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This dataset provides the Global Naturalized Alien Flora (GloNAF) database, version 1.2. GloNAF represents a data compendium on the occurrence and identity of naturalized alien vascular plant taxa across geographic regions (e.g. countries, states, provinces, districts, islands) around the globe. The dataset includes 13,939 taxa and covers 1,029 regions (including 381 islands). The dataset is based on 210 data sources. For each taxon-by-region combination, we provide information on whether the taxon is considered to be naturalized in the specific region (i.e. has established self-sustaining populations in the wild). Non-native taxa are marked as “alien”, when it is not clear whether they are naturalized. To facilitate alignment with other plant databases, we provide for each taxon the name as given in the original data source and the standardized taxon and family names used by The Plant List Version 1.1 (http://www.theplantlist.org/). We provide an ESRI shapefile including polygons for each region and information on whether it is an island or a mainland region, the country and the Taxonomic Databases Working Group (TDWG) regions it is part of (TDWG levels 1–4). We also provide several variables that can be used to filter the data according to quality and completeness of alien taxon lists, which vary among the combinations of regions and data sources. A previous version of the GloNAF dataset (version 1.1) has already been used in several studies on, for example, historical spatial flows of taxa between continents and geographical patterns and determinants of naturalization across different taxonomic groups. We intend the updated and expanded GloNAF version presented here to be a global resource useful for studying plant invasions and changes in biodiversity from regional to global scales. We release these data into the public domain under a Creative Commons Zero license waiver (https://creativecommons.org/share-your-work/public-domain/cc0/). When you use the data in your publication, we request that you cite this data paper. If GloNAF is a major part of the data analyzed in your study, you should consider inviting the GloNAF core team (see Metadata S1: Originators in the Overall project description) as collaborators. If you plan to use the GloNAF dataset, we encourage you to contact the GloNAF core team to check whether there have been recent updates of the dataset, and whether similar analyses are already ongoing.

Background Shapefiles are a geospatial vector data format used to indicate geographic features in geographic information systems (GIS) software. Shapefiles are used in high-throughput phenotyping plant breeding and agronomic studies to identify plots from aerial imagery and extract remote sensing data. However, the process of manually creating shapefiles is tedious and error prone. Current options that assist in shapefile generation suffer from issues such as installation processes that require a degree of programming knowledge or inefficient methods for incorporating plot-level information from field books. In this study, we have developed a program called ‘SHP Buddy’, a QGIS plugin that provides accessible and intuitive functions that quickly generate shapefiles for common experimental layouts used in agricultural research. Results SHP Buddy is a free and open source QGIS plugin that is easily downloaded directly from the QGIS plugin repository. It provides options for generating serpentine replicated and unreplicated experimental layouts. Further, SHP Buddy is the first of its type to provide an intuitive method for removing non-experimental plots, such as non-experimental “fill” plots at the end of experiments or plots in irrigation wheel tracks. Plot information is easily incorporated by uploading a field book CSV file that contains a column of matching plot numbers. Lastly, plot dimensions can be modified to produce more precise regions of interest. Conclusions SHP Buddy substantially reduces the time and increases the accuracy of shapefile generation. This results in reliable shapefiles that improve record keeping and the quality of high-throughput phenotyping data extracted. By working natively in QGIS, SHP Buddy provides an efficient solution to shapefile generation while maintaining a low learning curve.

Saltmarshes are extremely valuable but often overlooked ecosystems, contributing to livelihoods locally and globally through the associated ecosystem services they provide, including fish production, carbon storage and coastal protection. Despite their importance, knowledge of the current spatial distribution (occurrence and extent) of saltmarshes is incomplete. In light of increasing anthropogenic and environmental pressures on coastal ecosystems, global data on the occurrence and extent of saltmarshes are needed to draw attention to these critical ecosystems and to the benefits they generate for people. Such data can support resource management, strengthen decision-making and facilitate tracking of progress towards global conservation targets set by multilateral environmental agreements, such as the Aichi Biodiversity Targets of the United Nations' (UN's) Strategic Plan for Biodiversity 2011-2020, the Sustainable Development Goals of the UN's 2030 Agenda for Sustainable Development and the Ramsar Convention. Here, we present the most complete dataset on saltmarsh occurrence and extent at the global scale. This dataset collates 350,985 individual occurrences of saltmarshes and presents the first global estimate of their known extent. The dataset captures locational and contextual data for saltmarsh in 99 countries worldwide. A total of 5,495,089 hectares of mapped saltmarsh across 43 countries and territories are represented in a Geographic Information Systems polygon shapefile. This estimate is at the relatively low end of previous estimates (2.2-40 Mha), however, we took the conservative approach in the mapping exercise and there are notable areas in Canada, Northern Russia, South America and Africa where saltmarshes are known to occur that require additional spatial data. Nevertheless, the most extensive saltmarsh worldwide are found outside the tropics, notably including the low-lying, ice-free coasts, bays and estuaries of the North Atlantic which are well represented in our global polygon dataset. Therefore, despite the gaps, we believe that, while incomplete, our global polygon data cover many of the important areas in Europe, the USA and Australia.

The development of a smart city and digital twin requires the integration of Building Information Modeling (BIM) and Geographic Information Systems (GIS), where BIM models are to be integrated into GIS for visualization and/or analysis. However, the intrinsic differences between BIM and GIS have led to enormous problems in BIM-to-GIS data conversion, and the use of City Geography Markup Language (CityGML) has further escalated this issue. This study aims to facilitate the use of BIM models in GIS by proposing using the shapefile format, and a creative approach for converting Industry Foundation Classes (IFC) to shapefile was developed by integrating a computer graphics technique. Thirteen building models were used to validate the proposed method. The result shows that: (1) the IFC-to-shapefile conversion is easier and more flexible to realize than the IFC-to-CityGML conversion, and (2) the computer graphics technique can improve the efficiency and reliability of BIM-to-GIS data conversion. This study can facilitate the use of BIM information in GIS and benefit studies working on digital twins and smart cities where building models are to be processed and integrated in GIS, or any other studies that need to manipulate IFC geometry in depth.

Panoptic segmentation combines instance and semantic predictions, allowing the detection of countable objects and different backgrounds simultaneously. Effectively approaching panoptic segmentation in remotely sensed data is very promising since it provides a complete classification, especially in areas with many elements as the urban setting. However, some difficulties have prevented the growth of this task: (a) it is very laborious to label large images with many classes, (b) there is no software for generating DL samples in the panoptic segmentation format, (c) remote sensing images are often very large requiring methods for selecting and generating samples, and (d) most available software is not friendly to remote sensing data formats (e.g., TIFF). Thus, this study aims to increase the operability of panoptic segmentation in remote sensing by providing: (1) a pipeline for generating panoptic segmentation datasets, (2) software to create deep learning samples in the Common Objects in Context (COCO) annotation format automatically, (3) a novel dataset, (4) leverage the Detectron2 software for compatibility with remote sensing data, and (5) evaluate this task on the urban setting. The proposed pipeline considers three inputs (original image, semantic image, and panoptic image), and our software uses these inputs alongside point shapefiles to automatically generate samples in the COCO annotation format. We generated 3400 samples with 512 × 512 pixel dimensions and evaluated the dataset using Panoptic-FPN. Besides, the metric analysis considered semantic, instance, and panoptic metrics, obtaining 93.865 mean intersection over union (mIoU), 47.691 Average (AP) Precision, and 64.979 Panoptic Quality (PQ). Our study presents the first effective pipeline for generating panoptic segmentation data for remote sensing targets.

The integration of building information modeling (BIM) and geographic information system (GIS) is attracting more attention than ever due to its potential benefits for both the architecture, engineering, and construction (AEC) domain and the geospatial industry. The main challenge in BIM and GIS integrated application comes from the fundamental data conversion, especially for the geometric information. BIM and GIS use different modeling paradigms to represent objects. The BIM dataset takes, for example, Industry Foundation Classes (IFC) that use solid models, such as boundary representation (B-Rep), swept solid, constructive solid geometry (CSG), and clipping, while the GIS dataset mainly uses surface models or B-Rep. The fundamental data conversion between BIM and GIS is the foundation of BIM and GIS integrated application. However, the efficiency of data conversion has been greatly impaired by the human intervention needed, especially for the conversion of the clipping geometry. The goal of this study is to automate the conversion of IFC clipping representation into the shapefile format. A process-level approach was developed with an algorithm for instantiating unbounded half spaces using B-Rep. Four IFC models were used to validate the proposed method. The results show that (1) the proposed approach can successfully automate the conversion of IFC clipping representation into the shapefile format; and (2) increasing boundary size has no effect on the file size of unbounded half spaces, but slightly increases the producing time of half spaces and processing time of building components. The efficiency of this study can be further improved by using an open-source package, instead of using the low-efficiency packages provided by ArcGIS.

Over the last 50 years, countries across North Africa and the Middle East have seen a significant increase in dam construction which, notwithstanding their benefits, have endangered archaeological heritage. Archaeological surveys and salvage excavations have been carried out in threatened areas in the past, but the formation of reservoirs often resulted in the permanent loss of archaeological data. However, in 2018, a sharp fall in the water level of the Mosul Dam reservoir led to the emersion of the archaeological site of Kemune and allowed for its brief and targeted investigation. Reservoir water level change is not unique to the Mosul Dam, but it is a phenomenon affecting most of the artificial lakes of present-day Iraq. However, to know in advance which sites will be exposed due to a decrease in water level can be a challenging task, especially without any previous knowledge, field investigation, or high-resolution satellite image. Nonetheless, by using time-series medium-resolution satellite images, combined to obtain spectral indexes for different years, it is possible to monitor “patterns” of emerging archaeological sites from three major Iraqi reservoirs: Mosul, Haditha and Hamrin lake. The Normalised Difference Water Index (NDWI), generated from annual composites of Landsat and Sentinel-2 images, allow us to distinguish between water bodies and other land surfaces. When coupled with a pixel analysis of each image, the index can provide a mean for highlighting whether an archaeological site is submerged or not. Moreover, using a zonal histogram algorithm in QGIS over polygon shapefiles that represent a site surface, it is possible to assess the area of a site that has been exposed over time. The same analyses were carried out on monthly composites for the year 2018, to assess the impact of monthly variation of the water level on the archaeological sites. The results from both analyses have been visually evaluated using medium-resolution true colour images for specific years and locations and with 3 m resolution Planetscope images for 2018. Understanding emersion “patterns” of known archaeological sites provides a useful tool for targeted rescue excavation, while also expanding the knowledge of the post-flooding impact on cultural heritage in the regions under study.

The climate change is one of the important problem of the current situation in the world. The urban heat island intensity is a major problem of increasing the climate condition in the developed and developing countries. In current situation, the growth of population in the Pakistan causes over population in the cities. The population of Larkana is increasing rapidly day by day. The purpose of this research was to investigate the impact of Urbanization on the Climate. In this proposed research study, two types of data were collected (i) satellite data of Thematic Mapper (TM) Landsat 5 which was downloaded from the United States Geological Survey (USGS) of 1990, 2000, and 2010, furthermore satellite data of 2023 downloaded the Landsat 8 from USGS. (ii) Second data used from secondary sources of population census report of Pakistan 1981, 1998, 2017 and 2023 was collected from the Pakistan Statistics Bureau. The land surface temperature was found from satellite data of 1990, 2000, 2010 and 2023. The average temperature in 1990 was 4.25 0 C greater than 2000 in summer season and average temperature of 2010 was 4.73 0 C less than from 2023 in summer season. The average temperature in 1990 was 3.15 0 C less than from 2000 in winter season and in 2022 was 1 0 C higher than 2010 in winter season average temperature. Recently census reported above 579,000 populations lived in the urban city of Larkana. The shape file of the Larkana classification total area showed 41 Square kilometers. The supervised classification showed that settlement increased from 8.16 Square kilometers in 1990 to 23.98 Square kilometers in 2023. The correlation was shown between urban expansion and the growth of population strongly positive to each other. Another finding relationship between urban heat islands with urban expansion that correlation showed a positive relationship between each other.

Aim Establishments of non‐native forest pests (insects and pathogens) continue to increase worldwide with growing numbers of introductions and changes in invasion pathways. Quantifying spatio‐temporal patterns in establishment locations and subsequent invasion dynamics can provide insight into the underlying mechanisms driving invasions and assist biosecurity agencies with prioritizing areas for proactive surveillance and management. Location United States of America. Time period 1794–2018. Major taxa studied Insecta, plant pathogens. Methods Using locations of first discovery and county‐level occurrence data for 101 non‐native pests across the contiguous USA, we (a) quantified spatial patterns in discovery points and county‐level species richness with spatial point process models and spatial hotspot analyses, respectively, and (b) identified potential proxies for propagule pressure (e.g., human population density) associated with these observed patterns. Results Discovery points were highly aggregated in space and located in areas with high densities of ports and roads. Although concentrated in the north‐eastern USA, discovery points also occurred farther west and became less aggregated as time progressed. Invasion hotspots were more common in the north‐east. Geographic patterns of discovery points and hotspots varied substantially among pest origins (i.e., global region of pests’ native ranges) and pest feeding guilds. Significant variation in invasion richness was attributed to the patterns of first discovery locations. Data and shapefiles comprising analyses are provided. Main conclusions Use of spatial point pattern analyses provided a quantitative characterization of the central role of human activities in establishment of non‐native pests. Moreover, the decreased aggregation of discovery points through time suggests that invasion pathways to certain areas in the USA have either been created or intensified by human activities. Overall, our results suggest that spatio‐temporal variability in the intensity of invasion pathways has resulted in marked geographic patterns of establishment and contributed to current macroscale patterns of pest invasion in the USA.

The data set covers a 101-yr period (1915–2016) of quadrat-based plant sampling at the Jornada Experimental Range in southern New Mexico. At each sampling event, a pantograph was used to record the location and perimeter of living plants within permanent quadrats. Basal area was recorded for perennial grass species, canopy cover area was recorded for shrub species, and all other perennial species were recorded as point data. The data set includes 122 1 × 1 m permanent quadrats, although not all quadrats were sampled in each year of the study and there is a gap in monitoring from 1980 to 1995. These data provide a unique opportunity to investigate changes in the plant community over 100 yr of variation in precipitation and other environmental conditions. We provide the following data and data formats: (1) the digitized maps in shapefile format; (2) a data table containing coordinates (x, y) of perennial species within quadrats, including cover area for grasses and shrubs; (3) a data table of counts of annual plant individuals per quadrat; (4) a species list indicating growth form and habit of recorded species; (5) a table of dates when each quadrat was sampled; (6) a table of the pasture each quadrat was located within (note that pasture boundaries have changed over time); (7) a table of depth to petrocalcic layer measurements taken at quadrat locations; (8) a table of particle size analysis of soil samples taken at quadrat locations; (9) a table of topographic characteristics of quadrat locations (e.g., concave or convex topography). Pantograph sampling is currently conducted at 5-yr intervals by USDA-ARS staff, and new data will be added periodically to the EDI Data Portal Repository (see section V.E.2). This information is released under the Creative Commons license—Attribution—CC BYand the consumer of these data is required to cite it appropriately in any publication that results from its use.