Transformer-based semantic segmentation for large-scale building footprint extraction from very-high resolution satellite images

Gibril, Mohamed Barakat A.; Al-Ruzouq, Rami; Shanableh, Abdallah; Jena, Ratiranjan; Bolcek, Jan; Shafri, Helmi Zulhaidi Mohd; Ghorbanzadeh, Omid

doi:10.1016/j.asr.2024.03.002

Cited by 3 publications

References 53 publications

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An extensive analysis of artificial intelligence and segmentation methods transforming cancer recognition in medical imaging

Ramalakshmi,

Srinivasa Raghavan,

Rajagopal

et al. 2024

Biomed. Phys. Eng. Express

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Recent advancements in computational intelligence, deep learning, and computer-aided detection have had a significant impact on the field of medical imaging. The task of image segmentation, which involves accurately interpreting and identifying the content of an image, has garnered much attention. The main objective of this task is to separate objects from the background, thereby simplifying and enhancing the significance of the image. However, existing methods for image segmentation have their limitations when applied to certain types of images. This survey paper aims to highlight the importance of image segmentation techniques by providing a thorough examination of their advantages and disadvantages. The accurate detection of cancer regions in medical images is crucial for ensuring effective treatment. In this study, we have also extensively analysed Computer-Aided Diagnosis (CAD) systems for cancer identification, with a focus on recent research advancements. The paper critically assesses various techniques for cancer detection and compares their effectiveness. Convolutional neural networks (CNNs) have attracted particular interest due to their ability to segment and classify medical images in large datasets, thanks to their capacity for self-learning and decision-making.

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An extensive analysis of artificial intelligence and segmentation methods transforming cancer recognition in medical imaging

Ramalakshmi,

Srinivasa Raghavan,

Rajagopal

et al. 2024

Biomed. Phys. Eng. Express

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Influence of Augmentation of Uav Collected Data on Deep Learning Based Facade Segmentation Task

Balak,

Łysak,

Choromański

et al. 2024

Preprint

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MSTrans: Multi-Scale Transformer for Building Extraction from HR Remote Sensing Images

Yang,

Jiang,

et al. 2024

Electronics

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Buildings are one of the most important goals of human transformation of the Earth’s surface. Therefore, building extraction (BE), such as in urban resource management and planning, is a task that is meaningful to actual production and life. Computational intelligence techniques based on convolutional neural networks (CNNs) and Transformers have begun to be of interest in BE, and have made some progress. However, the BE methods based on CNNs are limited by the difficulty in capturing global long-range relationships, while Transformer-based methods are often not detailed enough for pixel-level annotation tasks because they focus on global information. To conquer the limitations, a multi-scale Transformer (MSTrans) is proposed for BE from high-resolution remote sensing images. In the proposed MSTrans, we develop a plug-and-play multi-scale Transformer (MST) module based on atrous spatial pyramid pooling (ASPP). The MST module can effectively capture tokens of different scales through the Transformer encoder and Transformer decoder. This can enhance multi-scale feature extraction of buildings, thereby improving the BE performance. Experiments on three real and challenging BE datasets verify the effectiveness of the proposed MSTrans. While the proposed approach may not achieve the highest Precision and Recall accuracies compared with the seven benchmark methods, it improves the overall metrics F1 and mIoU by 0.4% and 1.67%, respectively.

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Transformer-based semantic segmentation for large-scale building footprint extraction from very-high resolution satellite images

Cited by 3 publications

References 53 publications

An extensive analysis of artificial intelligence and segmentation methods transforming cancer recognition in medical imaging

An extensive analysis of artificial intelligence and segmentation methods transforming cancer recognition in medical imaging

Influence of Augmentation of Uav Collected Data on Deep Learning Based Facade Segmentation Task

MSTrans: Multi-Scale Transformer for Building Extraction from HR Remote Sensing Images

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