Analysis of Satellite Images Using Deep Learning Techniques and Remotely Piloted Aircraft for a Detailed Description of Tertiary Roads
This document presents the results of a proof of concept for describing with more detail the social and complementary infrastructure around the tertiary roads of the Taminango region in the department of Nariño, Colombia. A dataset with samples of free satellite images from Google Maps and OpenStree...
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| Format: | Article |
| Language: | English |
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Universidad Pedagógica y Tecnológica de Colombia
2021-12-01
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| Series: | Revista Facultad de Ingeniería |
| Subjects: | |
| Online Access: | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13816 |
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| author | Maria-Camila Moreno-Vergara Brayan-Daniel Sarmiento-Iscala Fabián-Enrique Casares-Pavia Yerson-Duvan Angulo-Rodríguez Danilo-José Morales-Arenales |
| author_facet | Maria-Camila Moreno-Vergara Brayan-Daniel Sarmiento-Iscala Fabián-Enrique Casares-Pavia Yerson-Duvan Angulo-Rodríguez Danilo-José Morales-Arenales |
| author_sort | Maria-Camila Moreno-Vergara |
| collection | DOAJ |
| description | This document presents the results of a proof of concept for describing with more detail the social and complementary infrastructure around the tertiary roads of the Taminango region in the department of Nariño, Colombia. A dataset with samples of free satellite images from Google Maps and OpenStreetMaps was obtained. Then, a supervised deep learning algorithm with FCN (Fully Convolutional Network) topology is applied for the points of interest labeling process and the identification of the state of the roads using Keras and TensorFlow. Subsequently, a system consisting of a desktop application and a mobile application that integrates the functionalities of the trained algorithm through an intuitive interface and simple logic that stimulates interaction with the consultant is proposed. The desktop application includes a GUI designed in Python for tagging points of interest. The mobile application was developed with Flutter and comprises a database with documentation of the routes and road network in the region. It includes an augmented reality system in Vuforia Engine and Unity with virtual content developed in Blender and SolidWorks; A 3D model of the map of the region has been recreated for easier interaction and visualization of the points of interest and the status of the studied roads. In addition, complementary information was collected through remotely piloted aircraft for data acquisition in environments difficult to access, and through the community participation for the description and identification of areas not visible on official maps or statistics. This study addresses a method for the classification and identification of state of tertiary road network of the studied region, as well as labeling points of interest for the efficient management of resources for the development of new infrastructure there. |
| first_indexed | 2024-12-22T16:00:07Z |
| format | Article |
| id | doaj.art-e3408c5a2ea04885b90f034370a7b18e |
| institution | Directory Open Access Journal |
| issn | 0121-1129 2357-5328 |
| language | English |
| last_indexed | 2024-12-22T16:00:07Z |
| publishDate | 2021-12-01 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | Article |
| series | Revista Facultad de Ingeniería |
| spelling | doaj.art-e3408c5a2ea04885b90f034370a7b18e2022-12-21T18:20:42ZengUniversidad Pedagógica y Tecnológica de ColombiaRevista Facultad de Ingeniería0121-11292357-53282021-12-013058e13816e1381610.19053/01211129.v30.n58.2021.1381611261Analysis of Satellite Images Using Deep Learning Techniques and Remotely Piloted Aircraft for a Detailed Description of Tertiary RoadsMaria-Camila Moreno-Vergara0https://orcid.org/0000-0002-9732-1622Brayan-Daniel Sarmiento-Iscala1https://orcid.org/0000-0002-0447-0902Fabián-Enrique Casares-Pavia2https://orcid.org/0000-0001-6593-8807Yerson-Duvan Angulo-Rodríguez3https://orcid.org/0000-0002-9037-2283Danilo-José Morales-Arenales4https://orcid.org/0000-0001-8650-7889Universidad de PamplonaUniversidad de PamplonaUniversidad de PamplonaUniversidad de PamplonaUniversidad de PamplonaThis document presents the results of a proof of concept for describing with more detail the social and complementary infrastructure around the tertiary roads of the Taminango region in the department of Nariño, Colombia. A dataset with samples of free satellite images from Google Maps and OpenStreetMaps was obtained. Then, a supervised deep learning algorithm with FCN (Fully Convolutional Network) topology is applied for the points of interest labeling process and the identification of the state of the roads using Keras and TensorFlow. Subsequently, a system consisting of a desktop application and a mobile application that integrates the functionalities of the trained algorithm through an intuitive interface and simple logic that stimulates interaction with the consultant is proposed. The desktop application includes a GUI designed in Python for tagging points of interest. The mobile application was developed with Flutter and comprises a database with documentation of the routes and road network in the region. It includes an augmented reality system in Vuforia Engine and Unity with virtual content developed in Blender and SolidWorks; A 3D model of the map of the region has been recreated for easier interaction and visualization of the points of interest and the status of the studied roads. In addition, complementary information was collected through remotely piloted aircraft for data acquisition in environments difficult to access, and through the community participation for the description and identification of areas not visible on official maps or statistics. This study addresses a method for the classification and identification of state of tertiary road network of the studied region, as well as labeling points of interest for the efficient management of resources for the development of new infrastructure there.https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13816tertiary roadssatellite imagesdeep learningremotely piloted aircraftcommunity participationaugmented reality |
| spellingShingle | Maria-Camila Moreno-Vergara Brayan-Daniel Sarmiento-Iscala Fabián-Enrique Casares-Pavia Yerson-Duvan Angulo-Rodríguez Danilo-José Morales-Arenales Analysis of Satellite Images Using Deep Learning Techniques and Remotely Piloted Aircraft for a Detailed Description of Tertiary Roads Revista Facultad de Ingeniería tertiary roads satellite images deep learning remotely piloted aircraft community participation augmented reality |
| title | Analysis of Satellite Images Using Deep Learning Techniques and Remotely Piloted Aircraft for a Detailed Description of Tertiary Roads |
| title_full | Analysis of Satellite Images Using Deep Learning Techniques and Remotely Piloted Aircraft for a Detailed Description of Tertiary Roads |
| title_fullStr | Analysis of Satellite Images Using Deep Learning Techniques and Remotely Piloted Aircraft for a Detailed Description of Tertiary Roads |
| title_full_unstemmed | Analysis of Satellite Images Using Deep Learning Techniques and Remotely Piloted Aircraft for a Detailed Description of Tertiary Roads |
| title_short | Analysis of Satellite Images Using Deep Learning Techniques and Remotely Piloted Aircraft for a Detailed Description of Tertiary Roads |
| title_sort | analysis of satellite images using deep learning techniques and remotely piloted aircraft for a detailed description of tertiary roads |
| topic | tertiary roads satellite images deep learning remotely piloted aircraft community participation augmented reality |
| url | https://revistas.uptc.edu.co/index.php/ingenieria/article/view/13816 |
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