论文标题
Synthinel-1数据集：高分辨率合成间接费用图像的集合
The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation
论文作者
论文摘要
最近，深度学习 - 卷积神经网络（CNN） - 为在大型开销（例如卫星）图像基准上建立细分的任务带来了令人印象深刻的表现。但是，这些基准数据集仅捕获现实世界间开销图像中存在的可变性的一小部分，从而限制了适当训练或评估现实世界应用模型的能力。不幸的是，由于图像的成本和图像的手动像素标签，开发一个捕获甚至一小部分现实世界可变性的数据集通常是不可行的。在这项工作中，我们开发了一种方法来快速，廉价地产生大型多样化的虚拟环境，从中我们可以从中捕获合成间接费用图像，以供训练分割CNN。使用这种方法，生成并公开释放合成间接费用图像的集合 - 称为Synthinel-1具有完整的像素建筑标签。我们使用几个基准数据集来证明合成1在增加现实世界训练图像时始终是有益的，尤其是当在新的地理位置或条件上测试CNN时。
Recently deep learning - namely convolutional neural networks (CNNs) - have yielded impressive performance for the task of building segmentation on large overhead (e.g., satellite) imagery benchmarks. However, these benchmark datasets only capture a small fraction of the variability present in real-world overhead imagery, limiting the ability to properly train, or evaluate, models for real-world application. Unfortunately, developing a dataset that captures even a small fraction of real-world variability is typically infeasible due to the cost of imagery, and manual pixel-wise labeling of the imagery. In this work we develop an approach to rapidly and cheaply generate large and diverse virtual environments from which we can capture synthetic overhead imagery for training segmentation CNNs. Using this approach, generate and publicly-release a collection of synthetic overhead imagery - termed Synthinel-1 with full pixel-wise building labels. We use several benchmark dataset to demonstrate that Synthinel-1 is consistently beneficial when used to augment real-world training imagery, especially when CNNs are tested on novel geographic locations or conditions.
