论文标题
分层的地形注意力和多尺度降雨指导洪水图像预测
Hierarchical Terrain Attention and Multi-Scale Rainfall Guidance For Flood Image Prediction
论文作者
论文摘要
随着气候的恶化，雨水引起的洪水的现象变得频繁。为了减轻其影响，最近的作品采用了卷积神经网络或其变体来预测洪水。但是，这些方法直接迫使模型通过全局约束重建洪水图像的原始像素，从而俯瞰着地形特征和降雨模式中包含的潜在信息。为了解决这个问题，我们为精确的洪水图预测提供了一个新颖的框架，该框架结合了层次地形的空间关注，以帮助该模型关注地形特征的空间空间区域，并构造嵌入多规模的降雨，以将降雨模式广泛地整合到一代中。为了更好地适应各种降雨条件下的模型，我们利用发电机和鉴别器的降雨回归损失作为额外的监督。对实际流域数据集的广泛评估证明了我们方法的出色性能，这在不同的降雨条件下大大超过了先前的艺术。
With the deterioration of climate, the phenomenon of rain-induced flooding has become frequent. To mitigate its impact, recent works adopt convolutional neural network or its variants to predict the floods. However, these methods directly force the model to reconstruct the raw pixels of flood images through a global constraint, overlooking the underlying information contained in terrain features and rainfall patterns. To address this, we present a novel framework for precise flood map prediction, which incorporates hierarchical terrain spatial attention to help the model focus on spatially-salient areas of terrain features and constructs multi-scale rainfall embedding to extensively integrate rainfall pattern information into generation. To better adapt the model in various rainfall conditions, we leverage a rainfall regression loss for both the generator and the discriminator as additional supervision. Extensive evaluations on real catchment datasets demonstrate the superior performance of our method, which greatly surpasses the previous arts under different rainfall conditions.
