Remote Sensing Applied to the Extraction of Road Geometric Features Based on Optimum Path Forest Classifiers, Northeastern Brazil

Macedo, M.; Maia, Maria; Rabbani, Emilia Kohlman; Neto, Oswaldo Lima

doi:10.4236/jgis.2020.121002

Cited by 9 publications

(5 citation statements)

References 46 publications

(51 reference statements)

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“…The Br 232 highway dataset between km 141 and 356 became used for the cause of some distance flung sensing done to the extraction of avenue geometric abilities based totally on the most effective direction wooded region classifiers [34]. They used SVM, and ANN is the class process for pre-processing to enhance the effects.…”

Section: Comparison Of Different Methods and Approaches For Lane Dete...mentioning

confidence: 99%

Artificial Intelligence based Lane Detection using Satellite Images

Rehman¹,

Ghous²,

Malik

et al. 2023

ijisct

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Lane detection is one of the most important task for tragedy or emergency response using satellite imagery structures. Most of the researchers adopted Machine Learning (ML) approaches to detect white lines on the roads namely lanes. These approaches are more complex and computational. In order to identify knowledge gaps, this study compares and analyses the various lane detection and tracking algorithms that have been used in research papers in the past. Additionally, it provides an overview of some of the datasets used in various research papers. The main challenge in detecting the lane using satellite images is the weather, which makes it difficult to achieve the expected objectives.

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Section: Comparison Of Different Methods and Approaches For Lane Dete...mentioning

confidence: 99%

Artificial Intelligence based Lane Detection using Satellite Images

Rehman¹,

Ghous²,

Malik

et al. 2023

ijisct

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show abstract

“…In order to analyze the data obtained in photogrammetry, it is necessary to know the phototriangulation technique, which is presented as a technique that helps in the mathematical interpretation of photographs. The aerial survey, on the other hand, can be described as a set of air or space operations for measuring, computing and recording terrain data using specific sensors, consisting of an aerospace phase for capturing and recording data and a phase that refers to the data processing (Macedo et al, 2020;Sobrinho, 2021).…”

Section: Perspectives Of Imaging Technologies In Brazilmentioning

confidence: 99%

Use of Advanced Technologies for Topographic Surveys in Civil Construction

Macedo¹,

Ferreira²

2023

JMS

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The complexity that has characterized market relations in recent years, with demands for product and process innovation, has even had repercussions on more traditional activities, such as the civil construction segment. Behind only agriculture, the construction industry represents 13% of the global GDP and its volume is US$ 8 trillion per year. However, construction projects often exceed budget by 80%, and deadlines by 20 months. Between 8% and 10% of productivity gains are related to the insertion of technologies (IPEA, 2022). In the field of civil construction, these digital technologies, such as cloud computing, automation, virtual reality and augmented reality, 3D modeling, communication applications and BIM—Building Information Modeling and even machine learning, have been called Construction 4.0. This article evaluates the feasibility of replacing the use of Terrestrial Laser Scanner (TLS) by the use of Unmanned Aerial Vehicles (UAV). For this, it presents a comparison in the use of equipment for carrying out planimetric surveys in civil construction, using as an example the UAV and the TLS—more modern equipment, in addition to the total station—more conventional equipment—for surveying control points. The results show that in the UAV image processing, the RMSE presented a centimeter accuracy (1.93044 cm) for the model. Even if the accuracy of the models generated by TLS is millimetric, it can be considered that the results obtained here were satisfactory, however it is necessary to apply imaging techniques more efficiently to obtain a more accurate product, in order to arrive at millimeter accuracy. Studies on better positioning of targets and georeferencing of models would also be of great contribution to applications in civil construction.

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“…Moreover, Macedo et al [14] introduced Hyperion hyperspectral remote sensing images to the context of supervised OPF classification, outperforming traditional Support Vector Machines (SVM) and better distinguishing between unpaved roads and exposed soil. Recently, Passos et al [15] were the first to employ the OPFython package academically.…”

Section: Impact On Academic Researchmentioning

confidence: 99%

OPFython: A Python implementation for Optimum-Path Forest

Rosa

Papa

2021

Software Impacts

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OPFython is an open-sourced Python package that implements Optimum-Path Forest algorithms using objectoriented programming and a straightforward structure. It provides an alternative implementation to the standard LibOPF package, which heavily depends on the C language and occasionally hinders fast prototyping. Additionally, OPFython provides documented code, unitary tests, and examples that assist users in learning how to work with the package. Such features are well-suited for researchers and developers interested in exploring alternative state-of-the-art machine learning algorithms.

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Remote Sensing Applied to the Extraction of Road Geometric Features Based on Optimum Path Forest Classifiers, Northeastern Brazil

Cited by 9 publications

References 46 publications

Artificial Intelligence based Lane Detection using Satellite Images

Artificial Intelligence based Lane Detection using Satellite Images

Use of Advanced Technologies for Topographic Surveys in Civil Construction

OPFython: A Python implementation for Optimum-Path Forest

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