Automatic Deep Inference of Procedural Cities from Global-scale Spatial Data
Automatic Deep Inference of Procedural Cities from Global-scale Spatial Data
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DOI：
10.1145/3423422
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发表时间：
2020-10
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通讯作者：
Xiaowei Zhang;Aly Shehata;Daniel G. Aliaga
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作者：
Xiaowei Zhang;Aly Shehata;Daniel G. Aliaga
大空间数据采集和深度学习的最新进展允许几年前不可能实现的新算法。我们介绍了一种新的逆过程建模算法的城市地区，解决了空间数据的质量和不确定性的问题。我们的方法是全自动的，并在给定卫星图像和全球范围的数据（包括道路网络，人口和高程数据）的情况下生成城市区域的3D近似值。通过分析城市数据的价值和分布，例如，地块，建筑物，人口和海拔，我们构建了一个大规模的城市的程序近似。我们的方法有三个主要组成部分：（1）程序模型生成，以创建地块和建筑物几何形状，（2）地块面积估计，训练神经网络为城市街区的分割卫星图像提供初始地块大小，以及（3）可选优化，可以使用整体平均建筑物占地面积和建筑物数量的部分知识来改善结果。我们在地球仪上展示和评估了我们的方法，这些城市具有广泛不同的结构，并自动生成多达91，000座建筑物的程序模型，跨度高达150平方公里。我们得到的空间布置的地块和建筑物类似地面真相和建筑物的大小分布类似地面真相，从而产生一个统计上类似的合成城市空间。我们在多个尺度上生成程序模型，与地面实况相比，最好的情况下地块和建筑面积的误差小于1%，测试城市的平均误差为5.8%。
Recent advances in big spatial data acquisition and deep learning allow novel algorithms that were not possible several years ago. We introduce a novel inverse procedural modeling algorithm for urban areas that addresses the problem of spatial data quality and uncertainty. Our method is fully automatic and produces a 3D approximation of an urban area given satellite imagery and global-scale data, including road network, population, and elevation data. By analyzing the values and the distribution of urban data, e.g., parcels, buildings, population, and elevation, we construct a procedural approximation of a city at a large-scale. Our approach has three main components: (1) procedural model generation to create parcel and building geometries, (2) parcel area estimation that trains neural networks to provide initial parcel sizes for a segmented satellite image of a city block, and (3) an optional optimization that can use partial knowledge of overall average building footprint area and building counts to improve results. We demonstrate and evaluate our approach on cities around the globe with widely different structures and automatically yield procedural models with up to 91,000 buildings, and spanning up to 150 km2. We obtain both a spatial arrangement of parcels and buildings similar to ground truth and a distribution of building sizes similar to ground truth, hence yielding a statistically similar synthetic urban space. We produce procedural models at multiple scales, and with less than 1% error in parcel and building areas in the best case as compared to ground truth and 5.8% error on average for tested cities.