Poverty is a global challenge, the effects of which are felt on the individual to national scale. To develop effective support policies to reduce poverty, local governments require precise poverty distribution data, which are lacking in many areas. In this study, we proposed a model to estimate poverty on a spatial scale of 10 × 10 km by combining features extracted from multiple data sources, including nighttime light remote sensing data, normalized difference vegetation index, surface reflectance, land cover type, and slope data, and applied the model to Nigeria. Considering that the trends of environmental factors contain valid information related to poverty, time-series features were extracted through convolutional long short-term memory and used for the assessment. The poverty level is represented by the wealth index derived from the Demographic and Health Survey Program. The model exhibited good ability to estimate poverty, with an R ² of 0.73 between the actual and estimated wealth index in Nigeria in 2018. Applying the proposed model to poverty estimation for Nigeria in 2021 yielded an R ² value of 0.69, indicating good generalization ability. To further validate model reliability, we compared the assessment results with high-resolution satellite imagery and a state-level multidimensional poverty index. We also investigated the impact of incorporating time-series features on the accuracy of poverty assessment. Results showed that the addition of time-series features increased the accuracy of poverty estimation from 0.64 to 0.73. The proposed method has valuable applications for estimating poverty at the grid scale in countries without such data.

Landslide susceptibility is crucial for assessing the probability and severity of landslide disasters in a region. Previous studies have focused on static landslide susceptibility, using landslide assessment factor data from varying years, making it difficult to estimate spatio-temporal consistency and resulting in low prediction accuracy. Taking Hong Kong, China, as the study region, this study proposes a framework to estimate spatio-temporally consistent landslide susceptibility. The landslide assessment factors are divided into static and dynamic factors, with a temporal resolution of 5 years. Specifically, the dynamic assessment of landslide susceptibility is conducted for the periods 2000–2004, 2005–2009, 2010–2014, and 2015–2019, covering a total span from 2000 to 2019. Results show that the accuracy of the proposed model, defined as the proportion of correctly classified samples relative to the total number of samples, exceeds 0.7 across these four time periods. Both the F1-Score and the receiver operating characteristic (ROC) curve indicate that the proposed research framework exhibits good accuracy and practicality in susceptibility assessment. The proposed framework could capture temporal variations in landslide occurrence, allowing for a more accurate prediction of landslide susceptibility. The findings provide valuable insights for landslide disaster prevention and mitigation in Hong Kong and would also be applicable in other countries or regions.