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Deep Learning based Aerial Image Segmentation for Computing Green Area Factor
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0001-7387-6650
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-0579-7181
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-3122-693X
2022 (English)In: 2022 10th European Workshop on Visual Information Processing (EUVIP), IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

The Green Area Factor(GYF) is an aggregate norm used as an index to quantify how much eco-efficient surface exists in a given area. Although the GYF is a single number, it expresses several different contributions of natural objects to the ecosystem. It is used as a planning tool to create and manage attractive urban environments ensuring the existence of required green/blue elements. Currently, the GYF model is gaining rapid attraction by different communities. However, calculating the GYF value is challenging as significant amount of manual effort is needed. In this study, we present a novel approach for automatic extraction of the GYF value from aerial imagery using semantic segmentation results. For model training and validation a set of RGB images captured by Drone imaging system is used. Each image is annotated into trees, grass, soil/open surface, building, and road. A modified U-net deep learning architecture is used for the segmentation of various objects by classifying each pixel into one of the semantic classes. From the segmented image we calculate the class-wise fractional area coverages that are used as input into the simplified GYF model called Sundbyberg for calculating the GYF value. Experimental results yield that the deep learning method provides about 92% mean IoU for test image segmentation and corresponding GYF value is 0.34.

Place, publisher, year, edition, pages
IEEE, 2022.
Series
European Workshop on Visual Information Processing, ISSN 2471-8963, E-ISSN 2164-974X
Keywords [en]
green Area index, deep learning, CNN, image, segmentation, urban planning, semantic classification
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
Identifiers
URN: urn:nbn:se:oru:diva-102544DOI: 10.1109/EUVIP53989.2022.9922743ISI: 000886233300019Scopus ID: 2-s2.0-85141101986ISBN: 9781665466233 (electronic)ISBN: 9781665466240 (print)OAI: oai:DiVA.org:oru-102544DiVA, id: diva2:1716056
Conference
10th European Workshop on Visual Information Processing (EUVIP), Lisbon, Portugal, September 11-14, 2022
Available from: 2022-12-05 Created: 2022-12-05 Last updated: 2022-12-05Bibliographically approved

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Rahaman, G M AtiqurLängkvist, MartinLoutfi, Amy

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Citation style
  • apa
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Output format
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  • asciidoc
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