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.

Geospatial data have become a valuable asset in the 21st century with its applications in almost everyday life and an overriding scope in the field of research. One such growing spatial data is the remotely sensed nighttime lights (NTL) imagery, which simply is a depiction of human activities around the globe at night. It may be a stunning visual to many yet the valuable insights it provides in measuring a number of parameters like population, poverty, electrification, migration, disaster, health, fishing, fires, GDP, pollution, urbanization, settlement, etc. have made researchers and scientists look up to this data to validate and evaluate socio-economic and other indicators independently and concurrently. Apart from using as a proxy in many researches, NTL allows to track statistics of region where data is often not collected or is not reliable. It has potential applications for policy makers and government in the decision making processes. Nighttime lights were in used since the mid 1990's and are publicly made available from 1992 onwards through the Defense Meteorological Satellite Program (DMSP) provided by National Ocean and Atmospheric Administration (NOAA). A more advance system called Visible Infrared Imaging Radiometer Suite (VIIRS) Day Night band (DNB) replaces DMSP system. The extraction provided uses VIIRS monthly aggregates with spatial polygon units of India at sub-districts level. The monthly raw dataset is available from April 2012 onwards. This extraction cover 141 months till December 2023. The primary intent is to disseminate the dataset to a larger audience, be it researcher or policy analyst and planners. The broader objective is to keep on updating the data continuously.

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.

The Annual Visible Night Light (VNL) V2 (VIIRS) images at 500-m spatial resolution for the period 2012 to 2021 (Elvidge et al., 2021) have been used to extrapolate the values backwards for years 2000–2011. This was done by fitting a logistic regression (per pixel) and then predicting the values for the previous years (see nightlights_stack_500m.R). After consistent time-series have been produced, I also derived the difference between average of the years 2020/2021 and years 2000/2021 (nightlights.difference_viirs.v21_m_500m_s_2000_2021_go_epsg4326_v20230318.tif): this shows average rate of change for the 22 years period. Use with caution: extrapolation of values can lead to artifacts. For most of the land surface, however, it appears that the growth of night lights follows exponential growth function and hence nights in the past can be represented accurately by fitting decay / logistic regression function.

Original values from the Annual VNL V2 product have been converted from 0–200 to 0–2000 scale and are available as Cloud-Optimized GeoTIFFs.

To cite the Annual VNL V2, please use:

• Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C., & Taneja, J. (2021). Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922

Historic night light images are also available (but at a much coarser spatial resolution) from:

• Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). A harmonized global nighttime light dataset 1992–2018. Scientific data, 7(1), 168. https://doi.org/10.1038/s41597-020-0510-y

Remote sensing data has the potential to revolutionize social science. One of the most prominent examples of this is the Nighttime Lights dataset, which provides digital measures of nighttime luminosity from 1992 to 2013. This study evaluates the Nighttime Lights data against detailed rural electrification data from the 2011 Census of India. The results suggest that many nighttime luminosity measures derived from satellite data are surprisingly accurate for measuring rural electrification, even at the village level and using simple statistical tools. We also demonstrate that this accuracy can be substantially improved by using of better GIS maps, basic geoprocessing tools, and particular aggregations of nighttime luminosity. Nighttime luminosity performs worse in measuring financial inclusion or proxies of poverty, however, and detects rural electrification less accurately when the supply of power is intermittent. These results offer guidelines for when and how remote sensing data can be used when administrative data is absent or unreliable.

The datasets and software code (in the form of STATA dofiles) relate to the publication in Applied Economics entitled: "Lights along the frontier: convergence of economic activity in the proximity of the Polish-German border, 1992-2012".

The analysis dataset in STATA format is created by combining data coming from:

1) NOAA Version 4 DMSP-OLS Nighttime Lights Time Series (https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html);

2) Map data copyrighted OpenStreetMap (OSM) contributors and available from https://www.openstreetmap.org;

3) Administrative division of Poland, municipality level Shapefiles for 2018, PRG (http://www.gugik.gov.pl/pzgik/dane-bez-oplat/dane-z-panstwowego-rejestru-granic-i-powierzchni-jednostek-podzialow-terytorialnych-kraju-prg);

4) Map of the municipalities and districts of Germany as of 31.12.2013, VG250 and VG250-EW, © GeoBasis-DE / BKG 2013 (https://gdz.bkg.bund.de/);

Geographical data (nighttime lights, municipality borders for Poland and Germany and OpenStreetMap data) have been imported into PostgreSQL database using PostGIS plugin using batch processing in Python. Nighttime intensities for municipalities were created by intersecting vector municipality borders and raster lights data for each avaliable year and satelite. Light totals and averages were calculated using calibrated pixel values using 2nd deg. polynominal intercalibration parameters from Elvidge et al., National Trends in Satellite Observed Lighting: 1992-2009. Bridge crossings were identified using contemporary map data and OSM. OSM data were used to calculate road travel times and distances using pgRouting in PostgreSQL. Data were exported into CSV using Python and imported and merged in Stata, creating the initial dataset.

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 …

The Earth Observations Group (EOG) at National Oceanic and Atmospheric Administration (NOAA)/National Geophysical Data Center (NGDC) 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.

The dataset is the night light annual composite in year of 2015. The dataset is a KML file which requires the Google earth to visualize. For other monthly and annual basis night light geotiff datasets (up to Sep 2017), please download at https://www.ngdc.noaa.gov/eog/viirs/download_dnb_composites.html#NTL_2015

Citation: the Earth Observation Group, NOAA National Geophysical Data Center

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 global LED night light for kids market is experiencing robust growth, driven by increasing parental awareness of children's sleep hygiene and safety concerns. The market, estimated at $500 million in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $950 million by 2033. This expansion is fueled by several key factors. The rising adoption of smart home technology and the increasing availability of energy-efficient, aesthetically pleasing LED night lights specifically designed for children are significantly impacting the market. Parents are increasingly seeking products that offer features such as adjustable brightness, timers, and soothing sounds, contributing to the growth of the plug-in and battery-operated segments. The residential sector dominates the market, owing to high demand for creating a calming and safe sleep environment for children in bedrooms and hallways. However, the commercial sector, including nurseries, daycare centers, and hotels, is also showing promising growth potential. Geographic distribution reveals strong demand in North America and Europe, driven by high disposable incomes and awareness of child safety. Asia Pacific, particularly China and India, is expected to witness substantial growth due to rising middle-class populations and increased awareness regarding child-centric products. Despite the positive outlook, certain restraints exist. Price sensitivity in developing economies and the potential for counterfeit products pose challenges. Competition among established players like Signify, Eaton, Osram, and emerging brands adds complexity. To overcome these hurdles, manufacturers are focusing on product differentiation through innovative features, improved safety standards, and strategic partnerships with retailers catering to families with young children. The increasing availability of personalized and customizable night lights, incorporating aspects such as favorite characters or themes, is another crucial factor driving the market's expansion. The transition towards more sustainable and eco-friendly products will likely further shape the landscape of this growing market segment.

The dataset contains (1) temporally calibrated DMSP-OLS NTL time series data from 1992-2013; and (2) converted NTL time series from the VIIRS data (2014-2024)Spatial resolution: 30 arc-seconds (~1km)Information about the composited images from the calibrated DMSP dataset:F10(1992-1994); F12(1995-1996); F14(1997-2003); F16(2004-2009); F18(2010-2013)We suggested using pixels with DN values greater than 7.

The data include the night light data of Tibetan Plateau with a spatial resolution of 1km*1km, a temporal resolution of 5 years and a time coverage of 2000, 2005 and 2010.The data came from Version 4 dmsp-ols products. DMSP/OLS sensors took a unique approach to collect radiation signals generated by night lights and firelight.DMSP/OLS sensors, working at night, can detect low-intensity lights emitted by urban lights, even small-scale residential areas and traffic flows, and distinguish them from dark rural backgrounds.Therefore, DMSP/OLS nighttime light images can be used as a representation of human activities and become a good data source for human activity monitoring and research.

Nightlights (NTL) have been widely used as a proxy for economic activity, despite known limitations in accuracy and comparability, particularly with outdated Defense Meteorological Satellite Program (DMSP) data. The emergence of newer and more precise Visible Infrared Imaging Radiometer Suite (VIIRS) data offers potential, yet challenges persist due to temporal and spatial disparities between the two datasets. Addressing this, we employ a novel harmonized NTL dataset (VIIRS + DMSP), which provides the longest and most consistent database available to date. We evaluate the association between newly available harmonized NTL data and various indicators of economic activity at the subnational level across 34 countries in sub-Saharan Africa from 2004 to 2019. Specifically, we analyze the accuracy of the new NTL data in predicting socio-economic outcomes obtained from two sources: 1) nationally representative surveys, i.e., the household Wealth Index published by Demographic and Health Surveys, and 2) indicators derived from administrative records such as the gridded Human Development Index and Gross Domestic Product per capita. Our findings suggest that even after controlling for population density, the harmonized NTL remain a strong predictor of the wealth index. However, while urban areas show a notable association between harmonized NTL and the wealth index, this relationship is less pronounced in rural areas. Furthermore, we observe that NTL can also significantly explain variations in both GDP per capita and HDI at subnational levels.

Data collected as input to the geospatial least-cost plan for universal electricity access by 2030 developed as part of the ESMAP funded World Bank Nigeria Electricity Access Project (NEAP). The dataset covers the service area for the Kaduna Electricity Distribution Company (KEDCO) Nigeria. The data was downloaded on April 7th, 2016 for the four states of the Kaduna Electric utility coverage area: Kaduna, Kebbi, Sokoto and Zamfara. The data source for the nightlights data is from : Earth Observations Group (EOG) at NOAA/NGDC - https://ngdc.noaa.gov/eog/viirs/download_dnb_composites.html The data was downloaded for the range of 20151101 to 20151130 2015. The data represent the light outputs from cities, towns, and other sites with persistent lighting, including gas flares. Ephemeral events, such as fires have been discarded. Then the background noise was identified and replaced with values of zero. The data was downloaded on 1/28/2016 for the West and North Africa region and then further clipped to only include the four states of the Kaduna Electric utility coverage area: Kaduna, Kebbi, Zamfara and Sokoto states.

‡ Robust standard errors clustered at the municipality level are presented in parentheses.Significance level: *** p < 0.01, ** p < 0.05. All dependent variables have been normalized between 0 and 1 to facilitate interpretation of coefficients. Night lights data takes a value between 0 and 63 for each approximately (1-km) pixel. Pixel data was aggregated at the municipality level using the zonal statistics package in QGIS in each year.Heterogeneity by type of DTO.‡

Artificial nightlight is increasingly recognized as an important environmental disturbance that influences the habitats and fitness of numerous species. However, its effects on wide-ranging vertebrates and their interactions remain unclear. Light pollution has the potential to amplify land-use change, and as such, answering the question of how this sensory stimulant affects behavior and habitat use of species valued for their ecological roles and economic impacts is critical for conservation and land-use planning. Here, we combined satellite-derived estimates of light pollution, with GPS-data from cougars (Puma concolor; n = 56), mule deer (Odocoileus hemionus; n = 263), and locations of cougar-killed deer (n = 1,562 carcasses), to assess the effects of light exposure on mammal behavior and predator-prey relationships across wildland-urban gradients in the southwestern United States. Our results indicate that deer used the anthropogenic environments to access forage and were more active...

The world in which birds evolved to migrate has been drastically altered in the Anthropocene by artificial light. Sources of light such as urban centers or bright upward-facing lights attract migrants, altering their behavior, especially during inclement weather, often leading to mortality. Seemingly less extreme sources, such as pole-mounted floodlighting ubiquitous throughout much of the world, have received comparatively less study, and migrant responses to such sources are poorly understood. We studied migrant behavior in relation to light at White Sands Missile Range (New Mexico, USA) by recording nocturnal flight calls at sites with and without lights during non-inclement weather. We collected 103,424 h of recordings and detected 2,851,863 calls over three fall migration seasons. We assessed how temporal, weather, and lighting variables explain variability in call rates between light and dark sites, and examined how different taxonomic groups behave in relation to light. Contrary ..., , Dryad dataset

Dataset DOI: 10.5061/dryad.w6m905r26

Description of the data and file structure

The data and code included herein was used to assess in-flight avian behavioral responses to artificial light at night and included analyses of behavioral responses under a variety of weather, temporal, and lighting based variables, and taxonomic specific analyses of responses to light.

Files and variables

File: Code_for_Osterhaus_et_al._(2025)_Biological_Conservation.Rmd

Description:Â Code written in R Markdown that is used to access data included in this dataset and recreate the analyses associated with the related manuscript

File: AllDurations2021.csv / AllDurations2022.csv / File: AllDurations2023.csv

Description:Â Duration information for each of the acoustic recordings for 2021, 2022, 2023.

Variables

• filename: name associated with the specific recording file
• Duration: duration of the specific recording (in seconds)

**...,

1) based on 1 km resolution data lights in 2019, covers all the way "area" 64 1 countries along the (Chinese), for analyzing "One Belt And One Road" along the national and social development relationship between population and resources environment is very important for the regional heterogeneity;2) the data from earth observation satellite Suomi NPP, the satellite visible light/infrared radiation imaging (VIIRS) to obtain a new night lighting remote sensing image, developed jointly by NASA, NOAA and the U.S. air force, Raytheon company offers;3) earth observation satellite Suomi NPP using GIS and remote sensing methods such as innovation, provide scientists with higher precision and improve weather prediction and climate of the earth's atmosphere information cognitive ability;4) data from 2019 in the night light data resulting from the average, and through the add blank value, eliminate outliers, space transverse longitudinal comparison and processing, to meet the large scale geographical research, in the heart of the "One Belt And One Road" related research application is very extensive, follow-up will be subject to an increase in the number of all the countries, constantly update perfect this set of data.

Amidst a growing need for effective energy management, government policies increasingly rely on accurate electricity consumption forecasts to make informed decisions on renewable energy adoption. This study investigates the predictive capabilities of night light satellite imagery in forecasting electricity usage in India, aligning with Sustainable Development Goals 7 and 10. Utilizing data from the VIIRS satellite and NASA’s Black Marble product, the research employs various LSTM models to analyse electricity consumption trends. Additionally, state-wise analyses have been conducted by applying k-means clustering to capture spatial consumption variations. By demonstrating the strong correlation between night lights and electricity consumption, the study emphasizes the utility of satellite imagery for actionable insights into energy dynamics. The results emphasize the viability of night light data as a dependable indicator of electricity demand, with MAPE values below 10% and RMSE values below 20 MU. It also highlights the transformative impact of remote sensing technologies in advancing sustainable development agendas and highlights the pivotal role of night light imagery in energy forecasting initiatives.

Light pollution is rapidly increasing and can have deleterious effects on biodiversity, yet light types differ in their effect on wildlife. Among the light types used for street lamps, light-emitting diodes (LEDs) are expected to become globally predominant within the next few years. In a large-scale field experiment, we recorded bat activity at 46 street lights for 12 nights each and investigated how the widespread replacement of conventional illuminants by LEDs affects urban bats: we compared bat activity at municipal mercury vapour (MV) street lamps that were replaced by LEDs with control sites that were not changed. Pipistrellus pipistrellus was the most frequently recorded species; it was 45% less active at LEDs than at MV street lamps, but the activity did not depend on illuminance level. Light type did not affect the activity of Pipistrellus nathusii, Pipistrellus pygmaeus or bats in the Nyctalus/Eptesicus/Vespertilio (NEV) group, yet the activity of P. nathusii increased with illuminance level. Bats of the genus Myotis increased activity 4·5-fold at LEDs compared with MV lights, but illuminance level had no effect. Decreased activity of P. pipistrellus, which are considered light tolerant, probably paralleled insect densities around lights. Further, our results suggest that LEDs may be less repelling for light-averse Myotis spp. than MV lights. Accordingly, the transition from conventional lighting techniques to LEDs may greatly alter the anthropogenic impact of artificial light on urban bats and might eventually affect the resilience of urban bat populations. Synthesis and applications. At light-emitting diodes (LEDs), the competitive advantage – the exclusive ability to forage on insect aggregations at lights – is reduced for light-tolerant bats. Thus, the global spread of LED street lamps might lead to a more natural level of competition between light-tolerant and light-averse bats. This effect could be reinforced if the potential advantages of LEDs over conventional illuminants are applied in practice: choice of spectra with relatively little energy in the short wavelength range; reduced spillover by precisely directing light; dimming during low human activity times; and control by motion sensors. Yet, the potential benefits of LEDs could be negated if low costs foster an overall increase in artificial lighting.