H.6.5.10. Remote sensing
Mehdi Alizadeh; Parvin Ahmadi; Masoumeh Azimzadeh
Abstract
Field boundary detection is a critical task in modern agriculture, enabling precision farming, optimized resource management, and efficient crop monitoring. Despite its importance, existing deep learning models often fail to achieve high accuracy in delineating field boundaries due to challenges such ...
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Field boundary detection is a critical task in modern agriculture, enabling precision farming, optimized resource management, and efficient crop monitoring. Despite its importance, existing deep learning models often fail to achieve high accuracy in delineating field boundaries due to challenges such as complex landscapes, varying resolutions, and noise in remote sensing images. To overcome these limitations, we propose HURA-Net, an advanced deep learning framework that intelligently integrates UNet++, ResUNet, and an attention mechanism into a unified architecture. By hybridizing these models, HURA-Net effectively combines their strengths—such as multi-scale feature extraction (UNet++), residual learning (ResUNet), and focus on salient regions (attention mechanism)—while minimizing their individual weaknesses. To further enhance performance, we introduce a refined loss function that not only improves segmentation precision but also addresses the class imbalance problem, which is common in boundary detection tasks. Extensive experiments on a diverse dataset of high-resolution satellite images from different regions of Iran demonstrate that HURA-Net significantly outperforms existing state-of-the-art models. Specifically, it achieves a recall of 45.85% (a 15.59% improvement over ResUNet) and an F1-score of 42.62% (7.27% higher than ResUNet), setting a new benchmark for accuracy. Moreover, our study highlights the critical role of strategic data augmentation in boosting model generalization, particularly in handling variations in lighting, crop types, and field shapes. The success of HURA-Net underscores the importance of innovative architecture design, optimized loss functions, and robust training strategies in advancing remote sensing image segmentation.
H.3. Artificial Intelligence
Saiful Bukhori; Muhammad Almas Bariiqy; Windi Eka Y. R; Januar Adi Putra
Abstract
Breast cancer is a disease of abnormal cell proliferation in the breast tissue organs. One method for diagnosing and screening breast cancer is mammography. However, the results of this mammography image have limitations because it has low contrast and high noise and contrast as non-coherence. This research ...
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Breast cancer is a disease of abnormal cell proliferation in the breast tissue organs. One method for diagnosing and screening breast cancer is mammography. However, the results of this mammography image have limitations because it has low contrast and high noise and contrast as non-coherence. This research segmented breast cancer images derived from Ultrasonography (USG) photo using a Convolutional Neural Network (CNN) using the U-Net architecture. Testing on the CNN model with the U-Net architecture results the highest Mean Intersection over Union (Mean IoU) value in the data scenario with a ratio of 70:30, 100 epochs, and a learning rate of 5x10-5, which is 77%, while the lowest Mean IoU in the data scenario with a ratio 90:10, 50 epochs, and a learning rate of 1x10-4 learning rate, which is 64.4%.
