Digital signage is an important outdoor advertising medium in cities. However, advertising on digital signage often lacks pertinence. Thus, it is important to introduce an accurate digital signage audience classification method to facilitate targeted advertising. In this study, a multi-label classification model based on a backpropagation (BP) neural network and the Huff model, referred to as the Huff-BP model, is proposed to investigate digital signage audience classification. A case study is performed on outdoor digital signage within the 6th Ring Road in Beijing, China, and economic census, population census, average housing price, social media check-in and the centrality of traffic networks as research data. The data are divided into 100 × 100-1,000 × 1,000 m normal grids. Multi-label classification modelling factors for various grid scales are constructed. The BP neural network classification algorithm is improved to solve the multilabel classification problem. In addition, an improved Huff model is used to calculate the digital signage influence values between each grid cell and integrated into the improved BP neural network to classify modelling factors at various scales. Finally, four metrics are used to examine the effectiveness of the proposed model. The results show that the Huff-BP-based multi-label classification model achieves relatively good classification results, and the digital signage audiences are mainly concentrated within the 4th Ring Road and near the 5th Ring Road.

Under the background of global climate change, the impact from drought on the ecosystem exhibits the characteristics of complexity and multi-process, especially for the main component, which is the grassland ecosystem of the overall ecosystem. Identifying past droughts and predicting future ones is vital in limiting their effects. However, the random and non-linear nature of drought variables makes accurate drought prediction still a challenging scientific problem. In this study, the boundaries, Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) of Asian Grassland Ecosystem (AGE) were obtained by Google Earth Engine (GEE), which were used to construct LST-EVI feature spaces to calculate the dry-wet edge fitting equations and Temperature Vegetation Drought Index (TVDI). Mann–Kendall test and Sen trend degrees were further used to analyze the drought trend of AGE. The results showed that there were obvious spatial differences in the wet and dry conditions of AGE, which showed that the TVDI increased from east to west and from north to south, with humid areas mainly concentrated in northern Asia and severe drought areas concentrated in southern Asia. From 2010 to 2018, the area of humid areas and severe drought areas of AGE decreased, and some humid areas changed to normal areas or even drought areas, while the drought in severe drought areas was alleviated. The results of the Sen trend test further show that the aggravating trend of drought in severe drought areas of South Asia is relatively low, and some areas show a trend of changing to humidity. However, there is an obvious aggravating trend of drought in humid areas or low drought areas of South Asia, these areas should also be the focus areas for drought prevention in the future. This study identified the spatio-temporal distribution characteristics and evaluated the evolution trend of the drought of AGE, which is of great significance to the management and prevention of drought of AGE.