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Background Avena fatua and A. sterilis are challenging to distinguish due to their strong similarities. However, Artificial Neural Networks (ANN) can effectively extract patterns and identify these species. We measured seed traits of Avena species from 122 locations across the Balkans and from some populations from southern, western, and central Europe (total over 22 000 seeds). The inputs for the ANN model included seed mass, size, color, hairiness, and placement of the awn attachment on the lemma. Results The ANN model achieved high classification accuracy for A. fatua and A. sterilis (R2 > 0.99, RASE < 0.0003) with no misclassification. Incorporating geographic coordinates as inputs also resulted in successful classification (R2 > 0.99, RASE < 0.000001) with no misclassification. This highlights the significant influence of geographic coordinates on the occurrence of Avena species. The models revealed hidden relationships between morphological traits that are not easily detectable through traditional statistical methods. For example, seed color can be partially predicted by other seed traits combined with geographic coordinates. When comparing the two species, A. fatua predominantly had the lemma attachment point in the upper half, while A. sterilis had it in the lower half. A. sterilis exhibited slightly longer seeds and hairs than A. fatua , while seed hairiness and mass were similar in both species. A. fatua populations primarily had brown, light brown, and black colors, while A. sterilis populations had black, brown, and yellow colors. Conclusions Distinguishing A. fatua from A. sterilis based solely on individual characteristics is challenging due to their shared traits and considerable variability of traits within each species. However, it is possible to classify these species by combining multiple seed traits. This approach also has significant potential for exploring relationships among different traits that are typically difficult to assess using conventional methods.

Satellite images are always partitioned into regular patches with smaller sizes and then individually fed into deep neural networks (DNNs) for semantic segmentation. The underlying assumption is that these images are independent of one another in terms of geographic spatial information. However, it is well known that many land-cover or land-use categories share common regional characteristics within a certain spatial scale. For example, the style of buildings may change from one city or country to another. In this paper, we explore some deep learning approaches integrated with geospatial hash codes to improve the semantic segmentation results of satellite images. Specifically, the geographic coordinates of satellite images are encoded into a string of binary codes using the geohash method. Then, the binary codes of the geographic coordinates are fed into the deep neural network using three different methods in order to enhance the semantic segmentation ability of the deep neural network for satellite images. Experiments on three datasets demonstrate the effectiveness of embedding geographic coordinates into the neural networks. Our method yields a significant improvement over previous methods that do not use geospatial information.

Image-based geo-localization focuses on predicting the geographic information of query images by matching them with annotated images in a database. To facilitate relevant studies, researchers collect numerous images to build the datasets, which explore many challenges faced in real-world geo-localization applications, significantly improving their practicability. However, a crucial challenge that often arises is overlooked, named the cross-time challenge in this paper, i.e., if query and database images are taken from the same landmark but at different time periods, the significant difference in their image content caused by the time gap will notably increase the difficulty of image matching, consequently reducing geo-localization accuracy. The cross-time challenge has a greater negative influence on non-real-time geo-localization applications, particularly those involving a long time span between query and database images, such as satellite-view geo-localization. Furthermore, the rough geographic information (e.g., names) instead of precise coordinates provided by most existing datasets limits the geo-localization accuracy. Therefore, to solve these problems, we propose a dataset, MTGL40-5, which contains remote sensing (RS) satellite images captured from 40 large-scale geographic locations spanning five different years. These large-scale images are split to create query images and a database with landmark labels for geo-localization. By observing images from the same landmark but at different time periods, the cross-time challenge becomes more evident. Thus, MTGL40-5 supports researchers in tackling this challenge and further improving the practicability of geo-localization. Moreover, it provides additional geographic coordinate information, enabling the study of high-accuracy geo-localization. Based on the proposed MTGL40-5 dataset, many existing geo-localization methods, including state-of-the-art approaches, struggle to produce satisfactory results when facing the cross-time challenge. This highlights the importance of proposing MTGL40-5 to address the limitations of current methods in effectively solving the cross-time challenge.

Building maintenance plays an increasingly important role as buildings age. During maintenance, it is necessary to analyze building defects and record their locations when performing exterior inspections. Hence, this study proposes an automatic three-dimensional (3D) modeling method based on image analysis using unmanned aerial vehicle (UAV) flights and convolutional neural networks. A geographic information system is used to acquire geographic coordinate points (GCPs) for the geometry of the building, and a UAV is flown to collect the GCPs and images, which provide location information on the building elements and defects. Comparisons revealed that the generated 3D models were similar to the actual buildings. Next, the recorded locations of the building defects and the actual locations were examined, and the results confirmed that the defects were generated correctly. Our findings indicated that the proposed method can improve building maintenance. However, it has several limitations, which provide directions for future research.

This study focuses on developing a mathematical model to compute geographic coordinates (GCS) for any point on Earth based on the horizontal coordinates of observable celestial bodies with known motion at a specific time, such as the Sun, Moon, planets, and stars. Also, the model is applicable during daylight hours, as it can be implemented using sunlight shadows. Additionally, the position of a celestial body at a given time enables precise determination of geographic directions, such as true north. This facilitates accurate alignment of buildings intended for specific orientations, including scientific facilities, temples, and residential buildings designed to harmonize with wind patterns and sunlight exposure. This method is cost-effective, as it does not rely on Global Navigation Satellite System (GNSS) like GPS, BDS, GALILEO, and GLONASS. In addition, a prototype device was engineered to instantaneously determine the Sun’s horizontal coordinates using photovoltaic cells.

The Anthropocene is witnessing a loss of biodiversity, with well-documented declines in the diversity of ecosystems and species. For intraspecific genetic diversity, however, we lack even basic knowledge on its global distribution. We georeferenced 92,801 mitochondrial sequences for >4500 species of terrestrial mammals and amphibians, and found that genetic diversity is 27% higher in the tropics than in nontropical regions. Overall, habitats that are more affected by humans hold less genetic diversity than wilder regions, although results for mammals are sensitive to choice of genetic locus. Our study associates geographic coordinates with publicly available genetic sequences at a massive scale, yielding an opportunity to investigate both the drivers of this component of biodiversity and the genetic consequences of the anthropogenic modification of nature.

Dams and reservoirs are among the most widespread human-made infrastructures on Earth. Despite their societal and environmental significance, spatial inventories of dams and reservoirs, even for the large ones, are insufficient. A dilemma of the existing georeferenced dam datasets is the polarized focus on either dam quantity and spatial coverage (e.g., GlObal geOreferenced Database of Dams, GOODD) or detailed attributes for a limited dam quantity or region (e.g., GRanD (Global Reservoir and Dam database) and national inventories). One of the most comprehensive datasets, the World Register of Dams (WRD), maintained by the International Commission on Large Dams (ICOLD), documents nearly 60 000 dams with an extensive suite of attributes. Unfortunately, the WRD records provide no geographic coordinates, limiting the benefits of their attributes for spatially explicit applications. To bridge the gap between attribute accessibility and spatial explicitness, we introduce the Georeferenced global Dams And Reservoirs (GeoDAR) dataset, created by utilizing the Google Maps geocoding application programming interface (API) and multi-source inventories. We release GeoDAR in two successive versions (v1.0 and v1.1) at https://doi.org/10.5281/zenodo.6163413 (Wang et al., 2022). GeoDAR v1.0 holds 22 560 dam points georeferenced from the WRD, whereas v1.1 consists of (a) 24 783 dam points after a harmonization between GeoDAR v1.0 and GRanD v1.3 and (b) 21 515 reservoir polygons retrieved from high-resolution water masks based on a one-to-one relationship between dams and reservoirs. Due to geocoding challenges, GeoDAR spatially resolved ∼ 40 % of the records in the WRD, which, however, comprise over 90 % of the total reservoir area, catchment area, and reservoir storage capacity. GeoDAR does not release the proprietary WRD attributes, but upon individual user requests we may provide assistance in associating GeoDAR spatial features with the WRD attribute information that users have acquired from ICOLD. Despite this limit, GeoDAR, with a dam quantity triple that of GRanD, significantly enhances the spatial details of smaller but more widespread dams and reservoirs and complements other existing global dam inventories. Along with its extended attribute accessibility, GeoDAR is expected to benefit a broad range of applications in hydrologic modeling, water resource management, ecosystem health, and energy planning.

The duality between \"niche\" and \"biotope\" proposed by G. Evelyn Hutchinson provides a powerful way to conceptualize and analyze biogeographical distributions in relation to spatial environmental patterns. Both Joseph Grinnell and Charles Elton had attributed niches to environments. Attributing niches, instead, to species, allowed Hutchinson's key innovation: the formal severing of physical place from environment that is expressed by the duality. In biogeography, the physical world (a spatial extension of what Hutchinson called the biotope) is conceived as a map, each point (or cell) of which is characterized by its geographical coordinates and the local values of n environmental attributes at a given time. Exactly the same n environmental attributes define the corresponding niche space, as niche axes, allowing reciprocal projections between the geographic distribution of a species, actual or potential, past or future, and its niche. In biogeographical terms, the realized niche has come to express not only the effects of species interactions (as Hutchinson intended), but also constraints of dispersal limitation and the lack of contemporary environments corresponding to parts of the fundamental niche. Hutchinson's duality has been used to classify and map environments; model potential species distributions under past, present, and future climates; study the distributions of invasive species; discover new species; and simulate increasingly more realistic worlds, leading to spatially explicit, stochastic models that encompass speciation, extinction, range expansion, and evolutionary adaptation to changing environments.

We analyze ozone trends in the upper troposphere and lower stratosphere (UTLS, ∼${\\sim} $ 300–50 hPa), using geographical (latitude‐pressure and latitude‐altitude) and, for the first time, dynamical (equivalent latitude‐potential temperature, EqL‐θ$\\theta $ ) coordinates. Trends are determined using linear least squares fits, multiple linear regression, and dynamical linear modeling. Regardless of the method, EqL‐θ$\\theta $improves consistency between trends across the UTLS, reduces large UT tropical uncertainties, alters the magnitude of mid‐latitude trends, and, most notably, in the Southern polar lower stratosphere, reveals statistically significant trends exceeding 8% per decade during Antarctic Spring. This provides further evidence of Antarctic ozone recovery. These robust trends are not captured using geographical coordinates. We argue that EqL‐θ$\\theta $enables more physically grounded interpretations of chemical ozone trends and their uncertainties, as EqL‐θ$\\theta $accounts for the adiabatic (reversible) transport of ozone.

The upper limit of fluoride concentration in water for human consumption is 1.5 ppm. Many studies have been carried out concerning the water fluoride concentration in wide areas around the world, but none have studied the change of fluoride concentration as a function of geographical coordinates and through time. This paper describes ‘microvariation’ of fluoride concentration among wells separated by less than 500 m in a month. On the other hand, ‘macrovariation’ is also studied describing changes among cities that are separated by more than 10 km and compared with fluoride concentrations measured 65 years ago. Fluoride concentration was measured in a wide geographical area of Argentina, which is 133,000 km 2 . Samples of water were collected from different regions. Macrovariation : Differences in fluoride concentration in well water among regions were found, as well as an increase in water fluoride concentration during seven decades. Microvariation : Daily water fluoride concentration in a specific area displayed a great variation in the measurements through time. In addition, wells with no more than 500 m of separation were measured at the same time and were significantly different. These results indicate that in order to determine the fluoride concentration of a region, different samples of the same area should be obtained and a sampling through time should also be done.