Automatic Deep Inference of Procedural Cities from Global-scale Spatial Data

IF 1.2 Q4 REMOTE SENSING

ACM Transactions on Spatial Algorithms and Systems Pub Date : 2020-10-27 DOI:10.1145/3423422

ZhangXiaowei, ShehataAly, BenešBedřich, AliagaDaniel

引用次数: 3

Abstract

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 urb...

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基于全球尺度空间数据的程序性城市自动深度推理

大空间数据采集和深度学习的最新进展使得几年前不可能实现的新算法成为可能。提出了一种新的逆过程建模算法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACM Transactions on Spatial Algorithms and Systems REMOTE SENSING-

CiteScore

4.40

自引率

5.30%

发文量

期刊介绍： ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes the highest quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective in that it spans a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually) such as geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.