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: | , , , , , |
|---|---|
| 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!
|
| _version_ | 1832591653040291840 |
|---|---|
| author | Jiejie Wu Liang Zhou Deping Li Daoquan Zhang Tingting Jiang Chengzhi Zong |
| author_facet | Jiejie Wu Liang Zhou Deping Li Daoquan Zhang Tingting Jiang Chengzhi Zong |
| author_sort | Jiejie Wu |
| collection | DOAJ |
| description | 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, ALAN information is efficiently extracted and statistically analyzed. Key findings include: (1) With the drone camera set to f2.8, the best correlation between image RGB color channel brightness and measured illuminance occurs at ISO 200 and 1/4s, achieving R2 reaches 0.83 and MAE reaches 7.89 lx. (2) Mask R-CNN and Deeplabv3 achieve over 0.91 in extraction accuracy, with Mask R-CNN excelling in window, street, and neon lights, while Deeplabv3 better handles building sides and road surfaces. (3) Vertical light pollution from typical residential buildings shows a gradual attenuation trend with increasing height. This research presents a novel technical approach for monitoring urban ALAN pollution through UAV-captured tilted images. |
| format | Article |
| id | doaj-art-7c2dd5ac625e484d8e9810faacaa5ef6 |
| institution | Kabale University |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-7c2dd5ac625e484d8e9810faacaa5ef62025-01-22T07:28:33ZengTaylor & Francis GroupGeocarto International1010-60491752-07622025-12-0140110.1080/10106049.2025.2453631Assessing nighttime artificial light pollution from the perspective of an unmanned aerial vehicle tiltJiejie Wu0Liang Zhou1Deping Li2Daoquan Zhang3Tingting Jiang4Chengzhi Zong5Hunan Key Laboratory of Geospatial Big Data Mining and Application Affiliation, Hunan Normal University, Changsha, ChinaHunan Key Laboratory of Geospatial Big Data Mining and Application Affiliation, Hunan Normal University, Changsha, ChinaHunan Key Laboratory of Geospatial Big Data Mining and Application Affiliation, Hunan Normal University, Changsha, ChinaHunan Key Laboratory of Geospatial Big Data Mining and Application Affiliation, Hunan Normal University, Changsha, ChinaHunan Key Laboratory of Geospatial Big Data Mining and Application Affiliation, Hunan Normal University, Changsha, ChinaHunan Key Laboratory of Geospatial Big Data Mining and Application Affiliation, Hunan Normal University, Changsha, ChinaIncreasing 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, ALAN information is efficiently extracted and statistically analyzed. Key findings include: (1) With the drone camera set to f2.8, the best correlation between image RGB color channel brightness and measured illuminance occurs at ISO 200 and 1/4s, achieving R2 reaches 0.83 and MAE reaches 7.89 lx. (2) Mask R-CNN and Deeplabv3 achieve over 0.91 in extraction accuracy, with Mask R-CNN excelling in window, street, and neon lights, while Deeplabv3 better handles building sides and road surfaces. (3) Vertical light pollution from typical residential buildings shows a gradual attenuation trend with increasing height. This research presents a novel technical approach for monitoring urban ALAN pollution through UAV-captured tilted images.https://www.tandfonline.com/doi/10.1080/10106049.2025.2453631UAV tilted imagesartificial light at nightlight pollutionilluminancedeep learning |
| spellingShingle | Jiejie Wu Liang Zhou Deping Li Daoquan Zhang Tingting Jiang Chengzhi Zong Assessing nighttime artificial light pollution from the perspective of an unmanned aerial vehicle tilt Geocarto International UAV tilted images artificial light at night light pollution illuminance deep learning |
| title | Assessing nighttime artificial light pollution from the perspective of an unmanned aerial vehicle tilt |
| title_full | Assessing nighttime artificial light pollution from the perspective of an unmanned aerial vehicle tilt |
| title_fullStr | Assessing nighttime artificial light pollution from the perspective of an unmanned aerial vehicle tilt |
| title_full_unstemmed | Assessing nighttime artificial light pollution from the perspective of an unmanned aerial vehicle tilt |
| title_short | Assessing nighttime artificial light pollution from the perspective of an unmanned aerial vehicle tilt |
| title_sort | assessing nighttime artificial light pollution from the perspective of an unmanned aerial vehicle tilt |
| topic | UAV tilted images artificial light at night light pollution illuminance deep learning |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2025.2453631 |
| work_keys_str_mv | AT jiejiewu assessingnighttimeartificiallightpollutionfromtheperspectiveofanunmannedaerialvehicletilt AT liangzhou assessingnighttimeartificiallightpollutionfromtheperspectiveofanunmannedaerialvehicletilt AT depingli assessingnighttimeartificiallightpollutionfromtheperspectiveofanunmannedaerialvehicletilt AT daoquanzhang assessingnighttimeartificiallightpollutionfromtheperspectiveofanunmannedaerialvehicletilt AT tingtingjiang assessingnighttimeartificiallightpollutionfromtheperspectiveofanunmannedaerialvehicletilt AT chengzhizong assessingnighttimeartificiallightpollutionfromtheperspectiveofanunmannedaerialvehicletilt |