Nighttime light imageries are widely used for mapping the gross domestic product (GDP) over large areas. Future work will improve the model accuracy in densely populated areas. The RF model performs better at building-scale population estimation using easily accessible multisource geographic data. In a comparison with a multiple linear (ML) regression model, the mean absolute errors of the RF and ML models are 2.52 and 3.21, respectively, the root mean squared errors are 8.2 and 9.8, and the R2 values are 0.44 and 0.18. The results show that 91% of the buildings in Lin’an District have absolute error values of less than six compared with the actual population data.
Then, a building-scale population estimation model is trained by a random forest (RF) regression algorithm. In this paper, 30 features are extracted using easily accessible multisource geographic data. Geospatial big data and machine learning provide new fine-scale population distribution mapping methods. For example, large-scale population censuses cannot be conducted in underdeveloped countries or regions, and remote sensing data lack semantic information indicating the different human activities occurring in a precise geographic location. Previous approaches have used remotely sensed imagery to disaggregate census data, but this approach has limitations. Fine-scale population spatialization is essential for urbanization and disaster prevention. Population spatialization reveals the distribution and quantity of the population in geographic space with gridded population maps. KeywordsPoint of interest (POI)-Location-based service (LBS)-Global Positioning System (GPS)-Quality filtering-Simplification The research introduces a system that provides an interoperableįramework in which to work with other geospatial services through open geospatial standards. Techniques using the Douglas-Peucker algorithm and PgRouting. This research shows functionalities that can minimize GPS errors using Dilution of Precision filtering and data quality enhancing The system supports real-time data collection for the future ubiquitous environment and also can monitor real-time GPS positions. The main function of the system can be separated into three parts: data collection, data management, and data quality enhancement.
#GPSBABEL SHORT GSA SENTENCE ARCHIVE#
GPS track log and point of interest (POI) management was developed to archive a collaborative framework in field surveys. In this research, a Web-based prototype system for Projects such as OpenStreetMap or other User Generated Contents services. High-speed broadband technology has promoted collaborative Recent advanced performance of low-cost Global Positioning System (GPS) and GPS-enabled cell phones has contributed a greatĭeal to the development of location-aware services and systems.
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