FloodNet is a high-resolution image dataset acquired by a small UAV platform, DJI Mavic Pro quadcopters, after Hurricane Harvey. The dataset presents a unique challenge of advancing the damage assessment process for post-disaster scenarios using unlabeled and limited labeled dataset. We propose a solution to address their classification and semantic segmentation challenge. We approach this problem by generating pseudo labels for both classification and segmentation during training and slowly incrementing the amount by which the pseudo label loss affects the final loss. Using this semi-supervised method of training helped us improve our baseline supervised loss by a huge margin for classification, allowing the model to generalize and perform better on the validation and test splits of the dataset. In this paper, we compare and contrast the various methods and models for image classification and semantic segmentation on the FloodNet dataset.

翻译：FloodNet是一个由 " 哈维 " 飓风后的小型无人驾驶航空器平台DJI Mavic Pro Quadcopters所获取的高分辨率图像数据集。该数据集对利用无标签和有限的标签数据集推进灾后情景的损害评估进程提出了独特的挑战。我们提出了解决其分类和语义分割挑战的解决方案。我们通过在培训期间制作分类和分解的假标签和缓慢增加假标签损失影响最终损失的数额来解决这一问题。使用这种半监督的培训方法,帮助我们通过一个巨大的分类差幅改进了我们基准监测的损失,使模型能够对数据集的验证和测试分解进行概括化和更好地运行。在本文中,我们比较和比较了在FloodNet数据集上图像分类和分解的各种方法和模型。