Zhen Qian

TL;DR: In this article , a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery, which can be used for data support and decision-making to facilitate sustainable urban development effectively.

TL;DR: Wang et al. as discussed by the authors used a deep learning-based method to detect existing noise barrier sites from massive street-view images, and then identified the planned barrier sites with urban policies.

TL;DR: In this paper , a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery, which can be used for data support and decision-making to facilitate sustainable urban development effectively.

TL;DR: Wang et al. as discussed by the authors proposed Deep Roof Refiner, an end-to-end and detail-oriented deep learning network and proposed a synthetic strategy to enhance the network's performance.

TL;DR: A vectorized RNB dataset in China using street view imagery and a geospatial artificial intelligence framework is created and can be applied as an accurate and reliable dataset for a variety of large-scale urban studies, such as estimating the regional solar photovoltaic potential, developing 3D urban models, and designing rational urban layouts.