Abstract
Digital Terrain Models (DTMs) used for the representation of the bare earth are produced from elevation data obtained using high-end mapping platforms and technologies. These require the handling of complex post-processing performed by authoritative and commercial mapping agencies. In this research, we aim to exploit user-generated data to produce DTMs by handling massive volumes of position and elevation data collected using ubiquitous smartphone devices equipped with Assisted-GPS sensors. As massive position and elevation data are collected passively and straightforwardly by pedestrians, cyclists, and drivers, it can be transformed into valuable topographic information. Specifically, in dense and concealed built and vegetated areas, where other technologies fail, handheld devices have an advantage. Still, Assisted-GPS measurements are not as accurate as high-end technologies, requiring pre- and post-processing of observations. We propose the development and implementation of a 2D Kalman filter and smoothing on the acquired crowdsourced observations for topographic representation production. When compared to an authoritative DTM, results obtained are very promising in producing good elevation values. Today, open-source mapping infrastructures, such as OpenStreetMap, rely primarily on the global authoritative SRTM (Shuttle Radar Topography Mission), which shows similar accuracy but inferior resolution when compared to the results obtained in this research. Accordingly, our crowdsourced methodology has the capacity for reliable topographic representation production that is based on ubiquitous volunteered user-generated data.
Highlights
A Digital Terrain Model (DTM) serves as the digital representation of the bare earth, usually in raster or vector-based Triangular Irregular Network (TIN) format
Our crowdsourced methodology has the capacity for reliable topographic representation production that is based on ubiquitous volunteered user-generated data
Due to the fact that large portions of the boundary conditions use values that are based on extrapolation points, elevation bias is introduced into the Kalman filter process, which is manifested mainly in the maximum difference value, which is more than two times larger than of the previous area. This is definitely the main issue of using the 2D Kalman filter for Digital Terrain Models (DTMs) smoothing; the results show that if the bias is not too severe, the solution will not skew the results significantly, as they are still qualitative, where the implementation of the Kalman filter manages to improve the original raw results
Summary
A Digital Terrain Model (DTM) serves as the digital representation of the bare earth, usually in raster (grid) or vector-based Triangular Irregular Network (TIN) format. DTMs are used for a variety of applications, such as GIS, city modeling, land use studies, drainage control, and geology—to name a few. DTMs were almost exclusively produced via scanning and digitizing of contour maps. DTMs are often produced from data collected using high-end mapping platforms and technologies, such as LiDAR (Light Detection and Ranging), aerial and satellite photogrammetry, and InSAR (Interferometric Synthetic Aperture Radar). Regardless of the method used to produce the DTMs, most are produced by authoritative and commercial mapping agencies, such that the data collection process and post-processes tend to be costly and time-consuming, involving specialists in the field. It is common that high-quality DTMs are not available in the public domain and must be purchased. There exist free open-source DTMs, such as ASTER
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