Urban human settlements monitoring model and its application based on multi-source spatial data fusion

Monitoring of human settlement environment, a basic practice in urban human settlement environment construction and management, is the focus of human settlement environment research. The traditional urban human settlement environmental data have shortcomings in terms of renewal speed and accuracy. This paper proposes to use remote sensing images and point of interest(POI) from the internet to build a human settlement environment model. The model is composed of two key parts. The first part involves constructing an automatic building extraction algorithm, which are the global optimization and region growth algorithms, based on urban land coverage feature sets and taking corresponding POI point samples as seeds. The second part involves the calculation of the human settlement environment index using density and distance spatial analysis algorithms, which use the land cover extraction results with POI data as input data. Based on the above model, Beijing-2 remote sensing images and POI data in April 2018 were used to verify the results in the Huilongguan community of Beijing. The results show that the overall accuracy of the information extraction results is more than 95%, the Kappa coefficient is more than 92%, and the extraction efficiency is improved by 2.3-fold. On applying the model to monitor human settlements in Huilongguan community, it was found that there is little difference in natural indicators, but due to the overall lack of water ecosystem, biodiversity is not rich enough. Socioeconomic indicators mainly account for the impact of business, school, and medical care. On the whole, businesses in the community are prosperous, and schools and medical care are adequate, the latter is the case especially in large hospitals. Through the research and application analysis of the human settlements monitoring model, the application of remote sensing data and internet data fusion in human settlement environmental quality monitoring effectively improves the accuracy and speed and is conducive to business applications and government management.