Efficient geospatial mapping of buildings, woodlands, water and roads from aerial imagery using deep learning

As more aerial imagery becomes readily available, massive volumes of data are being gathered constantly. Several groups can benefit from the data provided by this geographical imagery. However, it is time-consuming to manually analyze each image to gain information on land cover. This research sugge...

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Bibliographic Details
Published in:PeerJ. Computer science 2024-06, Vol.10, p.e2039, Article e2039
Main Authors: Abbas, Sidra, Almadhor, Ahmad, Sampedro, Gabriel Avelino, Alsubai, Shtwai, Al Hejaili, Abdullah, Strážovská, Ľubomíra, Zaidi, Monji Mohamed
Format: Article
Language:English
Subjects:
Algorithms and Analysis of Algorithms
Ariel imagery
Artificial Intelligence
Computer Vision
Data augmentation
Deep learning
Geospatial information
Land cover
Natural resources
Poland
Spatial and Geographic Information Systems
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Summary:As more aerial imagery becomes readily available, massive volumes of data are being gathered constantly. Several groups can benefit from the data provided by this geographical imagery. However, it is time-consuming to manually analyze each image to gain information on land cover. This research suggests using deep learning methods for precise and rapid pixel-by-pixel classification of aerial imagery for land cover analysis, which would be a significant step forward in resolving this issue. The suggested method has several steps, such as the augmentation and transformation of data, the selection of deep learning models, and the final prediction. The study uses the three most popular deep learning models (Vanilla-UNet, ResNet50 UNet, and DeepLabV3 ResNet50) for the experiments. According to the experimental results, the ResNet50 UNet model achieved an accuracy of 94.37%, the DeepLabV3 ResNet50 model achieved an accuracy of 94.77%, and the Vanilla-UNet model achieved an accuracy of 91.31%. The accuracy, precision, recall, and F1-score of DeepLabV3 and ResNet50 are higher than those of the other two models. The proposed approach is also compared to the existing UNet approach, and the proposed approaches have produced greater probability prediction scores than the conventional UNet model for all classes. Our approach outperforms model DeepLabV3 ResNet50 on aerial image datasets based on the performance.
ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.2039