Land use and land cover mapping using deep learning based segmentation approaches and VHR Worldview-3 images

Sertel, E., Ekim, B., Ettehadi Osgouei, P. and Kabadayi, M. E. (2022) Land use and land cover mapping using deep learning based segmentation approaches and VHR Worldview-3 images. Remote Sensing, 14(18), 4558. (doi: 10.3390/rs14184558)

Abstract

Deep learning-based segmentation of very high-resolution (VHR) satellite images is a significant task providing valuable information for various geospatial applications, specifically for land use/land cover (LULC) mapping. The segmentation task becomes more challenging with the increasing number and complexity of LULC classes. In this research, we generated a new benchmark dataset from VHR Worldview-3 images for twelve distinct LULC classes of two different geographical locations. We evaluated the performance of different segmentation architectures and encoders to find the best design to create highly accurate LULC maps. Our results showed that the DeepLabv3+ architecture with an ResNeXt50 encoder achieved the best performance for different metric values with an IoU of 89.46%, an F-1 score of 94.35%, a precision of 94.25%, and a recall of 94.49%. This design could be used by other researchers for LULC mapping of similar classes from different satellite images or for different geographical regions. Moreover, our benchmark dataset can be used as a reference for implementing new segmentation models via supervised, semi- or weakly-supervised deep learning models. In addition, our model results can be used for transfer learning and generalizability of different methodologies.

• Item Type: Articles
• Additional Information: This work was supported by the European Research Council (ERC) project: “Industrialisation and Urban Growth from the mid-nineteenth century Ottoman Empire to Contemporary Turkey in a Comparative Perspective, 1850–2000” under the European Union’s Horizon 2020 research and innovation program Grant Agreement No. 679097, acronym UrbanOccupationsOETR. M. Erdem Kabadayı is the principal investigator of UrbanOccupationsOETR.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: KABADAYI, Dr ERDEM

Creator Roles

Kabadayi, M. E.: Investigation, Resources, Writing – original draft, Writing – review and editing, Supervision, Project administration, Funding acquisition

• Authors: Sertel, E., Ekim, B., Ettehadi Osgouei, P., and Kabadayi, M. E.
• College/School: College of Science and Engineering > School of Geographical and Earth Sciences
• Journal Name: Remote Sensing
• Publisher: MDPI
• ISSN: 2072-4292
• ISSN (Online): 2072-4292
• Published Online: 12 September 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Remote Sensing 14(18): 4558
• Publisher Policy: Reproduced under a Creative Commons License

Related URLs

• Research Data

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
314747		Industrialisation and Urban Growth from the mid-nineteenth century Ottoman Empire to Contemporary Turkey in a Comparative Perspective, 1850-2000	Ana Basiri	European Commission (EC)	679097	GES - Geography