REMOTE SENSING DATA FROM SPACE FOR ROAD IMAGE RECOGNITION IN THE FORESTRY
Paper presents an overview of history and current research state on the use of remote sensing data from space to recognize roads for the regional projects in the forestry. We reviewed the principles of road detection on the optical satellite imagery. Group of direct recognition features used in comb...
| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Russian Academy of Sciences, Center for Forest Ecology and Productivity
2023-03-01
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| Series: | Вопросы лесной науки |
| Subjects: | |
| Online Access: | https://jfsi.ru/6-1-2023-podolskaya/ |
| _version_ | 1797693032455208960 |
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| author | E. S. Podolskaia |
| author_facet | E. S. Podolskaia |
| author_sort | E. S. Podolskaia |
| collection | DOAJ |
| description | Paper presents an overview of history and current research state on the use of remote sensing data from space to recognize roads for the regional projects in the forestry. We reviewed the principles of road detection on the optical satellite imagery. Group of direct recognition features used in combinations such as brightness and texture, geometry and brightness. Three research directions with examples were identified: visual roads recognition, use of special software and libraries for developers, and neural networks. For the road network detection we have described methods and software, type and spatial resolution of imagery. Road image recognition based on the optical survey from the open and commercial sources, machine learning methods and neural networks. Up-to-date tasks of road recognition are the following: evaluation of road surface condition, modeling of existing roads location, designing and building new roads, roads seasonality. A functional summary of MapFlow plugin for road recognition in Open Source QGIS is given. Paper is a part of regional forestry transport modeling project to access the forest fires and forest resources by ground means. |
| first_indexed | 2024-03-12T02:37:41Z |
| format | Article |
| id | doaj.art-c6a9e604206a4792a30460f14476c2ee |
| institution | Directory Open Access Journal |
| issn | 2658-607X |
| language | English |
| last_indexed | 2024-03-12T02:37:41Z |
| publishDate | 2023-03-01 |
| publisher | Russian Academy of Sciences, Center for Forest Ecology and Productivity |
| record_format | Article |
| series | Вопросы лесной науки |
| spelling | doaj.art-c6a9e604206a4792a30460f14476c2ee2023-09-04T14:10:08ZengRussian Academy of Sciences, Center for Forest Ecology and ProductivityВопросы лесной науки2658-607X2023-03-016111410.31509/2658-607x-202361-121REMOTE SENSING DATA FROM SPACE FOR ROAD IMAGE RECOGNITION IN THE FORESTRY E. S. Podolskaia0Center for Forest Ecology and Productivity of the RASPaper presents an overview of history and current research state on the use of remote sensing data from space to recognize roads for the regional projects in the forestry. We reviewed the principles of road detection on the optical satellite imagery. Group of direct recognition features used in combinations such as brightness and texture, geometry and brightness. Three research directions with examples were identified: visual roads recognition, use of special software and libraries for developers, and neural networks. For the road network detection we have described methods and software, type and spatial resolution of imagery. Road image recognition based on the optical survey from the open and commercial sources, machine learning methods and neural networks. Up-to-date tasks of road recognition are the following: evaluation of road surface condition, modeling of existing roads location, designing and building new roads, roads seasonality. A functional summary of MapFlow plugin for road recognition in Open Source QGIS is given. Paper is a part of regional forestry transport modeling project to access the forest fires and forest resources by ground means.https://jfsi.ru/6-1-2023-podolskaya/remote sensing data from spaceroad networkimage recognitionforestryneural networksconvolutional neural networksopen source qgispluginsmapflow |
| spellingShingle | E. S. Podolskaia REMOTE SENSING DATA FROM SPACE FOR ROAD IMAGE RECOGNITION IN THE FORESTRY Вопросы лесной науки remote sensing data from space road network image recognition forestry neural networks convolutional neural networks open source qgis plugins mapflow |
| title | REMOTE SENSING DATA FROM SPACE FOR ROAD IMAGE RECOGNITION IN THE FORESTRY |
| title_full | REMOTE SENSING DATA FROM SPACE FOR ROAD IMAGE RECOGNITION IN THE FORESTRY |
| title_fullStr | REMOTE SENSING DATA FROM SPACE FOR ROAD IMAGE RECOGNITION IN THE FORESTRY |
| title_full_unstemmed | REMOTE SENSING DATA FROM SPACE FOR ROAD IMAGE RECOGNITION IN THE FORESTRY |
| title_short | REMOTE SENSING DATA FROM SPACE FOR ROAD IMAGE RECOGNITION IN THE FORESTRY |
| title_sort | remote sensing data from space for road image recognition in the forestry |
| topic | remote sensing data from space road network image recognition forestry neural networks convolutional neural networks open source qgis plugins mapflow |
| url | https://jfsi.ru/6-1-2023-podolskaya/ |
| work_keys_str_mv | AT espodolskaia remotesensingdatafromspaceforroadimagerecognitionintheforestry |