The India Lights platform shows light output at night for 20 years for 600,000 villages across India. The Defense Meteorological Satellite Program (DMSP) has taken pictures of the Earth every night from 1993 to 2013. Researchers at the University of Michigan, in collaboration with the World Bank, used the DMSP images to extract the data you see on the India Lights platform. Each point you see on the map represents the light output of a specific village at a specific point in time. On the district level, the map also allows you to filter to view villages that have participated in India’s flagship electrification program. This tremendous trove of data can be used to look at changes in light output, which can be used to complement research about electrification in the country. About the Data: The DMSP raster images have a resolution of 30 arc-seconds, equal to roughly 1 square kilometer at the equator. Each pixel of the image is assigned a number on a relative scale from 0 to 63, with 0 indicating no light output and 63 indicating the highest level of output. This number is relative and may change depending on the gain settings of the satellite’s sensor, which constantly adjusts to current conditions as it takes pictures throughout the day and at night. Methodology To derive a single measurement, the light output values were extracted from the raster image for each date for the pixels that correspond to each village's approximate latitude and longitude coordinates. We then processed the data through a series of filtering and aggregation steps. First, we filtered out data with too much cloud cover and solar glare, according to recommendations from the National Oceanic and Atmospheric Administration (NOAA). We aggregated the resulting 4.4 billion data points by taking the median measurement for each village over the course of a month. We adjusted for differences among satellites using a multiple regression on year and satellite to isolate the effect of each satellite. To analyze data on the state and district level, we also determined the median village light output within each administrative boundary for each month in the twenty-year time span. These monthly aggregates for each village, district, and state are the data that we have made accessible through the API. To generate the map and light curve visualizations that are presented on this site, we performed some additional data processing. For the light curves, we used a rolling average to smooth out the noise due to wide fluctuations inherent in satellite measurements. For the map, we took a random sample of 10% of the villages, stratified over districts to ensure good coverage across regions of varying village density. Acknowledgments The India Lights project is a collaboration between Development Seed, The World Bank, and Dr. Brian Min at the University of Michigan. •Satellite base map © Mapbox. •India village locations derived from India VillageMap © 2011-2015 ML Infomap. •India population data and district boundaries © 2011-2015 ML Infomap. •Data for reference map of Uttar Pradesh, India, from Natural Earth Data •Banerjee, Sudeshna Ghosh; Barnes, Douglas; Singh, Bipul; Mayer, Kristy; Samad, Hussain. 2014. Power for all : electricity access challenge in India. A World Bank study. Washington, DC ; World Bank Group. •Hsu, Feng-Chi, Kimberly Baugh, Tilottama Ghosh, Mikhail Zhizhin, and Christopher Elvidge. "DMSP-OLS Radiance Calibrated Nighttime Lights Time Series with Intercalibration." Remote Sensing 7.2 (2015): 1855-876. Web. •Min, Brian. Monitoring Rural Electrification by Satellite. Tech. World Bank, 30 Dec. 2014. Web. •Min, Brian. Power and the Vote: Elections and Electricity in the Developing World. New York and Cambridge: Cambridge University Press. 2015. •Min, Brian, and Kwawu Mensan Gaba. Tracking Electrification in Vietnam Using Nighttime Lights. Remote Sensing 6.10 (2014): 9511-529. •Min, Brian, and Kwawu Mensan Gaba, Ousmane Fall Sarr, Alassane Agalassou. Detection of Rural Electrification in Africa using DMSP-OLS Night Lights Imagery. International Journal of Remote Sensing 34.22 (2013):8118-8141. Disclaimer Country borders or names do not necessarily reflect the World Bank Group's official position. The map is for illustrative purposes and does not imply the expression of any opinion on the part of the World Bank, concerning the legal status of any country or territory or concerning the delimitation of frontiers or boundaries.

Light Every Night - World Bank Nighttime Light Data – provides open access to all nightly imagery and data from the Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS DNB) from 2012-2020 and the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) from 1992-2013. The underlying data are sourced from the NOAA National Centers for Environmental Information (NCEI) archive. Additional processing by the University of Michigan enables access in Cloud Optimized GeoTIFF format (COG) and search using the Spatial Temporal Asset Catalog (STAC) standard. The data is published and openly available under the terms of the World Bank’s open data license.

Nighttime satellite imagery were accessed via Google Earth Engine). Version 4 of the DMSP-OLS Nighttime Lights Time Series consists of cloud-free composites made using all the available archived DMSP-OLS smooth resolution data for calendar years. In cases where two satellites were collecting data - two composites were produced. The products are 30 arc second grids, spanning -180 to 180 degrees longitude and -65 to 75 degrees latitude. Several attributes are included - we used stable_lights which represents lights from cities, towns, and other sites with persistent lighting, including gas flares. Ephemeral events, such as fires have been discarded. The background noise was identified and replaced with values of zero.These data were provided to Google Earth Engine by teh National Centers for Environmental Information - National Oceanic and Atmospheric Administration of the United States (see Supporting Documentation).CANUE staff exported the annual data and extracted values of annual mean nighttime brightness for all postal codes in Canada for each year from 1992 to 2013 (DMTI Spatial, 2015).

The Defense Meteorological Program (DMSP) Operational Line-Scan System (OLS) has a unique capability to detect visible and near-infrared (VNIR) emission sources at night. Version 4 of the DMSP-OLS Nighttime Lights Time Series consists of cloud-free composites made using all the available archived DMSP-OLS smooth resolution data for calendar years. In cases where two satellites were collecting data, two composites were produced. Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency.

The Day Night Band (DNB), from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Joint Polar-orbiting Satellite System (JPSS) satellites, provides global daily measurements of nocturnal visible and near-infrared light that are suitable for Earth system science and applications studies. The VIIRS Nighttime Lights Monthly Cloud-Free Composite is produced using average radiance composite images and excludes any data impacted by stray light. There are many areas of the globe where it is impossible to get good quality data coverage for that month. This can be due to cloud-cover, especially in the tropical regions, or due to solar illumination, as happens toward the poles in their respective summer months. Therefore, when used for analysis, it is imperative that users of these data utilize the cloud-free observations file (band-2) and not assume a value of zero in the average radiance image (band-1) means that no lights were observed.Geographic CoverageGlobal from 75N to 65SCoverage is affected by the length of day during different times of the year. For example, summer time in the northern hemisphere will have less nighttime coverage due to longer days.Temporal CoverageMonthly from January 2014 - February 2024BandsBand-1: Monthly average radianceUnits: (avg_rade9h) nW/cm2/srBand-2: Cloud free observations per monthUnits: DaysCoordinate Reference SystemSource images are stored in Geographic WGS84 (EPSG:4326) and transformed on-the-fly to Web Mercator (EPSG:3857)Spatial Resolution15 arc second (~500m at the Equator)VIIRS Nighttime Lights product generation is credited to the Earth Observation Group, Payne Institute for Public Policy.

Monthly average radiance composite images using nighttime data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB). As these data are composited monthly, there are many areas of the globe where it is impossible to get good quality data coverage for that month. This can be due to …

This nighttime lights product known as avg_lights_x_pct is derived from the average visible band digital number (DN) of cloud-free light detections multiplied by the percent frequency of light detection. The inclusion of the percent frequency of detection term normalizes the resulting digital values for variations in the persistence of lighting. For instance, the value for a light only detected half the time is discounted by 50%. Note that this product contains detections from fires and a variable amount of background noise. This is the product used to infer gas flaring volumes from the nighttime lights.

This composite set is a component of the DMSP-OLS nighttime lights imagery for the years 1992-2013. The individual images are cloud-free composites made using all the available archived DMSP-OLS (Defense Meteorological Satellite Program - Operational Linescan System) smooth resolution data for calendar years. In cases where two satellites were collecting data - two composites were produced. The products are 30 arc second grids, spanning -180 to 180 degrees longitude and -65 to 75 degrees latitude. Each composite set is named with the satellite and the year (F121995 is from DMSP satellite number F12 for the year 1995). Five ArcGIS image services are available, each containing images from 1992-2013.

Data creation: 2016-01-01

Contact points:

Metadata Contact: National Geophysical Data Center

Resource Contact: NOAA's National Geophysical Data Center NOAA

Resource Contact: NOAA National Centers for Environmental Information (NCEI)

Data lineage:

The products are 30 arc second grids, spanning -180 to 180 degrees longitude and -65 to 75 degrees latitude. A number of constraints are used to select the highest quality data for entry into the composites: * Data are from the center half of the 3000 km wide OLS swaths. Lights in the center half have better geolocation, are smaller, and have more consistent radiometry. * Sunlit data are excluded based on the solar elevation angle. * Glare is excluded based on solar elevation angle. * Moonlit data are excluded based on a calculation of lunar illuminance. * Observations with clouds are excluded based on clouds identified with the OLS thermal band data and NCEP surface temperature grids. * Lighting features from the aurora have been excluded in the northern hemisphere on an orbit-by-orbit manner using visual inspection.

More information on the attached file (gcv4_readme.txt) in the Distribution info section of this metadata.

Resource constraints:

Terms of Use: Whenever using or distributing DMSP data or derived images, please credit NOAA's National Centers for Environmental Information (NCEI). In details: Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency.

Online resources:

NOAA National Centers for Environmental Information (NCEI) DMSP Data Download

The Earth Observations Group (EOG) is producing a version 1 suite of average radiance composite images using nighttime data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB). Prior to averaging, the DNB data is filtered to exclude data impacted by stray light, lightning, lunar illumination, and cloud-cover. Cloud-cover is determined using the VIIRS Cloud Mask product (VCM). In addition, data near the edges of the swath are not included in the composites (aggregation zones 29-32). Temporal averaging is done on a monthly and annual basis. The version 1 series of monthly composites has not been filtered to screen out lights from aurora, fires, boats, and other temporal lights. However, the annual composites have layers with additional separation, removing temporal lights and background (non-light) values. The version 1 products span the globe from 75N latitude to 65S. The products are produced in 15 arc-second geographic grids and are made available in geotiff format as a set of 6 tiles. The tiles are cut at the equator and each span 120 degrees of latitude. Each tile is actually a set of images containing average radiance values and numbers of available observations. In the monthly composites, there are many areas of the globe where it is impossible to get good quality data coverage for that month. This can be due to cloud-cover, especially in the tropical regions, or due to solar illumination, as happens toward the poles in their respective summer months. Therefore, it is imperative that users of these data utilize the cloud-free observations file and not assume a value of zero in the average radiance image means that no lights were observed. The version 1 monthly series is run globally using two different configurations. The first excludes any data impacted by stray light. The second includes these data if the radiance vales have undergone the stray-light correction procedure (Reference). These two configurations are denoted in the filenames as "vcm" and "vcmsl" respectively. The "vcmsl" version, that includes the stray-light corrected data, will have more data coverage toward the poles, but will be of reduced quality. It is up to the users to determine which set is best for their applications. The annual versions are only made with the “vcm” version, excluding any data impacted by stray light. Filenaming convention: The version 1 composite products have 7 filename fields that are separated by an underscore "_". Internal to each field there can be an additional dash separator "-". These fields are followed by a filename extension. The fields are described below using this example filename: SVDNB_npp_20140501-20140531_global_vcmcfg_v10_c201502061154.avg_rade9 Field 1: VIIRS SDR or Product that made the composite "SVDNB" Field 2: satellite name "npp" Field 3: date range "20140501-20140531" Field 4: ROI "global" Field 5: config shortname "vcmcfg" Field 6: version "v10" is version 1.0 Field 7: creation date/time Extension: avg_rade9 The annual products can have other values for the config shortname (Field 5). They are: "vcm-orm" (VIIRS Cloud Mask - Outlier Removed) This product contains cloud-free average radiance values that have undergone an outlier removal process to filter out fires and other ephemeral lights. "vcm-orm-ntl" (VIIRS Cloud Mask - Outlier Removed - Nighttime Lights) This product contains the "vcm-orm" average, with background (non-lights) set to zero. "vcm-ntl" (VIIRS Cloud Mask - Nighttime Lights) This product contains the "vcm" average, with background (non-lights) set to zero. Data types/formats: To reach the widest community of users, files are delivered in compressed tarballs, each containing a set of 2 geotiffs. Files with extensions "avg_rade9" contain floating point radiance values with units in nanoWatts/cm2/sr. Note that the original DNB radiance values have been multiplied by 1E9. This was done to alleviate issues some software packages were having with the very small numbers in the original units. Files with extension "cf_cvg" are integer counts of the number of cloud-free coverages, or observations, that went in to constructing the average radiance image. Files with extension “cvg” are integer counts of the number of coverages or total observations available (regardless of cloud-cover). Credit: When using the data please credit the product generation to the Earth Observation Group, Payne Institute for Public Policy.

The VIIRS Plus DMSP Change in Lights (VIIRS+DMSP dLIGHT) data set fuses nighttime lights imagery from the U.S. Air Force Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) with a stable night light composite from the next generation Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band to map the spatial distribution and temporal evolution of global nighttime lights between 1992 and 2015. The product visualizes changes in both brightness and extent of nocturnal low lights over two decades while minimizing the spatial overextent (overglow) and bright saturation that compromise the DMSP-OLS composites. The map product utilizes annual DMSP-OLS stable lights composites, produced by the NOAA Earth Observation Group and archived at the NOAA National Geophysical Data Center (NGDC), in a tri-temporal global change map. To achieve greater spatial resolution and radiometric accuracy, the DMSP-OLS composites are co-registered and fused with the 2015 VIIRS annual composite from NGDC. The final product therefore retains the spatial detail and dynamic range of the VIIRS product, and the decadal change information from DMSP-OLS images.

These products are derived from RGB (red/green/blue) images, a satellite processing technique that uses a combination of satellite sensor bands (also called channels) and applies a red/green/blue (RGB) filter to each of them. The result is a false-color image, i.e. an image that does not correspond to what the human eye would see, but offers high contrast between different cloud types and surface features. The on-board sensor of a weather satellite obtains two basic types of information: visible light data (reflected light) reflecting off clouds and different surface types, also known as "reflectance", and infrared data (emitted radiation) which are short-wave and long-wave radiation emitted by clouds and surface features. RGBs are specially designed to combine this type of satellite data, resulting in an information-rich final product. Other products are based on the enhancement of channel data for a single wavelength, also aimed at highlighting meteorological features of the observed surface or clouds, but in a simpler way since only a single wavelength is involved. This older approach is still useful today, as its simplicity makes image interpretation easier in some cases.

Monthly satellite data (SeaWiFS, MODIS, MERIS, VIIRS) over a 21-year period (1998-2018; from the Globcolour project; http://globcolour.org) are used to calculate the Photosynthetically Available Radiation (PAR) reaching the seafloor in the coastal zone (0 to 200 m depth). Depths are from the 2019 General Bathymetric Chart of the Oceans (GEBCO; https://www.gebco.net) gridded bathymetry data (1/240 degree resolution). […]

The Remote Sensing Satellite Market size was valued at USD 9.67 USD Billion in 2023 and is projected to reach USD 19.95 USD Billion by 2032, exhibiting a CAGR of 10.9 % during the forecast period. Remote Sensing Satellite is/are an orbiting spacecraft equipped with sensors and instruments designed to observe the Earth surface, atmosphere, and oceans from a distance. Using a variety of the light spectrum, these satellites collect data and return it to Earth. Some of the uses of Remote Sensing Satellites are in the field of environmental protection, disaster response and recovery, agricultural yield analysis, city planning and implementation Military intelligence. Remote Sensing Satellites can be broadly classified as optical and radar. Optical satellites take images using their onboard cameras which use visible and infrared light, while radar satellites send out microwave pulses that can pass through clouds/unseen darkness. A Remote Sensing Satellite is composed of four parts including sensors, data transfer systems, power supplies and control devices. Remote sensing is beneficial because it allows for the monitoring of satellites with respect to real-time, global coverage, as well as being more cost-effective than traditional data collection methods. The market currently shifts to using cheaper, improved small satellites known as CubeSats and integrating artificial intelligence for analysis and interpretation of data. Key drivers for this market are: Rising Demand for Satellite Communication Equipment Due to Growing Space Exploration Programs Will Aid Market Growth. Potential restraints include: Increasing Satellite Density in Lower Orbits may Decelerate Market Growth. Notable trends are: Drone Surveillance is a Key Trend Gaining Traction in the Maritime Security Market.

Metadata record for data from ASAC Project 2668 See the link below for public details on this project.

The dataset contains data in the following formats:

The *.met files contain the height, time, direction and range of a meteor detection.

The *.vel file contains meteor determined wind velocities: the horizontal and vertical velocities.

There are other ancillary parameters in each file but these are the main ones.

The parameters are described in the pdf document included in the dataset. We have been able to get IDL based reading routines from the radar company (ATRAD) but in general, one is expected to write ones own software for reading the datasets.

Public The gap in our knowledge of the mesosphere and lower thermosphere (MLT) has stemmed from a difficulty in probing this remote region of our atmosphere. Spanning the height range between 50 and 110 km, the MLT is sometimes jokingly termed the 'ignorosphere'. However, observations from sites in Antarctica can now be combined with satellite data to overcome the limitations of our observing techniques. This project seeks to learn more about the many processes that contribute to the character of this region, with the goal of enhancing our understanding of the earth's atmosphere and identifying the effects of global climate change.

Project objectives: This project aims to provide a point of focus within the Australian Antarctic Program for investigations of the polar mesosphere and lower thermosphere (MLT) using satellite observations. Ground-based measurements typically have excellent vertical and temporal resolution, but are limited in their horizontal coverage. Satellite observations, on the other hand, provide a global perspective that cannot be achieved with ground-based instruments. Our knowledge of the polar MLT and its role in the global climate system can be significantly enhanced through studies that combine ground-based and satellite based measurements.

The importance of ground-based measurements of the structure and dynamics of the polar MLT is underlined by the Australian Antarctic Program's support of the unique combination of experiments operated at Davis station. An MF (medium frequency) radar measures horizontal wind speeds in this region every few minutes. A VHF (very high frequency) radar, LIDAR (laser radar) and a spectrometer provide other wind and temperature measurements when conditions allow. And all of these instruments yield data with a temporal and altitude resolution that cannot be achieved using a satellite.

Satellite observations of the MLT have, until recently, neglected the polar regions. The Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics (TIMED) mission, whose primary goal is to investigate and understand the basic structure, variation, and energy balance of the MLT region and the Ionosphere [Yee, 2003], sought to redress this neglect. Since its launch in December 2001, the TIMED satellite has made observations that extend well into the polar regions and include the latitude of Davis

Significantly, the instigators of TIMED recognised the contribution that ground-based experiments will make to its scientific yield by explicitly including them in the mission. A group of Ground Based Investigators (GBIs) have been funded to facilitate the incorporation of ground-based data sets into TIMED activities. The Davis MF radar is one of the instruments to be included in the TIMED mission through this mechanism.

It is therefore timely to focus some of our research activity on the opportunities provided by satellites such as TIMED. The availability of polar satellite data extends the reach of our existing ground-based experiments and adds value to our scientific endeavours. As a result, the common goals of the TIMED mission and the Australian Antarctic Science Program are achieved, our understanding of the role of Antarctica in the global climate system is enhanced and our international scientific profile is increased.

A document providing further details about the history of the project is available for download at the provided URL.

Taken from the 2009-2010 Progress Report: Progress against objectives: -Adding value to satellite data and ground-based data: As a result of the Fulbright sponsored visit of co-investigator Palo in late 2008, it is now clear that, due to differences in the characteristics of space- and ground-based data, the design of techniques for combining data sets should be specific to the wave class being considered (principally planetary waves and tides).

Significant contributions to the Aeronomy of Ice in the Mesosphere (AIM) satellite mission have been made using the tidal observations and analysis that form part of project 674. In the context of the current project, progress has been made in the following areas.

The 2007/2008 season of southern hemisphere observations has become a focus because both the AIM satellite instruments and the Antarctic MF radars operated well for much of ...

Topographic data were collected along study reach 2 in Caulks Creek in Wildwood, Missouri, on multiple dates, using terrestrial light detection and ranging (t-lidar), Global Navigation Satellite System (GNSS), and conventional surveying techniques (Rydlund and Densmore, 2012). These data are high-resolution topography in laser scan format, collected using a tripod mounted t-lidar at multiple scan setups. Data collection software integrated and stored the range and angular measurements from the t-lidar equipment. Computer software was used to process the raw data, align the various scans in reference to one another, classify the data, and extract the topography data in a useable format. The topographic data are provided in four subsections (A-D) for each of the four survey dates (February 2022, August 2022, February 2023, and July 2023). The data represent the channel, bank, and near overbank surface and were saved with RGB (red-green-blue) color in LAS format.

The LICIACube mission receives Tracking and Navigation Files (TRK-2-34) from the Deep Space Network (DSN) collected primarily from signals emitted by the high gain antenna (HGA) onboard the LICIACube spacecraft. These are binary files of table data whose fields and format are described by the TRK-2-34 DSN Tracking System Data Archival Format document referenced in the LICIACube Radio Science SIS. These files have been sorted by data record type, of which there are approximately 18. Both mission navigators and those working on radio science investigations use these data.

Recently, the stable light products and radiance calibrated products from Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) have been useful for mapping global fossil fuel carbon dioxide (CO2) emissions at fine spatial resolution. However, few studies on this subject were conducted with the new-generation nighttime light data from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite, which has a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. Therefore, this study performed the first evaluation of the potential of NPP-VIIRS data in estimating the spatial distributions of global CO2 emissions (excluding power plant emissions). Through a disaggregating model, three global emission maps were then derived from population counts and three different types of nighttime lights data (NPP-VIIRS, the stable light data and radiance calibrated data of DMSP-OLS) for a comparative analysis. The results compared with the reference data of land cover in Beijing, Shanghai and Guangzhou show that the emission areas of map from NPP-VIIRS data have higher spatial consistency of the artificial surfaces and exhibit a more reasonable distribution of CO2 emission than those of other two maps from DMSP-OLS data. Besides, in contrast to two maps from DMSP-OLS data, the emission map from NPP-VIIRS data is closer to the Vulcan inventory and exhibits a better agreement with the actual statistical data of CO2 emissions at the level of sub-administrative units of the United States. This study demonstrates that the NPP-VIIRS data can be a powerful tool for studying the spatial distributions of CO2 emissions, as well as the socioeconomic indicators at multiple scales.

For this project, the potential of using state-of-the-art aerial digital framing cameras that have time delayed integration (TDI) to acquire useful low light level imagery was enhanced. Computational photography is an emerging field of study pertaining to capturing, processing and manipulating digital imagery with the purpose of enhancing and improving the imagery beyond what is typically accomplished using traditional image processing techniques.

While computational photography techniques have been extensively applied to computer vision and computer graphics problems and are becoming more common in consumer cameras and mobile devices, they have only limitedly been applied within the remote sensing community. With increased computer processing power and awareness of the utility of computational photography, these techniques are now beginning to be applied to the remote sensing image processing chain. This project made use of two computational photography techniques, high dynamic range (HDR) imagery formulation and bilateral filters to enable novel imaging applications. By carefully combining multiple data sets, the effective dynamic range within the image can be increased without over or underexposing portions of the scene. Using this technique, HDR image products were produced from imagery acquired under extreme low light level conditions.

This project made use of two computational photography techniques, high dynamic range (HDR) imagery formulation and bilateral filters to enable novel imaging applications in support of developing a low light level imaging capability to improve imagery. HDR imaging is a technique that generates an image with a greater dynamic range than ordinarily achievable given an imaging system’s hardware architecture. HDR images are generated by acquiring multiple images of the same scene at different exposure settings. Each individual image contains a collection of properly exposed pixels and pixels that are both dark (underexposed) and saturated (overexposed). HDR image products are generated by combining multiple frames of data at different exposure times such that the darkest areas within a frame are imaged with the longest exposure time and the brightest areas within a frame are imaged with the shortest exposure time. This technique can be very powerful when processing imagery acquired under low light level conditions. Standard imagery acquired under low light often contains a significant number of pixels that are extremely dark whereby information content is lost in the shadows. Bilateral filters reduce the noise in relatively uniform areas within an image while minimizing blurring of edges and other spatial features. Edge preserving noise reduction filters are important for improving the imagery of poorly lit scenes. These filters can be used to improve the quality of imagery acquired under low light level conditions. This type of filter preserves edges by only allowing pixels with similar radiometric values to be included in the spatial filter. By looping through each pixel within an image and assigning weights to adjacent pixels, the entire image is processed.

However, implementation of the bilateral filter can be computationally intensive, so alternative algorithms that rely on approximations were developed. The bilateral filter described above was implemented in Matlab® and a simple simulated edge target image was constructed to functionally test the algorithms. For both sets of images noise levels (2% and 4%), and the bilateral filter results were more pronounced as light level and image quality decreased. By using these two computational photography techniques, representative HDR image products with imagery acquired under extreme low light conditions were successfully produced.

According to Cognitive Market Research, the global MEO Satellite market size is USD 47581.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 11.50% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 19032.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.7% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 14274.36 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 10943.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 13.5% from 2024 to 2031.
Latin America's market has more than 5% of the global revenue, with a market size of USD 2379.06 million in 2024, and will grow at a compound annual growth rate (CAGR) of 10.9% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 951.62 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.2% from 2024 to 2031.
The Above 1000kg held the highest MEO Satellite market revenue share in 2024.

Market Dynamics of MEO Satellite Market

Key Drivers for MEO Satellite Market

Rising Demand for Connectivity to Increase the Demand Globally

One key driver in the MEO Satellite market is the continuous advancements in projection technology. As technology evolves, manufacturers are able to develop MEO satellites with higher resolution, brighter displays, and enhanced connectivity options. These advancements make MEO Satellite more versatile and capable of meeting the evolving needs of consumers, whether for business presentations, educational purposes, or entertainment on the go. For example, the integration of LED or laser light sources in MEO Satellite improves image quality, extends lamp life, and reduces power consumption, enhancing the overall user experience. Additionally, features such as wireless connectivity, built-in battery packs, and compact designs contribute to the convenience and portability of MEO Satellite, driving their adoption among professionals, educators, and consumers.

Satellite Miniaturization for Better Fuel and Operational Efficiency to Propel Market Growth

Another key driver in the MEO Satellite market is satellite miniaturization for better fuel and operational efficiency. Miniaturized satellites, like CubeSats and SmallSats, optimize resources, leading to reduced fuel consumption and operational costs. These smaller satellites leverage advanced technology to deliver comparable performance to larger counterparts while being more cost-effective to manufacture and launch. The lower weight and size enable multiple satellites to be deployed in a single launch, increasing deployment frequency and coverage. Additionally, miniaturized satellites facilitate innovative mission designs, such as distributed constellations, offering improved global connectivity, Earth observation, and remote sensing capabilities. As the demand for efficient and affordable satellite solutions grows across various industries, satellite miniaturization emerges as a crucial driver propelling market growth, fostering innovation, and expanding the reach of satellite-based services.

Restraint Factor for the MEO Satellite Market

Competition from Alternative Technologies to Hinder the Growth

One key restraint in the MEO Satellite market is the competition from alternative technologies. Emerging technologies such as high-altitude platforms (HAPs), terrestrial 5G networks, and low Earth orbit (LEO) satellite constellations offer alternative solutions for providing connectivity services. HAPs, like stratospheric balloons and drones, promise lower latency and deployment costs. Terrestrial 5G networks offer high-speed internet access, particularly in urban areas. LEO satellite constellations, such as SpaceX's Starlink, aim to provide global broadband coverage with low latency. As these technologies advance and gain traction, they pose stiff competition to MEO satellites, challenging their market dominance and hindering their growth potential in certain market segments.

Impact of Covid-19 on the MEO Satellite Market

The COVID-19 pandemic has had a mixed impact on the Medium Earth Orbit (MEO) satellite market. While some sectors, such as commercial satellite broadband, experienced increased demand due to remote work and digit...

Annual compositions of stable and cloud-free lights, formed from all the daily images available during 1995 of the Defense Meteorologycal Satellite Program (DMSP) satellites taken by the Operational Linescan System (OLS) sensor and that have no resolution problems. In cases where more than one satellite collects data during this year, there are compounds from each satellite. The composite excludes images that are illuminated by the sun or with glare, those that are illuminated by the moon and those that are illuminated by the aurora. The images have pixels resampled to 100m and are from the Iberian Peninsula and Andalusia.

This data is the surface solar irradiance data obtained for NASA/GISS to allow the production of photosynthetically active solar irradiance fields to allow the calculation of primary production using satellite ocean colour products. The information comes from the International Satellite Cloud Climatology Project (ISCCP) using C1 data from multiple geostationary and polar orbiting meteorological satellites to provide a global view of the occurrence and properties of clouds. Atmospheric, cloud and surface data from ICSSP are used as input along with a scheme for computing clear-sky irradiance from the solar zenith angle, air properties, and surface reflectance. The scheme then uses simple cloud properties (cloud fraction, cloud optical thickness, and diffuse albedo) to produce total and photosynthetically active solar irradiance fields (Bishop and Rossow 1991; ISCCP Documentation of Cloud Data; Frouin et al. 1989). Input and output data fields are given in a 2.5° latitude and longitude grid. Monthly output data averages have been used for this project.