LiDAR (short for "Light Detection And Ranging" or "Laser Imaging, Detection, And Ranging") technology can be used to provide detailed three-dimensional elevation maps of urban and rural landscapes. To date, airborne LiDAR imaging has been predominantly confined to the environmental and archaeological domains. However, the geographically granular and open-source nature of this data also lends itself to an array of societal, organizational and business applications where geo-demographic type data is utilised. Arguably, the complexity involved in processing this multi-dimensional data has thus far restricted its broader adoption. In this paper, we propose a series of convenient task-agnostic tile elevation embeddings to address this challenge, using recent advances from unsupervised Deep Learning. We test the potential of our embeddings by predicting seven English indices of deprivation (2019) for small geographies in the Greater London area. These indices cover a range of socio-economic outcomes and serve as a proxy for a wide variety of downstream tasks to which the embeddings can be applied. We consider the suitability of this data not just on its own but also as an auxiliary source of data in combination with demographic features, thus providing a realistic use case for the embeddings. Having trialled various model/embedding configurations, we find that our best performing embeddings lead to Root-Mean-Squared-Error (RMSE) improvements of up to 21% over using standard demographic features alone. We also demonstrate how our embedding pipeline, using Deep Learning combined with K-means clustering, produces coherent tile segments which allow the latent embedding features to be interpreted.
翻译:liDAR (“ 光探测和测距” 或“ 激光测距、 测距、 测距、 测距” ) 技术可以用来提供详细的城乡景观三维升程图。 到目前为止, 空载的 LiDAR 成像主要局限于环境和考古领域。 然而, 这些数据的地理颗粒和开源性质也有利于一系列社会、 组织和商业应用, 其中使用了地理人口类型的数据。 可以说, 处理这一多维数据的复杂性已经大大限制了它的广泛采用。 本文中, 我们建议了一系列方便的任务- 不可知地梯升升嵌入的三维高图, 以迎接这一挑战, 使用未经监督的深层学习的最新进步。 我们测试了我们嵌入的可能性, 预测了7个英国失学指数( 2019) 用于大伦敦地区的小地理地理图。 这些指数覆盖一系列社会经济结果, 并用作一系列可应用嵌入的下游数据的替代数据。 我们认为, 这个数据是否适合使用直径直径直径的直径, 的直径直径, 也是我们用直径直径直径的内径的内嵌的内径, 。