nationwide and in the regions that predominantly relied on once-through cooling. The deployment of carbon capture and storage (CCS) technology in coal-fired power plants in the northwest region creates short-term pressure on regional water resources. Under strict carbon emissions constraints, adopting the phaseout strategy with guaranteed lifetime results in relatively higher water withdrawal for coal-fired power generation, and the variations in coal-fired power plants configuration across provinces lead to different responses in water withdrawal changes when facing the same unit retirement strategy.

Open Geospatial Data Integration in Game Engine for Urban Digital Twin Applications

Increasing interest has recently been shown towards the digital twins of cities. These urban digital twins utilize 3D city models together with other data sources, such as open data, which can be accessed, e.g., through application programming interfaces (APIs). A large amount of open geospatial data is already available through various open data portals and APIs, such as REST and MQTT. Game engines, on the other hand, have been seen as highly potential platforms for urban digital twins, with recent development increasing the useability of geospatial data in game engines. This study explores the possibilities of game engines for urban digital twins by integrating and visualizing API-based open geospatial datasets with a 3D city model data set in a game engine environment. Three open data integration approaches are demonstrated, and the performance of these approaches is tested for urban digital twins. Based on the results, modern game engines offer a great platform for creating urban digital twin applications; however, some problems and limitations must be overcome before the use of geospatial data in game engines becomes feasible on a larger scale.

Spatial Estimation of Soil Erosion Using RUSLE Model: A Case Study of Sangareddy Telangna State, India

Soil erosion is a major environmental challenge that affects agricultural productivity, water quality, and land management. To address this issue, accurate estimation and spatial mapping of soil erosion rates are essential for effective soil conservation planning and management. This study focuses on utilizing the Revised Universal Soil Loss Equation (RUSLE) model to estimate and map soil erosion rates in the Sangareddy district of Telangana State, India. The RUSLE model integrates several factors contributing to soil erosion, including rainfall erosivity, soil erodibility, slope length and steepness, cover management factor, and conservation practices. These factors were derived from geospatial datasets, including rainfall records, soil characteristics, topographic data, land use/land cover maps, and conservation practices information. By applying the RUSLE model, the study estimates the average annual soil erosion rates across the Sangareddy district. The spatial distribution of soil erosion is then mapped using geographic information system (GIS) techniques, providing valuable insights into the area’s most susceptible to erosion. These findings can inform decision-makers and land managers in prioritizing and implementing appropriate soil conservation strategies to mitigate erosion and protect valuable soil resources. The spatial estimation of soil erosion using the RUSLE model provides a scientific basis for land-use planning, conservation program implementation, and policy formulation to promote sustainable land management practices. It highlights the need for effective erosion control measures, such as contour farming, terracing, afforestation, and appropriate land use practices, in order to minimize soil erosion and preserve the soil quality in Sangareddy and similar regions.

Creating a digital historical map of the German-inhabited areas in southern Transylvania (central Romania) in 1848

ABSTRACT The paper describes the creation of a historical reference map of the mid-southern region of Transylvania (nowadays in central Romania) in 1848. This area was shaped by the presence of the Transylvanian Saxons, the ethnic German descendants of the settlers invited here by the kings of Hungary during the High Middle Ages. Transylvania became part of the Habsburg Empire at the end of the seventeenth century and was elevated to the status of a Grand Principality in 1765. The map is primarily based on historical cartographic documents and written publications, but also integrates digital geo-spatial datasets. It was produced at a scale of 1:400 000 and includes the hydrography, the administrative boundaries, the main roads, the postal stations and all the settlements attested in the Transylvanian Saxon administrative subdivisions before the mid-nineteenth century.

Mapping global non-floodplain wetlands.

Non-floodplain wetlands - those located outside the floodplains - have emerged as integral components to watershed resilience, contributing hydrologic and biogeochemical functions affecting watershed-scale flooding extent, drought magnitude, and water-quality maintenance. However, the absence of a global dataset of non-floodplain wetlands limits their necessary incorporation into water quality and quantity management decisions and affects wetland-focused wildlife habitat conservation outcomes. We addressed this critical need by developing a publicly available "Global NFW" (Non-Floodplain Wetland) dataset, comprised of a global river-floodplain map at 90 m resolution coupled with a global ensemble wetland map incorporating multiple wetland-focused data layers. The floodplain, wetland, and non-floodplain wetland spatial data developed here were successfully validated within 21 large and heterogenous basins across the conterminous United States. We identified nearly 33 million potential non-floodplain wetlands with an estimated global extent of over 16×106 km2. Non-floodplain wetland pixels comprised 53% of globally identified wetland pixels, meaning the majority of the globe's wetlands likely occur external to river floodplains and coastal habitats. The identified global NFWs were typically small (median 0.039 km2), with a global median size ranging from 0.018-0.138 km2. This novel geospatial Global NFW static dataset advances wetland conservation and resource-management goals while providing a foundation for global non-floodplain wetland functional assessments, facilitating non-floodplain wetland inclusion in hydrological, biogeochemical, and biological model development. The data are freely available through the United States Environmental Protection Agency's Environmental Dataset Gateway (https://gaftp.epa.gov/EPADataCommons/ORD/Global_NonFloodplain_Wetlands/, last access: 24 May 2023) and through https://doi.org/10.23719/1528331 (Lane et al., 2023a).

AUTOMATION OF NORMATIVE MONEY VALUE OF LANDS OF SETTLEMENTS WITH THE USE OF GEOINFORMATION TECHNOLOGIES

In this paper, the constituent elements of geoinformation support for the normative monetary valuation of the lands of settlements has been considered. A set of geospatial data necessary for land value of settlements at each of its stages has been formed. A method of economic and planning zoning based on the theory of fuzzy sets is proposed, namely the method of arranging objects according to a set of features, which allows applying real values of evaluation indicators and calculating their integral values. The algorithm of automated zoning with the use of GIS tools has been considered. Based on the proposed set of geospatial data, an ArcGIS tool was created using the Model Builder application, which allows you to determine the normative money value of a separate land plot and generate a report.

Deep learning for filtering the ground from ALS point clouds: A dataset, evaluations and issues

The capability of partially penetrating vegetation canopy and efficiently collecting high-precision point clouds over large areas makes airborne laser scanning (ALS) a valuable tool for various geospatial applications. However, automated ground filtering (GF), one fundamental and challenging step for most ALS applications, has remained a widely researched yet unsolved problem for decades. The recent breakthroughs in supervised deep learning (DL) techniques, which rely on sufficient and high-quality labeled datasets, provide a new solution to better solve this problem. Unfortunately, public 3D geospatial datasets are scarce, especially for those tailored for the landform-scale GF task. Moreover, whether advanced deep neural networks (DNNs) can be well-scaled to the problem of GF remains an open question. To comprehensively advance the development of effective DL-based GF pipelines, we first publish an ultra-large-scale GF dataset built upon open-access ALS point clouds of four different countries worldwide, which covers over 47 km2 and nine different terrain scenes. Then, multiple attractive advantages of DL techniques in GF are evaluated through extensive experimental comparisons with traditional GF methods on the presented dataset. Furthermore, we reveal several issues faced by generalizing existing advanced 3D DNNs into GF tasks with a series of in-depth experimental analyses. Finally, some promising directions for future research are suggested in response to the identified challenges. Our dataset, named OpenGF, is available at https://github.com/Nathan-UW/OpenGF.

Degradation of floodplain integrity within the contiguous United States

Despite the numerous hydrological, geological, and ecological benefits produced by floodplain landscapes, floodplains continue to be degraded by human activities at a much higher rate than other landscape types. This large-scale landscape modification has been widely recognized, yet a comprehensive, national dataset quantifying the degree to which human activities are responsible for this degradation has not previously been evaluated. In this research, we analyze floodplain integrity for the contiguous United States by spatially quantifying the impact of anthropogenic stressors on almost 80,000 floodplain units. We demonstrate the prevalence of human modifications through widely available geospatial datasets, which we use to quantify indicators of floodplain integrity for five essential floodplain functions of flood attenuation, groundwater storage, habitat provision, sediment regulation, and organics and solute regulation. Our results show that floodplain degradation is spatially heterogeneous and that the integrity of nearly 70% of floodplains in the United States is poor. We highlight that quantifying the integrity of spatially explicit floodplain elements can allow for restoration efforts to be targeted to the areas in most desperate need of preservation.

Tourism Morphometrics in Venice: Constructing a Tourism Services Index (TSI) to unmask the spatial interplay between tourism and urban form

Understanding how the built environment affects tourism saturation in cities is of central importance to the efficacy of tourism management strategies that rely on spatial datasets to inform space-specific policies. By employing the historic city of Venice (Italy) as a case study, this article provides a nuanced methodology defined as “tourism morphometrics” to inform tourism management plans and to detect urban form configurations prone to “overtourism.” The research contribution is twofold: first, it harnesses spatial datasets on lodging (6642 Airbnb listings, InsideAirbnb), dining (997 restaurants, Tripadvisor), and shopping (226 stores, Google Points of Interest) to construct a highly-granular ‘Tourism Services Index’ (TSI) that reveals the spatial distribution of tourism service capacity in Venice; second, it empirically reveals the spatial interplay between urban form parameters—landmarks, bridges, and street network—and TSI values. More specifically, statistical analyses found that TSI scores were positively associated with the number of street intersections, street widths values, and proximity to landmarks and bridges in Venice. The results offer insights into the relationship between tourism services and the spatial features of urban environments in which visitors move and behave. By leveraging by-product geospatial datasets, this research argues for a renewed management of overtourism through space-specific planning models.

Small Hydropower Assessment of Uganda Based on Multisource Geospatial Data

This article is based on the freely available data of the web-based hydropower map HYPOSO, which the authors compiled. Only the Ugandan river network and associated hydropower potential are highlighted here, using freely available geospatial datasets. The main objective was to assess Ugandan river and stream hydropower potential, compare it with previous assessments, and identify potential sites for small hydropower plant installation. GIS techniques were extensively used to analyse hydrological and other related geospatial data. The stream-reach power potential was determined based on channel slope, the length between tributaries, and the average flow derived from a specific runoff distribution map. Stream profiles extracted from the river network’s digital elevation model were validated against previous assessments. Uganda’s hydropower potential was determined in various patterns, and its values were compared with prior estimates. Around 500 potential high-energy intensity stream reaches and new potential areas for small hydropower plant development were identified in this country, considering a range of characteristics. Statistical datasets were analysed, and their straightforward summaries were presented. These summary characteristics of hydropower potential are necessary for decision-makers to foster hydropower development in this country.

Capturing deprived areas using unsupervised machine learning and open data: a case study in São Paulo, Brazil

ABSTRACT Managing the rapid growth of deprived areas (commonly known as slums, informal settlements, etc.) in cities of Low- to Middle-Income Countries (LMICs) demands detailed and consistent information that is often unavailable. Recent Earth Observation (EO) mapping approaches with supervised classification models overlook the diversity of deprived areas and require resource-intensive training sets. In this study, we analyse the potential of unsupervised machine learning (ML) models to capture intra-urban diversity of deprived areas in São Paulo, using solely open geodata. We provide a workflow of characterising deprivation at a city scale with a disaggregated approach, offering scalability and transferability potential. First, we extract a pool of spatial features from open geospatial datasets to characterise the morphological and environmental conditions of the study area. After input preparation, we train and optimise a k-means model, including a coupled feature importance tool. Four cluster types emerged with different deprivation aspects such as higher and lower accessibility to services and infrastructure, sparser and denser occupation; regular and complex morphology; flat and steep terrain. This alternative methodology to capture diversity of deprived areas with open EO-based features can inform locally targeted, thus more efficient, urban policies and interventions.

How do crosswalk delays affect pedestrian access in zoning areas? Walking access reduction by signalized crosswalks in Seoul, South Korea

Long waiting times at signalized crosswalks delay pedestrians and reduce the effective walksheds around urban facilities. In this study, we identify to what extent crosswalk delays at signalized crosswalks reduce pedestrians’ effective walking access and how the impact of crosswalk delays is differentiated by zoning types in Seoul, Korea. We demonstrate a pedestrian network analysis method considering crosswalk delay times, using a detailed pedestrian path geospatial layer and dataset of signal timings. Our results show significant reductions in walkshed size and served users. Employing multiple regression modeling, we identified that these reductions were not uniform and depended on zoning patterns in urban areas. In addition, our findings suggest that commercial zoning areas and recent residential developments, comprised of high-rise towers in mega-blocks, create greater delays to pedestrians and constrict effective walksheds. We discuss the need for further studies and the potential contribution of zoning system and its land use managements on improving walking conditions such as underground space developments and mixed residential density patterns.

Does the Region Make a Difference? Social Inequality in Transitions to Adulthood across Cohorts in West Germany

Since the availability of study opportunities is unequal across regions, entering the phase of post-secondary education is often accompanied by leaving (the parental) home. In these life-course transitions, social background plays a crucial role in the form of resources, e.g., to afford living independently while studying. We use a unique set of geospatial data by aggregating information on the municipality level flexibly within travel-time radii and link the data to the German National Educational Panel Study (NEPS) to examine whether socioeconomic and university infrastructure in the region can compensate for a lack of parental resources (i) in transitions to university and (ii) in the likelihood of staying in or leaving the home region to study. We analyse this across cohorts between 1986 and 2015. We find that the region makes a difference: a wide availability of universities in the region offsets social inequality in the transition to university. Yet, the increasing availability of alternative educational routes over time via vocational training and universities of applied sciences causes this moderating influence to decrease across cohorts. Our findings call for gearing the attention of policymakers towards the varying relevance of regional conditions over time and across social groups for individuals’ life-course transitions.