Abstract. Unmanned Aerial System (UAS) imagery has enabled very high-resolution multispectral image acquisition. Detection of wet areas and classification of land cover based on these images using the Machine Learning (ML) algorithm named Random Forest (RF) is our main purpose in this paper. Very high-resolution UAS images have been used as inputs for a machine learner to access the capability of different spectral bands and spectral vegetation indices, elevation, and texture features in the classification of land cover and detection of the wet riparian area in the case study in two different epochs. There are many existing methods for the classification of land cover based on UAS images, but very high-resolution centimeter-level data are of main importance in this analysis. Outstanding results have been produced in both epochs considering three extremely accurate performance analysers. Additionally, in this research, the most decisive and effective features have been discovered to compromise accuracy and the number of effectual features.

Abstract. This study explores the spatial relationship between the number of recorded events of the Safe Delivery App, which is a mobile learning tool to train midwives in developing countries, during three months, and several independent variables, including the number of health facilities, pregnancies, number of women of childbearing age, number of infants aged 0–1 years, mobile network coverage data and total population density. The study aims to identify and analyse the reach of the Safe Delivery App at the district level in Ghana country, considering the correlation between dependent and independent variables. The geospatial analysis of App usage events layered with several related explanatory variables is based on Geographically Weighted Regression. The explanatory variables were able to predict and explain the number of events with of accuracy score of 90 % at the district level. The results have provided valuable insights into the further roll-out of the App and helped to highlight the districts that need further support to roll out the Safe Delivery App considering the analysed independent variables.