Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images

Elif Sertel*, Burak Ekim, Paria Ettehadi Osgouei, M. Erdem Kabadayi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


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.

Original languageEnglish
Article number4558
JournalRemote Sensing
Issue number18
Publication statusPublished - Sept 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors.


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.

FundersFunder number
Horizon 2020 Framework Programme679097
European Research Council


    • image classification
    • image segmentation
    • land use/land cover
    • remote sensing
    • Worldview-3


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