{"id":5669,"date":"2023-04-19T16:01:57","date_gmt":"2023-04-19T13:01:57","guid":{"rendered":"https:\/\/jfsi.ru\/?p=5669"},"modified":"2023-04-19T18:09:08","modified_gmt":"2023-04-19T15:09:08","slug":"5-4-2022-podolskaia","status":"publish","type":"post","link":"https:\/\/jfsi.ru\/en\/5-4-2022-podolskaia\/","title":{"rendered":"USE OF REMOTE SENSING DATA FROM SPACE FOR ROAD IMAGE RECOGNITION IN THE FORESTRY"},"content":{"rendered":"

\"\"<\/a><\/p>\n

E. S. Podolskaia <\/strong><\/span><\/p>\n

Center for Forest Ecology and Productivity of the Russian Academy of Sciences <\/em><\/span><\/p>\n

Profsoyuznaya st. 84\/32 bldg. 14, <\/em>Moscow, 117997<\/em>,<\/em> Russian Federation<\/em><\/span><\/p>\n

\u00a0<\/em><\/span><\/p>\n

E-mail: podols_kate@mail.ru<\/span><\/p>\n

Received: 08.10.2022<\/span><\/p>\n

Revised: 19.12.2022<\/span><\/p>\n

Accepted: 20.12.2022<\/span><\/p>\n

\u00a0<\/em><\/span><\/p>\n

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. We have characterized principles of road detection on the imagery. A group of direct deciphering signs used in combinations such as brightness and texture, geometry and brightness. Three research directions with examples identified: visual roads recognition, use of special software and libraries for developers, and use of 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. Actual tasks of road recognition are the following: evaluation of road surface condition, modeling of existing roads location, designing and building new roads, seasonality of roads use. A functionality 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.<\/span><\/p>\n

Key words:<\/em><\/strong> remote sensing data from space, road network, image recognition, forestry, neural networks, convolutional neural networks, Open Source QGIS, plugins, MapFlow<\/em><\/span><\/p>\n

\u00a0<\/em><\/span><\/p>\n

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\u00a0<\/strong><\/span><\/p>\n

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E. S. Podolskaia Center for Forest Ecology and Productivity of the Russian Academy of Sciences Profsoyuznaya st. 84\/32 bldg. 14, Moscow, 117997, Russian Federation \u00a0 E-mail: podols_kate@mail.ru Received: 08.10.2022 Revised: 19.12.2022 Accepted: 20.12.2022 \u00a0...<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[27],"tags":[],"_links":{"self":[{"href":"https:\/\/jfsi.ru\/en\/wp-json\/wp\/v2\/posts\/5669"}],"collection":[{"href":"https:\/\/jfsi.ru\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jfsi.ru\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/jfsi.ru\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/jfsi.ru\/en\/wp-json\/wp\/v2\/comments?post=5669"}],"version-history":[{"count":6,"href":"https:\/\/jfsi.ru\/en\/wp-json\/wp\/v2\/posts\/5669\/revisions"}],"predecessor-version":[{"id":5684,"href":"https:\/\/jfsi.ru\/en\/wp-json\/wp\/v2\/posts\/5669\/revisions\/5684"}],"wp:attachment":[{"href":"https:\/\/jfsi.ru\/en\/wp-json\/wp\/v2\/media?parent=5669"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jfsi.ru\/en\/wp-json\/wp\/v2\/categories?post=5669"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jfsi.ru\/en\/wp-json\/wp\/v2\/tags?post=5669"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}