Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery
| العنوان: | Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery |
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| المؤلفون: | Mila Koeva, George Vosselman, Michael Ying Yang, Sophie Crommelinck |
| المساهمون: | Department of Earth Observation Science, UT-I-ITC-ACQUAL, Faculty of Geo-Information Science and Earth Observation, Department of Urban and Regional Planning and Geo-Information Management, UT-I-ITC-PLUS |
| المصدر: | Remote Sensing, Vol 11, Iss 21, p 2505 (2019) Remote sensing, 11(21):2505, 1-22. MDPI Remote Sensing; Volume 11; Issue 21; Pages: 2505 |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | 010504 meteorology & atmospheric sciences, Computer science, Cadastre, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, Convolutional neural network, Boundary (real estate), boundary delineation, image analysis, lcsh:Science, Image resolution, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, business.industry, Deep learning, deep learning, Image segmentation, 15. Life on land, indirect surveying, boundary extraction, machine learning, ITC-ISI-JOURNAL-ARTICLE, General Earth and Planetary Sciences, RF, lcsh:Q, Artificial intelligence, business, cadastral mapping, ITC-GOLD, CNN |
| الوصف: | Cadastral boundaries are often demarcated by objects that are visible in remote sensing imagery. Indirect surveying relies on the delineation of visible parcel boundaries from such images. Despite advances in automated detection and localization of objects from images, indirect surveying is rarely automated and relies on manual on-screen delineation. We have previously introduced a boundary delineation workflow, comprising image segmentation, boundary classification and interactive delineation that we applied on Unmanned Aerial Vehicle (UAV) data to delineate roads. In this study, we improve each of these steps. For image segmentation, we remove the need to reduce the image resolution and we limit over-segmentation by reducing the number of segment lines by 80% through filtering. For boundary classification, we show how Convolutional Neural Networks (CNN) can be used for boundary line classification, thereby eliminating the previous need for Random Forest (RF) feature generation and thus achieving 71% accuracy. For interactive delineation, we develop additional and more intuitive delineation functionalities that cover more application cases. We test our approach on more varied and larger data sets by applying it to UAV and aerial imagery of 0.02–0.25 m resolution from Kenya, Rwanda and Ethiopia. We show that it is more effective in terms of clicks and time compared to manual delineation for parcels surrounded by visible boundaries. Strongest advantages are obtained for rural scenes delineated from aerial imagery, where the delineation effort per parcel requires 38% less time and 80% fewer clicks compared to manual delineation. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2072-4292 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cbd4c9e18bcd1fb98aec8efc7322eb37 https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2019/isi/crommelinck_app.pdf |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....cbd4c9e18bcd1fb98aec8efc7322eb37 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20724292 |
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