Results 1 to 10 of about 2,348 (260)

Predicting County-Level Population From VIIRS Nighttime Light Imagery With Deep Learning

IEEE Sensors Journal
Viswadeep Lebakula, Vineela Datla, David W. Wanik, Arthur G. Cosby   +3 more
openaire   +1 more source

Sleep disorders in children/adolescents with neurodevelopmental and neurological disorders: what evidences do we have with the use of non-pharmacological interventions? [PDF]

Front Sleep
Nunes ML, El Halal CDS.
europepmc   +1 more source

Parallel joint encoding for drone-view object detection under low-light conditions. [PDF]

Front Artif Intell
Liu L, Zhou B, Li Q, Fu G, Wang Y, Chu H.   +5 more
europepmc   +1 more source

Remote sensing intelligent interpretation brain: Real-time intelligent understanding of the Earth. [PDF]

PNAS Nexus
Wan Y, Zhang C, Ma A, Chen Z, Sun C, Wang J, Zheng Z, Bao F, Zhang L, Zhong Y.   +9 more
europepmc   +1 more source

Evaluating the Feasibility of Using Nighttime Light Imagery to Detect Tornado-Induced Power Outages

Martin Appiah, Shenyue Jia, Myles Fankhauser   +2 more
openaire   +1 more source

Correcting the Pixel Blooming Effect (PiBE) of DMSP-OLS nighttime light imagery

Remote Sensing of Environment, 2020
Abstract In the last two decades, the advance in nighttime light (NTL) remote sensing has fueled a surge in extensive research towards mapping human footprints. Nevertheless, the full potential of NTL data is largely constrained by the blooming effect.
Qiming Zheng, Qihao Weng, Ke Wang
exaly   +2 more sources

Population bias in nighttime lights imagery

Remote Sensing Letters, 2019
ABSTRACTBrightness of nighttime lights (NTL) derived from remote sensing imagery has been extensively used to estimate population. However, population bias in NTL images has yet to be investigated. In this study, we explore the associations of race and age demographics with brightness of NTL in the United States (US) for 2015 and 2016.
Naizhuo Zhao, Eric L. Samson, Ying Liu
openaire   +1 more source

Summer suburbanization in Moscow Region: Investigation with nighttime lights satellite imagery

Environment and Planning A: Economy and Space, 2022
In the summertime, many Moscow residents move to second homes in the countryside, which causes strong seasonal fluxes in the population size of the Russian capital, its surroundings, and more remote rural areas. To better understand the spatial scale and patterns of seasonal suburbanization, we tracked the changes in average monthly nighttime light ...
Alexander Sheludkov, Alexandra Starikova
openaire   +1 more source

Improving Nighttime Light Imagery With Location-Based Social Media Data

IEEE Transactions on Geoscience and Remote Sensing, 2019
Location-based social media have been extensively utilized in the concept of “social sensing” to exploit dynamic information about human activities, yet joint uses of social sensing and remote sensing images are underdeveloped at present. In this paper, the close relationship between the number of Twitter users and brightness of nighttime lights (NTL ...
Naizhuo Zhao, Wei Zhang 0097, Ying Liu, Eric L. Samson, Yong Chen 0001, Guofeng Cao   +5 more
openaire   +1 more source

Modeling and Prediction of NPP-VIIRS Nighttime Light Imagery Based on Spatiotemporal Statistical Method

IEEE Transactions on Geoscience and Remote Sensing, 2021
The cloud-free monthly composite of the global nighttime light (NTL) data derived from the Suomi National Polar orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) has gained popularity for detecting anthropogenic and socioeconomic activities.
Yang Ye, Jinsong Deng, Lingyan Huang, Qiming Zheng, Ke Wang 0016, Cheng Tong, Yang Hong 0001   +6 more
openaire   +1 more source