The coverage of ** Mbit/s **-net in Sweden in the period between 2014 and 2018 increased. The share of ** net area coverage grew from roughly ** percent in 2014 to up to ** percent as of 2018.

This dataset visually presents comprehensive maps detailing the extent of the mobile network coverage of Cellcard, Metfone, and Smart across Cambodia. This network map shows the location of available networks in the area where people live, work, and where they need to communicate or use the Internet.

When asked about "Attitudes towards the internet", most Mexican respondents pick "It is important to me to have mobile internet access in any place" as an answer. 56 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

A geospatial dataset containing polylines of transportation network in Vietnam. It contains the railways, the principal roads and the secondary roads.

When asked about "Attitudes towards the internet", most Chinese respondents pick "It is important to me to have mobile internet access in any place" as an answer. 48 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

When asked about "Attitudes towards the internet", most Japanese respondents pick "I'm concerned that my data is being misused on the internet" as an answer. 35 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

The Ontario Road Network (ORN) Composite product is a segmented derivative of the ORN Road Net Element (ORNELEM) data class. You can use it for mapping and general spatial analysis. Road segment information includes:addressingfull street namealternate street namespeed limitnumber of lanespavement statusroad classjurisdictionroute numberdirection of traffic flowshield type informationThe ORN is a provincewide geographic database of over 250,000 km of:municipal roadsprovincial highwaysresource and recreational roadsThe ORN is the authoritative source of roads data for the Government of Ontario. This product is derived from the  ORN Road Net Element  data class. It combines three types of geometry:road elementsferry connectionsvirtual roadsThis product also includes additional road feature layers including:blocked passagesunderpassestoll pointsstructuresAdditional DocumentationOntario Road Network (ORN) Composite - User Guide (Word)Data Capture Specifications for Road Net Elements - Guide to Best Practices for Acquisition (Word)GO-ITS 29 - Ontario Road Network StandardOntario Road Network - List of Partners (Word)StatusOn going: data is being continually updatedMaintenance and Update FrequencyMonthly: data is updated on a monthly basisContactOntario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

When asked about "Attitudes towards the internet", most Australian respondents pick "It is important to me to have mobile internet access in any place" as an answer. 54 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

Images obtained in low-light scenes are often accompanied by problems such as low visibility, blurred details, and color distortion, enhancing them can effectively improve the visual effect and provide favorable conditions for advanced visual tasks. In this study, we propose a Multi-Technology Fusion of Low-light Image Enhancement Network (MTIE-Net) that modularizes the enhancement task. MTIE-Net consists of a residual dense decomposition network (RDD-Net) based on Retinex theory, an encoder-decoder denoising network (EDD-Net), and a parallel mixed attention-based self-calibrated illumination enhancement network (PCE-Net). The low-light image is first decomposed by RDD-Net into a lighting map and reflectance map; EDD-Net is used to process noise in the reflectance map; Finally, the lighting map is fused with the denoised reflectance map as an input to PCE-Net, using the Fourier transform for illumination enhancement and detail recovery in the frequency domain. Numerous experimental results show that MTIE-Net outperforms the comparison methods in terms of image visual quality enhancement improvement, denoising, and detail recovery. The application in nighttime face detection also fully demonstrates its promise as a pre-processing means in practical applications.