Road connection has high impact on the city development. It helps boost the economic environment along the road. Therefore, it is important to maintain and divide by their traffic flow and road reserve well to determine the privilege of maintenance and budget contribution for every year. Road opens up the relation of intercity and urban as it gives the impact of development along the road. To manage road over the country, geometry data of road is needed for decision making and project well management. The primary data is usually contributed by field technical support persons, such as surveyor, engineer, and others for conventional method of survey, image along the road, computer aid drawing (cad data) as built drawing or topographical plan, and others. This study proposes an urban road mapping with optimal flight parameter and flying low for detail texture acquisition of feature. It ensures the high efficiency, low cost, short cycle, strong maneuverability, convenient operation, and others of a product. The objective of the project is to determine the optimal flight parameter in mapping out a road feature inside the road reserve with detailed digital orthophoto model (DOM) and digital elevation model (DEM). The flight parameters of unmanned aerial vehicle (UAV) and requirements for focal length effectiveness, flight planning preparation, image lap percentage, UAV altitude, and ground control point (GCP) distribution setup were outlined. The study investigated the effect of different focal length effects, GCP shape-based network (pyramid square-, square-, and linear-based networks), UAV altitude (90m, 65m, and 35m), and end lap percentage of image (90%, 80%, and 70%) on the photogrammetry-derived product. The 95m and 65m altitudes gave the lowest root mean square error (RMSE) value (±5cm horizontal and ±8cm vertical). In addition, 80% consistently showed the lowest RMSE for all end lap percentage options. Meanwhile, the pyramid square-based network recovered a total of 40% accuracy higher than square- and linear-based networks. This study could help the local authorities to implement smart road maintenance within their region.
Pothole's defect is major damages indicated the road condition visually, and the structural defects due to some potential causes. Nowadays, new forms of remote sensing technique were widely used, but less studies in the application of low altitude multispectral mapping. The potential of multispectral images is its help better in resolution due to its spectral characteristic. Hence, it helps a lot in feature classification with proper training sample, classifier used, and spectral band composite. Thus, this study aims to extract the defective roads by using the multispectral image of Parrot Sequoia with low flight altitude. This study tries to detect a pothole's existence from band combination and supervised classification other than its common use which ultimately for agriculture purposes. The classifier used in this is Maximum Likelihood, Support vector machine (SVM) and Mahalanobis Distance. 15 different probability of band stacks of green, NIR, red edge, and red band were used as multispectral images. The comparison of the performance between the types of classifier and band combination was modeled and discussed in this study. Classifier algorithm maximum likelihood gives the lowest error of 0.108m² with a combination of NIR + red edge band. SVM gives the lowest error of 0.427m² with a combination of green + NIR + red edge + red band. While Mahalanobis distance gives the lowest error of -0.082m² with a combination of red edge + red band. Averagely, Mahalanobis distance gives the lowest error of 0.299m² of all bands used.
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