Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System
To effectively manage the terrestrial firefighting fleet in a forest fire scenario, namely, to optimize its displacement in the field, it is crucial to have a well-structured and accurate mapping of rural roads. The landscape’s complexity, mainly due to severe shadows cast by the wild vegetation and...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/1/271 |
| _version_ | 1797439785864790016 |
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| author | Miguel Lourenço Diogo Estima Henrique Oliveira Luís Oliveira André Mora |
| author_facet | Miguel Lourenço Diogo Estima Henrique Oliveira Luís Oliveira André Mora |
| author_sort | Miguel Lourenço |
| collection | DOAJ |
| description | To effectively manage the terrestrial firefighting fleet in a forest fire scenario, namely, to optimize its displacement in the field, it is crucial to have a well-structured and accurate mapping of rural roads. The landscape’s complexity, mainly due to severe shadows cast by the wild vegetation and trees, makes it challenging to extract rural roads based on processing aerial or satellite images, leading to heterogeneous results. This article proposes a method to improve the automatic detection of rural roads and the extraction of their centerlines from aerial images. This method has two main stages: (i) the use of a deep learning model (DeepLabV3+) for predicting rural road segments; (ii) an optimization strategy to improve the connections between predicted rural road segments, followed by a morphological approach to extract the rural road centerlines using thinning algorithms, such as those proposed by Zhang–Suen and Guo–Hall. After completing these two stages, the proposed method automatically detected and extracted rural road centerlines from complex rural environments. This is useful for developing real-time mapping applications. |
| first_indexed | 2024-03-09T11:59:10Z |
| format | Article |
| id | doaj.art-fdd00961f62345dbb05a971710b24716 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:59:10Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-fdd00961f62345dbb05a971710b247162023-11-30T23:06:37ZengMDPI AGRemote Sensing2072-42922023-01-0115127110.3390/rs15010271Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support SystemMiguel Lourenço0Diogo Estima1Henrique Oliveira2Luís Oliveira3André Mora4Department of Electrical and Computer Engineering, NOVA School of Science and Technology (FCT NOVA), NOVA University Lisbon, 2825-149 Caparica, PortugalDepartment of Electrical and Computer Engineering, NOVA School of Science and Technology (FCT NOVA), NOVA University Lisbon, 2825-149 Caparica, PortugalTelecommunications Institute, 1049-001 Lisbon, PortugalDepartment of Electrical and Computer Engineering, NOVA School of Science and Technology (FCT NOVA), NOVA University Lisbon, 2825-149 Caparica, PortugalDepartment of Electrical and Computer Engineering, NOVA School of Science and Technology (FCT NOVA), NOVA University Lisbon, 2825-149 Caparica, PortugalTo effectively manage the terrestrial firefighting fleet in a forest fire scenario, namely, to optimize its displacement in the field, it is crucial to have a well-structured and accurate mapping of rural roads. The landscape’s complexity, mainly due to severe shadows cast by the wild vegetation and trees, makes it challenging to extract rural roads based on processing aerial or satellite images, leading to heterogeneous results. This article proposes a method to improve the automatic detection of rural roads and the extraction of their centerlines from aerial images. This method has two main stages: (i) the use of a deep learning model (DeepLabV3+) for predicting rural road segments; (ii) an optimization strategy to improve the connections between predicted rural road segments, followed by a morphological approach to extract the rural road centerlines using thinning algorithms, such as those proposed by Zhang–Suen and Guo–Hall. After completing these two stages, the proposed method automatically detected and extracted rural road centerlines from complex rural environments. This is useful for developing real-time mapping applications.https://www.mdpi.com/2072-4292/15/1/271rural roadscenterline extractiondeep learninggeographic information system (GIS)wireless sensor networks (WSN)decision support system (DSS) |
| spellingShingle | Miguel Lourenço Diogo Estima Henrique Oliveira Luís Oliveira André Mora Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System Remote Sensing rural roads centerline extraction deep learning geographic information system (GIS) wireless sensor networks (WSN) decision support system (DSS) |
| title | Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System |
| title_full | Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System |
| title_fullStr | Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System |
| title_full_unstemmed | Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System |
| title_short | Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System |
| title_sort | automatic rural road centerline detection and extraction from aerial images for a forest fire decision support system |
| topic | rural roads centerline extraction deep learning geographic information system (GIS) wireless sensor networks (WSN) decision support system (DSS) |
| url | https://www.mdpi.com/2072-4292/15/1/271 |
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