Unmanned Aerial Vehicles for Three ‑dimensional Mapping and Change Detection Analysis 6

: Unmanned Aerial Vehicles (UAVs), commonly known as drones are increas‑ ingly being used for three ‑dimensional (3D) mapping of the environment. This study utilised UAV technology to produce a revised 3D map of the University of Lagos as well as land cover change detection analysis. A DJI Phantom 4 UAV was used to collect digital images at a flying height of 90 m, and 75% fore and 65% side overlaps. Ground control points (GCPs) for orthophoto rectifica‑ tion were coordinated with a Trimble R8 Global Navigation Satellite System. Pix4D Mapper was used to produce a digital terrain model and an orthophoto at a ground sampling distance of 4.36 cm. The change detection analysis, using the 2015 base map as reference, revealed a significant change in the land cover such as an increase of 16,306.7 m 2 in buildings between 2015 and 2019. The root mean square error analysis performed using 7 GCPs showed a horizontal and vertical accuracy of 0.183 m and 0.157 m respectively. This suggests a high level of accuracy, which is adequate for 3D mapping and change detection analysis at a sustainable cost.

[1]  C. Okolie,et al.  AN ASSESSMENT OF THE ACCURACY OF STRUCTURE-FROM-MOTION (SFM) PHOTOGRAMMETRY FOR 3D TERRAIN MAPPING , 2020, Geomatics, Landmanagement and Landscape.

[2]  Osamu Saito,et al.  Application of Unmanned Aerial Vehicle (UAV) for Urban Green Space Mapping in Urbanizing Indian Cities , 2019, Unmanned Aerial Vehicle: Applications in Agriculture and Environment.

[3]  N. Koedam,et al.  The advantages of using drones over space-borne imagery in the mapping of mangrove forests , 2018, PloS one.

[4]  Francesco Carlo Nex,et al.  Using UAVs for map creation and updating. A case study in Rwanda , 2018 .

[5]  Takashi Matsubara,et al.  Advantages of unmanned aerial vehicle (UAV) photogrammetry for landscape analysis compared with satellite data: A case study of postmining sites in Indonesia , 2018 .

[6]  Gabriella Caroti,et al.  Structure from motion (SfM) processing of UAV images and combination with terrestrial laser scanning, applied for a 3D-documentation in a hazardous situation , 2017 .

[7]  Özlem Akar,et al.  Mapping land use with using Rotation Forest algorithm from UAV images , 2017 .

[8]  Mark W. Smith,et al.  From experimental plots to experimental landscapes: topography, erosion and deposition in sub‐humid badlands from Structure‐from‐Motion photogrammetry , 2015 .

[9]  Weifeng Li,et al.  Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery , 2014, Remote. Sens..

[10]  Rongjun Qin,et al.  An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images , 2014, Remote. Sens..

[11]  Sérgio Freire,et al.  Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery , 2014 .

[12]  F. Nex,et al.  UAV for 3D mapping applications: a review , 2014 .

[13]  Halim Setan,et al.  High resolution survey for topographic surveying , 2014 .

[14]  Arzu Erener,et al.  An approach for detection of buildings and changes in buildings using orthophotos and point clouds: A case study of Van Erriş earthquake , 2014 .

[15]  Fabio Remondino,et al.  UAV PHOTOGRAMMETRY FOR MAPPING AND 3D MODELING - CURRENT STATUS AND FUTURE PERSPECTIVES - , 2012 .

[16]  Michael A. Wulder,et al.  Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas , 2002 .