Assessing nighttime artificial light pollution from the perspective of an unmanned aerial vehicle tilt
Increasing artificial light at night (ALAN) impacts urban sustainability and contributes to light pollution. Nighttime satellites miss side ALAN, so drone-captured tilted images and measured illuminance are used to assess ALAN pollution within urban streets. By integrating deep learning methods, ALA...
Saved in:
Main Authors: | Jiejie Wu, Liang Zhou, Deping Li, Daoquan Zhang, Tingting Jiang, Chengzhi Zong |
---|---|
Format: | Article |
Language: | English |
Published: |
Taylor & Francis Group
2025-12-01
|
Series: | Geocarto International |
Subjects: | |
Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2453631 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Assessing Electricity Supply Reliability by Detection of Anomalies in Daily Nighttime Light
by: Miaoying Chen, et al.
Published: (2025-01-01) -
Lutter contre la pollution lumineuse
by: Dany Lapostolle, et al.
Published: (2019-10-01) -
Pistes méthodologiques pour prendre en compte la pollution lumineuse dans les réseaux écologiques
by: Romain Sordello
Published: (2017-12-01) -
Lighting Spectrum Optimization With Deep Learning for Moss Species Classification
by: Kenichi Ito, et al.
Published: (2025-01-01) -
An Over-Actuated Hexacopter Tilt-Rotor UAV Prototype for Agriculture of Precision: Modeling and Control
by: Gabriel Oliveira Pimentel, et al.
Published: (2025-01-01)