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United Nations University

Academic institutionnorthamerica · us
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Research library7linked papers
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Selected work

Representative Papers

Explainable Flood Segmentation on Sentinel-1 SAR Imagery: A Comparative Study of CNN and Transformer Architectures

Jun 15, 2026

This study addresses the challenge of distinguishing inundated land from permanent water bodies in Sentinel-1 SAR imagery by systematically evaluating the performance of convolutional neural networks (U-Net, U-Net++, DeepLabV3) and vision Transformers (SegFormer-b0/b1/b2) on multi-class flood segmentation tasks. To assess spatial generalization, the authors introduce a scene-partitioning evaluation strategy and employ interpretability techniques—including Grad-CAM and uncertainty estimation—to elucidate differences in model decision mechanisms. Experimental results demonstrate that SegFormer-b2 significantly outperforms CNN-based models on the ETCI dataset; although this advantage narrows after fine-tuning on Sen1Floods11, SegFormer-b2 maintains superior performance in spatially fragmented flood scenarios. Interpretability analyses further confirm that its activations are more tightly aligned with genuine flood signatures, underscoring the potential and reliability of Transformer architectures for SAR-based flood mapping.

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FLoRA: Fusion-Latent for Optical Reconstruction and Flood Area Segmentation via Cross-Modal Multi-Task Distillation Network

May 03, 2026

Existing approaches struggle to effectively integrate the complementary information from spaceborne optical and SAR imagery, limiting the accuracy of flood mapping. This work proposes a cross-modal multi-task distillation network that simultaneously achieves high-fidelity optical image reconstruction and flood region segmentation within a unified latent space. The method innovatively combines a lightweight optical teacher guidance module, multi-scale windowed cross-attention, FiLM-based conditional modulation, and gated residual connections, alongside a dual-decoder architecture and a hydrologically aware loss function incorporating Charbonnier SSIM, edge-preserving FFT magnitude, and Dice-BCE alignment. Evaluated on SEN1FLOODS11, DEEPFLOOD, and SEN12MS datasets, the approach significantly outperforms current fusion methods in terms of PSNR, SSIM, and LPIPS, markedly enhancing flood mapping quality.

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Interoperability in AI Safety Governance: Ethics, Regulations, and Standards

Jan 06, 2026arXiv.org

This study addresses structural and conceptual gaps in AI safety governance—particularly regulatory fragmentation, insufficient global coordination, and limited participation from the Global South—that hinder cross-jurisdictional interoperability. Through a comparative analysis of China, South Korea, Singapore, and the United Kingdom across three high-risk domains—autonomous vehicles, education, and cross-border data flows—the research systematically integrates ethical, legal, and technical governance dimensions. It identifies convergences and divergences across seven key aspects: governance objectives, regulatory authorities, ethical principles, enforcement mechanisms, domain-specific frameworks, technical standards, and critical risks. Aligning interoperability pathways with the UN-endorsed Global Digital Compact and relevant UN resolutions, the study offers actionable policy recommendations that balance local contexts with global cooperation, thereby advancing an inclusive, effective, and trustworthy AI safety governance framework.

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Recent publications

Latest Papers

Explainable Flood Segmentation on Sentinel-1 SAR Imagery: A Comparative Study of CNN and Transformer Architectures

Jun 15, 2026

This study addresses the challenge of distinguishing inundated land from permanent water bodies in Sentinel-1 SAR imagery by systematically evaluating the performance of convolutional neural networks (U-Net, U-Net++, DeepLabV3) and vision Transformers (SegFormer-b0/b1/b2) on multi-class flood segmentation tasks. To assess spatial generalization, the authors introduce a scene-partitioning evaluation strategy and employ interpretability techniques—including Grad-CAM and uncertainty estimation—to elucidate differences in model decision mechanisms. Experimental results demonstrate that SegFormer-b2 significantly outperforms CNN-based models on the ETCI dataset; although this advantage narrows after fine-tuning on Sen1Floods11, SegFormer-b2 maintains superior performance in spatially fragmented flood scenarios. Interpretability analyses further confirm that its activations are more tightly aligned with genuine flood signatures, underscoring the potential and reliability of Transformer architectures for SAR-based flood mapping.

0 citationsRead paper

FLoRA: Fusion-Latent for Optical Reconstruction and Flood Area Segmentation via Cross-Modal Multi-Task Distillation Network

May 03, 2026

Existing approaches struggle to effectively integrate the complementary information from spaceborne optical and SAR imagery, limiting the accuracy of flood mapping. This work proposes a cross-modal multi-task distillation network that simultaneously achieves high-fidelity optical image reconstruction and flood region segmentation within a unified latent space. The method innovatively combines a lightweight optical teacher guidance module, multi-scale windowed cross-attention, FiLM-based conditional modulation, and gated residual connections, alongside a dual-decoder architecture and a hydrologically aware loss function incorporating Charbonnier SSIM, edge-preserving FFT magnitude, and Dice-BCE alignment. Evaluated on SEN1FLOODS11, DEEPFLOOD, and SEN12MS datasets, the approach significantly outperforms current fusion methods in terms of PSNR, SSIM, and LPIPS, markedly enhancing flood mapping quality.

0 citationsRead paper

Interoperability in AI Safety Governance: Ethics, Regulations, and Standards

Jan 06, 2026arXiv.org

This study addresses structural and conceptual gaps in AI safety governance—particularly regulatory fragmentation, insufficient global coordination, and limited participation from the Global South—that hinder cross-jurisdictional interoperability. Through a comparative analysis of China, South Korea, Singapore, and the United Kingdom across three high-risk domains—autonomous vehicles, education, and cross-border data flows—the research systematically integrates ethical, legal, and technical governance dimensions. It identifies convergences and divergences across seven key aspects: governance objectives, regulatory authorities, ethical principles, enforcement mechanisms, domain-specific frameworks, technical standards, and critical risks. Aligning interoperability pathways with the UN-endorsed Global Digital Compact and relevant UN resolutions, the study offers actionable policy recommendations that balance local contexts with global cooperation, thereby advancing an inclusive, effective, and trustworthy AI safety governance framework.

0 citationsRead paper