Crowdsourcing Data Engine Driven Deep Adaption Network for Global Urban Green Space Changes Monitoring

Accurate characterization of global urban green space (UGS) changes is essential for understanding the urban climate changes and supporting sustainable development goal (SDG) 11 of the 2030 Agenda. However, there is still a lack of high-spatiotemporal resolution monitoring of global UGS changes over the past three decades. This study developed a crowdsourcing data engine driven deep adaption network for monitoring global UGS changes. First, a crowdsourcing data engine is developed to create UGS label samples. Second, a deep adaption UGSs extraction network is proposed to enhance global UGS dynamics mapping accuracy in the lack of time-series label samples. Our method yielded an average accuracy of 85.13% for annual global UGS mapping from 1993–2022. Analyzing the global UGS change results, we found that areas of global UGS during 1993–2022 increased by non-UGS convert to UGS, expanding by 76.92 thousand km². The proposed method has significant application value for SDG 11.7 indicator monitoring by leveraging geospatial artificial intelligence and big earth data.