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Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world. The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale. This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery. A multi-neuro modeling methodology building on a simple Convolution Neural Networks architecture for pixel-wise image classification of built-up areas is developed. The core features of the proposed model are the image patch of size 5 × 5 pixels adequate for describing built-up areas from Sentinel-2 imagery and the lightweight topology with a total number of 1,448,578 trainable parameters and 4 2D convolutional layers and 2 flattened layers. The deployment of the model on the global Sentinel-2 image composite provides the most detailed and complete map reporting about built-up areas for reference year 2018. The validation of the results with an independent reference dataset of building footprints covering 277 sites across the world establishes the reliability of the built-up layer produced by the proposed framework and the model robustness. The results of this study contribute to cutting-edge research in the field of automated built-up areas mapping from remote sensing data and establish a new reference layer for the analysis of the spatial distribution of human settlements across the rural–urban continuum. [ABSTRACT FROM AUTHOR]

The validation of built‐up areas derived from different sensors is crucial for gaining a deeper understanding of the consistency and interoperability between them. This article presents the methodology and results of an inter‐sensor comparison of built‐up area data derived from Landsat, Sentinel‐1, Sentinel‐2, and SPOT5/SPOT6. The assessment was performed for 13 cities across the world for which cartographic reference building footprints were available. Several validation approaches were used: cumulative built‐up curve analysis, pixel‐by‐pixel performance metrics, and regression analysis. The results indicate that Sentinel‐1 and Sentinel‐2 contribute greatly to improved built‐up area detection compared to Landsat, within the global human settlement framework. However, Sentinel‐2 tends to show high omission errors while Landsat tends to have the lowest omission error. The built‐up area obtained from SPOT5/SPOT6 shows high consistency with the reference data for all European cities, and hence can potentially be considered as a reference dataset for wall‐to‐wall validation in Europe. [ABSTRACT FROM AUTHOR]

The application of last-generation spatial data modelling, integrating Earth Observation, population, economic and other spatially explicit data, enables insights into the sustainability of the global urbanisation processes with unprecedented detail, consistency, and international comparability. In this study, the land use efficiency indicator, as developed in the Sustainable Development Goals, is assessed globally for the first time at the level of Functional Urban Areas (FUAs). Each FUA includes the city and its commuting zone as inferred from statistical modelling of available spatial data. FUAs represent the economic area of influence of each urban centre. Hence, the analysis of land consumption within their boundary has significance in the fields of spatial planning and policy analyses as well as many other research areas. We utilize the boundaries of more than 9,000 FUAs to estimate the land use efficiency between 1990 and 2015, by using population and built-up area data extracted from the Global Human Settlement Layer. This analysis shows how, in the observed period, FUAs in low-income countries of the Global South evolved with rates of population growth surpassing the ones of land consumption. However, in almost all regions of the globe, more than half of the FUAs improved their land use efficiency in recent years (2000–2015) with respect to the previous decade (1990–2000). Our study concludes that the spatial expansion of urban areas within FUA boundaries is reducing compactness of settlements, and that settlements located within FUAs do not display higher land use efficiency than those outside FUAs. • Land Use Efficiency (SDG 11.3.1) is assessed in all FUAs of the world. • FUAs in the Global South show population growing faster than land consumption. • Development trajectories in FUAs show an improvement of efficiency in recent years. • Settlements within FUA commuting areas are not more efficient than those outside. • The proportion of urban centre population does not imply higher efficiency of the FUA. [ABSTRACT FROM AUTHOR]

There has been significant advancement in various aspects of scientific knowledge concerning the role of cerebellum in the etiopathogenesis of autism. In the current consensus paper, we will observe the diversity of opinions regarding the involvement of this important site in the pathology of autism. Recent emergent findings in literature related to cerebellar involvement in autism are discussed, including: cerebellar pathology, cerebellar imaging and symptom expression in autism, cerebellar genetics, cerebellar immune function, oxidative stress and mitochondrial dysfunction, GABAergic and glutamatergic systems, cholinergic, dopaminergic, serotonergic, and oxytocin-related changes in autism, motor control and cognitive deficits, cerebellar coordination of movements and cognition, gene-environment interactions, therapeutics in autism, and relevant animal models of autism. Points of consensus include presence of abnormal cerebellar anatomy, abnormal neurotransmitter systems, oxidative stress, cerebellar motor and cognitive deficits, and neuroinflammation in subjects with autism. Undefined areas or areas requiring further investigation include lack of treatment options for core symptoms of autism, vermal hypoplasia, and other vermal abnormalities as a consistent feature of autism, mechanisms underlying cerebellar contributions to cognition, and unknown mechanisms underlying neuroinflammation. [ABSTRACT FROM AUTHOR]

Monitoring sustainable urban development requires comparable geospatial information on cities across several thematic domains. Here we present the first global database combining such information with city extents. The Global Human Settlement Urban Centre Database (GHS-UCDB) is produced by geospatial data integration to characterise more than 10,000 urban centres worldwide. The database is multi-dimensional and multi-temporal, containing 28 variables across five domains and having multitemporal attributes for one or more epochs when the UC are delineated (1975-1990-2000-2015). Delineation of urban centres for the year 2015 is performed via a logic of grid cell population density, population size, and grid cell contiguity defined by the Degree of Urbanisation method. Each of the urban centres has 160 attributes, including a validation assessment. The novel aspects of this database concern the thematic richness and temporal depth of the variables (across geography, socio-economic, environmental, disaster risk reduction, and sustainable development domains) and the type of geo-information provided (location and extent), featuring an overall consistency that allows comparative analyses across locations and time., (© 2024. European Union.)

Several global high-resolution built-up surface products have emerged over the last five years, taking full advantage of open sources of satellite data such as Landsat and Sentinel. However, these data sets require validation that is independent of the producers of these products. To fill this gap, we designed a validation sample set of 50 K locations using a stratified sampling approach independent of any existing global built-up surface products. We launched a crowdsourcing campaign using Geo-Wiki ( https://www.geo-wiki.org/ ) to visually interpret this sample set for built-up surfaces using very high-resolution satellite images as a source of reference data for labelling the samples, with a minimum of five validations per sample location. Data were collected for 10 m sub-pixels in an 80 × 80 m grid to allow for geo-registration errors as well as the application of different validation modes including exact pixel matching to majority or percentage agreement. The data set presented in this paper is suitable for the validation and inter-comparison of multiple products of built-up areas., (© 2022. The Author(s).)

The dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Two main types of data are currently used to map stocks, night-time lights (NTL) from Earth-observing (EO) satellites and cadastral information. We present an alternative approach for broad-scale material stock mapping based on freely available high-resolution EO imagery and OpenStreetMap data. Maps of built-up surface area, building height, and building types were derived from optical Sentinel-2 and radar Sentinel-1 satellite data to map patterns of material stocks for Austria and Germany. Using material intensity factors, we calculated the mass of different types of buildings and infrastructures, distinguishing eight types of materials, at 10 m spatial resolution. The total mass of buildings and infrastructures in 2018 amounted to ∼5 Gt in Austria and ∼38 Gt in Germany (AT: ∼540 t/cap, DE: ∼450 t/cap). Cross-checks with independent data sources at various scales suggested that the method may yield more complete results than other data sources but could not rule out possible overestimations. The method yields thematic differentiations not possible with NTL, avoids the use of costly cadastral data, and is suitable for mapping larger areas and tracing trends over time.

Like humans, rhesus monkeys show cognitive decline and this review considers what structural age-related changes underlie this decline. Some structural measures do not alter significantly with age. These include brain weight, overall cortical thickness; numbers of cortical neurons; and numbers of astrocytes and microglial cells. Other structural measures change with age, but the change does not correlate with cognitive decline. These changes include nerve fiber loss from some fiber tracts, degeneration, and regeneration of myelin sheaths, and increase in the frequency of oligodendrocytes. Among the structural measures that increase in frequency with age and also correlate with cognitive decline are the increased frequency of degenerating myelin sheaths and a loss of nerve fibers from some fiber tracts; and the loss of synapses and dendritic spines from upper layers of prefrontal cortex. Consequently, the existing data suggest that cognitive decline correlates with changes in myelinated nerve fibers and with disconnections between and within cortical areas, as reflected by the age-related loss of synapses and of dendritic spines from some cortical areas., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Increasing evidence indicates that the GABAergic system in cerebellar and limbic structures is affected in autism. We extended our previous study that found reduced [(3)H]flunitrazepam-labeled benzodiazepine sites in the autistic hippocampus to determine whether this reduction was due to a decrease in binding site number (B (max)) or altered affinity (K (d)) to bind to the ligand. Quantitation of hippocampal lamina demonstrated a 20% reduction in B (max) indicating a trend toward a decreased number of benzodiazepine binding sites in the autistic group but normal K (d) values. A reduction in the number of hippocampal benzodiazepine binding sites suggests alterations in the modulation of GABA(A) receptors in the presence of GABA in the autistic brain, possibly resulting in altered inhibitory functioning of hippocampal circuitry.

Autism is a behaviourally defined disorder, initially described by Kanner in 1943. By definition, symptoms are manifested by 36 months of age and are characterized by delayed and disordered language, impaired social interaction, abnormal responses to sensory stimuli, events and objects, poor eye contact, an insistence on sameness, an unusual capacity for rote memory, repetitive and stereotypic behaviour and a normal physical appearance. Relatively few neuropathological studies have been performed on the brains of autistic subjects. Of those reported, abnormalities have been described in the cerebral cortex, the brainstem, the limbic system and the cerebellum. Although those with the disorder present with a specific set of core characteristics, each individual patient is somewhat different from another. Thus, it should not be surprising that the brains of these subjects should show a wide range of abnormalities. However, it is important to delineate the anatomic features, which are common to all cases, regardless of age, sex and IQ, in order to begin to understand the central neurobiological profile of this disorder. The results of our systematic studies indicate that the anatomic features that are consistently abnormal in all cases include reduced numbers of Purkinje cells in the cerebellum, and small tightly packed neurons in the entorhinal cortex and in the medially placed nuclei of the amygdala. It is known that the limbic system is important for learning and memory, and that the amygdala plays a role in emotion and behaviour. Research in the cerebellum indicates that this structure is important as a modulator of a variety of brain functions and impacts on language processing, anticipatory and motor planning, mental imagery and timed sequencing. Defining the differences and similarities in brain anatomy in autism and correlating these observations with detailed clinical descriptions of the patient may allow us greater insight into the underlying neurobiology of this disorder.