论文标题
卫星图像预测的城市变化
Urban Change Forecasting from Satellite Images
论文作者
论文摘要
预测新建筑物将出现在何时何地是一个尚未开发的话题，但是在许多学科中，诸如城市规划，农业，资源管理甚至自动飞行等许多学科都非常有用。在目前的工作中，我们提出了一种通过深层神经网络和自定义预处理程序来完成此任务的方法。在第1阶段，在暹罗网络体系结构中鉴定了一个U-NET主链，该网络架构旨在解决（构建）更改检测任务。在第2阶段中，将重新使用骨干，以预测仅根据其构建之前获得的一张图像的新建筑物的出现。此外，我们还提出了一个模型，该模型可以预测发生变化的时间范围。我们使用SpaceNet7数据集验证了我们的方法，该数据集涵盖了两年的时间24点的960 km^2。在我们的实验中，我们发现我们提出的训练方法始终使用ImageNet数据集优于传统预审计。我们还表明，在某种程度上有可能预测何时发生建筑物变化。
Forecasting where and when new buildings will emerge is a rather unexplored topic, but one that is very useful in many disciplines such as urban planning, agriculture, resource management, and even autonomous flying. In the present work, we present a method that accomplishes this task with a deep neural network and a custom pretraining procedure. In Stage 1, a U-Net backbone is pretrained within a Siamese network architecture that aims to solve a (building) change detection task. In Stage 2, the backbone is repurposed to forecast the emergence of new buildings based solely on one image acquired before its construction. Furthermore, we also present a model that forecasts the time range within which the change will occur. We validate our approach using the SpaceNet7 dataset, which covers an area of 960 km^2 at 24 points in time across two years. In our experiments, we found that our proposed pretraining method consistently outperforms the traditional pretraining using the ImageNet dataset. We also show that it is to some degree possible to predict in advance when building changes will occur.
