USE OF REMOTE SENSING DATA FROM SPACE FOR ROAD IMAGE RECOGNITION IN THE FORESTRY

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

 

E-mail: podols_kate@mail.ru

Received: 08.10.2022

Revised: 19.12.2022

Accepted: 20.12.2022

 

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.

Key words: remote sensing data from space, road network, image recognition, forestry, neural networks, convolutional neural networks, Open Source QGIS, plugins, MapFlow

 

REFERENCES

Akay A. E., Tas I., Using drone with a lidar data capture systems in forestry applications, International Academic Research Congress, INES, 2018, pp. 17–25.

Andreeva O. A., Konon N. I., Ratinskij M. G., K voprosu ispol’zovanija distancionnogo zondirovanija mestnosti pri proektirovanii zheleznyh dorog (To the question of use remote sensing for railways construction), Geodezija i kartografija, 2019, No 5. pp. 47–53. DOI: 10.22389/0016-7126-2019-947-5-47-53.

Ayala C., Aranda C., Galar M., Towards fine-grained roads maps extraction using Sentinel-2 imagery, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXIV ISPRS Congress (2021 edition), 2021, Vol. 3, pp. 9–14.

Barrile V., Bilotta G., Fotia F., Bernardo E., Road extraction for emergencies from satellite imagery, ICCSA 2020, LNCS, pp. 767–781, DOI: 10.1007/978-3-030-58811-3_55.

Bekturov A. K., Primenenie materialov ajerokosmicheskih s”emok v izyskanijah i proektirovanii avtomobil’nyh dorog v uslovijah vysokogor’ja (Use of aerospace survey materials in the exploration and design of highways under high-altitude conditions), Vestnik Kyrgyzskogo gosudarstvennogo universiteta stroitel’stva, transporta i arhitektury im. N. Isanova, 2015, No 3 (49), pp. 43–48.

Bhil K., Shindihatti R., Mirza S., Latkar S., Ingle Y. S., Shaikh N. F., Prabu I., Pardeshi S. N., Recent progress in object detection in satellite imagery: a review, Sustainable Advanced Computing. Lecture Notes in Electrical Engineering, 2022, Vol. 840, pp. 209–218, DOI: doi.org/10.1007/978-981-16-9012-9_18.

Botelho J. Jr., Costa S. C. P., Ribeiro J. G., Souza C. M. Jr., Mapping roads in the Brazilian Amazon with artificial intelligence and Sentinel-2, Remote Sensing, 2022, Vol. 14, pp. 1–17, DOI: 10.3390/ rs14153625.

Сaliskan E., Sevim Y., Forest road detection using deep learning models, Geocarto International, 2022, Vol. 37, No 20, pp. 5875–5890, DOI: 10.1080/10106049.2021.1926555.

Chelnokov V. V., Meshalkin V. P., Strelkov S. P., Kondrashin K. G., Vizualizacija dannyh distancionnogo zondirovanija dorozhnyh setej v celjah analiza jekologicheskogo i socio-jekologicheskogo vozdejstvija (Visualization of remote sensing data of road networks for the analysis of ecological and socio-ecological impact), Geodezija i kartografija, 2021, No 3, pp. 36–43, DOI: 10.22389/0016-7126-2021-969-3-36-43.

Dolgopolov D. V., Ispol’zovanie dannyh distancionnogo zondirovanija Zemli pri formirovanii geoinformacionnogo prostranstva truboprovodnogo transporta (Use of Earth remote sensing data in the geoinformation space of pipeline transport), Vestnik SGUGiT, 2020, Vol. 25, No 3, pp. 151–159.

Dolgopolov D. V., Nikonov D. V., Polujanova A. V., Melkij V. A., Vozmozhnosti vizual’nogo deshifrirovanija magistral’nyh truboprovodov i ob”ektov infrastruktury po sputnikovym izobrazhenijam vysokogo i sverhvysokogo prostranstvennogo razreshenija (Possibilities of visual decoding of trunk pipelines and infrastructure facilities from satellite images of high and ultra-high spatial resolution), Vestnik SGUGiT, 2019, Vol. 24, No 3, pp. 65–81.

Fedoseev A. A., Miheeva T. I., Saprykin O. P., Mingazov P. P., Raspoznavanie ob”ektov transportnoj infrastruktury na giperspektral’nyh snimkah metodom glubinnogo mashinnogo obuchenija (Recognition of transport infrastructure objects on hyperspectral images by deep machine learning), Informacionnye tehnologii intellektual’noj podderzhki prinjatija reshenij (ITIDS’2016), Trudy IV Mezhdun. konf., Ufa, Izd-vo UGATU, 2016, pp. 39–44.

Fedoseev A. A., Miheeva T. I., Miheev S. V., Postroenie modeli transportnoj infrastruktury na osnove prostranstvenno-spektral’noj ajerokosmicheskoj informacii (Construction of transport infrastructure model based on the spatial-spectral aerospace information), Programmnye produkty i sistemy, 2018, No 1 (31), pp. 25–31.

Filatova A. V., Pozdysheva O. N., Kistineva A. O., Analiz processa deshifrirovanija pri stroitel’stve avtomobil’nyh dorog (Analysis of recognition in the construction of highways), Jelektronnyj nauchnyj zhurnal “Inzhenernyj vestnik Dona”, 2017, No 2, pp. 1–11.

Gecen R., Sarp G., Road detection from high and low resolution satellite images, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2008, Vol. XXXVII, Part B4, Beijing, pp. 355–358.

Gomes O. F. M., Feitosa R. Q., Coutinho H. L., C. Sub-pixel unpaved roads detection in Landsat images, Proceedings of XXXV Congress ISPRS, Committee 3, 2015, pp. 1–5.

Guindon B., Application of spatial reasoning methods to the extraction of roads from high-resolution satellite imagery, IGARSS ‘98. Sensing and Managing the Environment. International Geoscience and Remote Sensing. Symposium Proceedings, 1998, Vol. 2, pp. 1076–1078, DOI: 10.1109/IGARSS.1998.699677.

Gulci S., Akgul M., Akay A. E., Tas I., Using ready-to-use drone images in forestry activities: case study of Cinarpinar in Kahramanmaras, Turkey, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLII-4/W6, 2017, 4th International GeoAdvances Workshop 14–15 October 2017, Safranbolu, Karabuk, Turkey. pp. 51–53.

Henry C., Azimi S. M., Merkle N., Road segmentation in SAR satellite images with deep fully convolutional neural networks, IEEE Geoscience and Remote Sensing Letters, 2018, pp. 1–6. DOI: 10.1109/LGRS.2018.2864342.

Ignat’ev A. V., Kulikov M. A., Capiev D. N., Tyrin V. V., Metodika avtomaticheskoj klassifikacii dorog s ispol’zovaniem nejronnoj seti Mask R-CNN (Method of automatic classification of roads using the neural network Mask R-CNN), Jelektronnyj nauchnyj zhurnal “Inzhenernyj vestnik Dona”, 2022, No 5, pp. 1–9.

Kobzeva E. A., Jeksperimental’naja ocenka tochnosti i deshifrovochnyh vozmozhnostej kosmicheskih snimkov RapidEye (Experimental evaluation of accuracy and decoding capabilities of RapidEye satellite images), Geomatics, 2010, No 2, pp. 33–36.

Maillard Ph., Cavayas F., Automatic map-guided extraction of roads from SPOT imagery for cartographic database updating, International Journal of Remote Sensing, 1989, No 10 (11), pp. 1775–1787, DOI: 10.1080/01431168908904007.

Mei J., Li R.-J., Gao W., Cheng M.-M., CoANet: Connectivity attention network for road extraction from satellite imagery, IEEE Transactions on Image Processing, 2021, Vol. 30, pp. 8540–8552, DOI: 10.1109/TIP.2021.3117076.

Mena J. B., State of the art on automatic road extraction for GIS update: a novel classification, Pattern Recognition Letters, 2003, Vol. 24, pp. 3037–3058.

Miheeva T. I., Fedoseev A. A., Klasterizacija giperspektral’nyh dannyh monitoringa ob’’ektov transportnoj infrastruktury (Clustering of hyperspectral monitoring data of transport infrastructure facilities), Izvestija Samarskogo nauchnogo centra Rossijskoj akademii nauk, 2014, Vol. 16, No 4 (2), pp. 404–408.

Miheeva T. N., Fedoseev A. A., Identifikacija izmenenij konfiguracii transportnoj seti na osnove kosmicheskoj s”emki (Identification of transport network configuration changes based on satellite imagery), Izvestija Samarskogo nauchnogo centra Rossijskoj akademii nauk, 2016, Vol. 18, No 4 (4), pp. 808–814.

Miroshnichenko S. Ju., Titov V. S., Jashhenko A. A., Metod avtomaticheskoj lokalizacii protjazhennyh geoprostranstvennyh ob”ektov (Method of automatic localization of extended geospatial objects), Izvestija vuzov. Priborostroenie, 2013, Vol. 56, No 6, pp. 17–22.

Muhametshin A. R., Samsonov T. E., Deshifrirovanie kosmicheskih snimkov s ispol’zovaniem mashinnogo obuchenija po dannym OpenStreetMap (Space images interpretation with machine learning based on the OpenStreetMap) Nauchnye issledovanija molodyh uchenyh-kartografov, vypolnennye pod rukovodstvom sotrudnikov kafedry kartografii i geoinformatiki geograficheskogo fakul’teta MGU imeni M. V. Lomonosova: sbornik statej, pod red. A. M. Karpachevskogo, Moscow, “KDU”, “Dobrosvet”, 2022, pp. 64–72.

Nachmany Y., Alemohammad H., Detecting roads from satellite imagery in the developing world, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 84–89.

Oehmcke S., Thrysoe C., Borgstad A., Vaz Salles M. A., Brandt M., Gieseke F., Detecting hardly visible roads in low-resolution satellite time series data, IEEE International Conference on Big Data, 2019, pp. 1–10, DOI:10.1109/BigData47090.2019.9006251.

Orlov V. A., Avtomatizirovannoe raspoznavanie lesnyh dorog po kosmicheskim snimkam (Automated recognition of forest roads based on satellite images), Aktual’nye problemy lesnogo kompleksa, 2006, No 14, pp. 1–4.

Pestunov I. A., Rylov S. A., Segmentacija sputnikovyh izobrazhenij vysokogo razreshenija po spektral’nym i teksturnym priznakam (Segmentation of satellite images of high spatial resolution by spectral and textural features), InterJekspo Geo-Sibir’, 2012, Vol. 1, No 4, pp. 86–91.

Podolskaia E. S., Sezonnost’ dorog v transportnom modelirovanii GIS-proekta lesnogo hozjajstva (Seasonality of roads in the transport modeling of GIS forestry project), Fundamental’nye, poiskovye, prikladnye issledovanija i innovacionnye proekty. Sbornik trudov Nacional’noj nauchno-prakticheskoj konferencii, Moscow, RTU MIRJeA, 2022, pp. 267–271.

Podolskaia E. S., Primenenie kosmicheskih skanernyh snimkov dlja ob”ektivizacii kartograficheskoj generalizacii na obzorno-topograficheskih kartah (Application of space scanner images for objectification of map generalization on the survey-and-topographic maps), Izv. vuzov. Ser. Geodezija i ajerofotos”emka, 2005, No 5, pp. 83–96.

Podolskaia E. S., Ershov D. V., Kovganko K. A., Transportnoe modelirovanie nazemnogo dostupa dlja bor’by s lesnymi pozharami na urovne Federal’nyh okrugov Rossii (Transport modeling of ground access to fight the forest fires at the level of the Federal Districts of Russia), Sbornik statej po itogam nauchno-tehnicheskih konferencij, 2020, No 11, Moscow: MIIGAiK. Prilozhenie k zhurnalu Izvestija vuzov “Geodezija i ajerofotos”emka”, pp. 154–156.

Podolskaia E., Ershov D., Kovganko K., Comparison of data sources on transport infrastructure for the regional forest fire management, Managing forests in the 21^st century: Book of abstracts, Managing forests in the 21^st century, Conference at the Potsdam Institute for Climate Impact Research, Potsdam 2020, 59 p. DOI: 10.2312/pik.2020.002.

Riedl M., Berthold I., Schauer P., Angelhuber M., Fischer P., Canzani E., How many roads? Object segmentation on satellite imagery in a production environment, Proceedings of 2019 Big Data from Space (BiDS’19), 19-21 February 2019, Munich (Germany), pp. 173–176. DOI: 10.2760/848593.

Satyanarayana V. L., Chandana Ch. G., Bindusha K., Padmanabhudu D., Shahanaaz bhanu G., Extraction of roads from satellite resolution images using Matlab, International Journal of Engineering Applied Sciences and Technology, 2020, Vol. 4, No 12, pp. 708–714.

Shoshina K. V., Sistema monitoringa i issledovanija lesnyh dorog (Forest road monitoring and research system), Vestnik Severnogo (Arkticheskogo) Federal’nogo universiteta. Serija: Estestvennye nauki, 2013, No 4, pp. 50–54.

Skripachev V. O., Gujda M. V., Gujda N. V., Zhukov A. O., Issledovanie svertochnyh nejronnyh setej dlja obnaruzhenija ob”ektov na ajerokosmicheskih snimkah (Research of convolutional neural networks to detect objects on the aerospace images), International Journal of Open Information Technologies, 2022, Vol. 10, No 7, pp. 54–64.

Tormozov V. S., Vasilenko K. A., Zolkin A. L., Nastrojka i obuchenie mnogoslojnogo perseptrona dlja zadachi vydelenija dorozhnogo pokrytija na kosmicheskih snimkah goroda (Setup and training of a multilayer perceptron for the task of highlighting the pavement on satellite images of the city), Programmnye produkty i sistemy, 2020, Vol. 33, No 2, pp. 343–348, DOI: 10.15827/0236-235X.130.343-348.

Turk Y., Boz F., Aydin A., Eker R., Evaluation of UAV usage possibility in determining the forest roads pavement degradation: preliminary results. 3rd International Engineering Research Symposium, INERS’19. Comparison of Autonomous and Manual UAV Flights in Determining Forest Road Surface Deformations, European Journal of Forest Engineering, 2022, Vol. 8 (2), pp. 77–84.

Tusikova A. A., Vihtenko Je. M., O raspoznavanii avtomobil’nyh dorog na sputnikovyh snimkah s ispol’zovaniem svertochnyh setej MASK-RCNN (About the recognition of highways on satellite images using convolutional networks MASK-CNN), V Mezhdunarodnaja konferencija “Informacionnye tehnologii i vysokoproizvoditel’nye vuchyslenija” (ITHPC-2019), Habarovsk, Rossija, 2019, pp. 308–314.

Wei X., Fu X., Yun Y., Lv X., Multiscale and multitemporal road detection from high resolution SAR images based attention mechanism, Remote Sensing, 2021, Vol. 13, p. 3149. DOI: 10.3390/rs 13163149.

Zegeye A., Road extraction from satellite imagery based on fully convolutional neural network, IOSR Journal of Computer Engineering (IOSR-JCE), 2020, Vol. 22, No 4, Ser. II, pp. 59–72, DOI: 10.9790/0661-2204035972.

Zhurkin I. G., Badyshev T. T., Analiz izmenenij zheleznodorozhnoj seti po kosmicheskim snimkam (Analysis of changes in the railway network based on satellite images), Izvestija vysshih uchebnyh zvedenij. Geodezija i ajerofotos”emka, 2014, Vol. 3, pp. 83–86.