International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-4/W19
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 39–46, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-39-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W19, 39–46, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W19-39-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Dec 2019

23 Dec 2019

ILLUMINANCE MAPPING OF NIGHTTIME ROAD ENVIRONMENT USING UNMANNED AERIAL SYSTEM

R. T. Bahia1, M. C. Estur1, A. C. Blanco1,2, and M. Soriano3 R. T. Bahia et al.
  • 1Department of Geodetic Engineering, University of the Philippines-Diliman, Philippines
  • 2Training Center for Applied Geodesy and Photogrammetry, University of the Philippines-Diliman, Philippines
  • 3National Institute of Physics, University of the Philippines-Diliman, Philippines

Keywords: 3D Model, Calibration, Linear Regression, Photogrammetry, Road Lighting

Abstract. One purpose of road lighting in night-time environment is to allow pedestrians safe passage and provide sense of security. To attain this purpose, guidelines are set to make sure that proper lighting are present in roads. Current mapping of road lighting in the Philippines is a tedious process. To address this issue, an alternative method of mapping illuminance using Unmanned Aerial System is developed. A 3D model of the road was created using overlapping images and is the basis of the data points. The results of the calibration show that each of the camera channels, as well the photopic luminance value denoted as ND, shows a linear trend with the illuminance value recorded within the subject area having a range of 0.17 to 15 lux. Linear regression models using each of the channels of the camera and ND can be used to calculate illuminance with RMSE of less than 0.6 lux. It also shows that all values calculated from all regression models exhibits similar trend with blue differing by being generally lower in value. With blue as the best linear regression model, 3D and 2D models of illuminance were created. With these results, it can be concluded that application of photogrammetry through UAV-mounted camera can be used to map illuminance at low level lighting.