In this paper we explore deep learning models to monitor longitudinal liveability changes in Dutch cities at the neighbourhood level. Our liveability reference data is defined by a country-wise yearly survey based on a set of indicators combined into a liveability score, the Leefbaarometer. We pair this reference data with yearly-available high-resolution aerial images, which creates yearly timesteps at which liveability can be monitored. We deploy a convolutional neural network trained on an aerial image from 2016 and the Leefbaarometer score to predict liveability at new timesteps 2012 and 2020. The results in a city used for training (Amsterdam) and one never seen during training (Eindhoven) show some trends which are difficult to interpret, especially in light of the differences in image acquisitions at the different time steps. This demonstrates the complexity of liveability monitoring across time periods and the necessity for more sophisticated methods compensating for changes unrelated to liveability dynamics.

翻译：本文探索利用深度学习模型监测荷兰城市街区层面的纵向宜居性变化。我们的宜居性参考数据基于全国年度调查，该调查通过一组指标综合计算宜居性得分（Leefbaarometer）。我们将参考数据与每年可获取的高分辨率航拍影像配对，从而形成可监测宜居性变化的年度时间步。我们采用基于2016年航拍图像和Leefbaarometer得分训练的卷积神经网络，预测2012年和2020年两个新时间步的宜居性。在训练城市（阿姆斯特丹）和训练中未见过的城市（埃因霍温）的实验结果显示，某些变化趋势难以解释，尤其考虑到不同时间步影像采集的差异。这表明跨时段宜居性监测的复杂性，以及需要更精细的方法来补偿与宜居性动态无关的变化。