Tools for generating 3D maps from GNSS data
GnssMapper provides tools for generating 3D maps by using Global Navigation Satellite System (GNSS) data. It is connected to a research project at the University of Glasgow, which investigates methods for using crowdsourced GNSS data for mapping. It is written in Python and built upon GeoPandas objects.
It provides the following capabilities:
- read 'raw' GNSS data from Google's gnsslogger app, available for Android phones
- process data into a set of observations
- estimate building heights based on the observations
- simulate observations for algorithm testing
It does not include any functionality for processing GNSS data in order to estimate position, and assumes position data is available from the log file, or calculated elsewhere.
GnssMapper depends on the GeoPandas package (and underlying dependencies for pandas, shapely, fiona, and pyproj). It also depends on pygeos to speed up vectorised geometry operations. We recommend following the Geopandas instructions for installing these packages.
GnssMapper has only been tested with the following setup:
Python : 3.9.1
GEOS : 3.9.0
GDAL : 3.2.1
PROJ : 7.2.1
geopandas : 0.8.2
pandas : 1.2.2
fiona : 1.8.18
numpy : 1.19.5
shapely : 1.7.1
pyproj : 3.0.0.post1
pygeos : 0.9
Most methods return GeoPandas GeoDataFrames in particular forms.
A set of GNSS data generated from gnsslogger output. A collection of 3D points with time column, representing receiver position, along with additional signal features (e.g. satellite identifier (svid) and signal strength(CN0DbHz)). An example gnsslogger file is included in the folder 'examplefiles'.
>>> import gnssmapper as gm
>>> log = gm.read_gnsslogger("./examplefiles/gnss_log_2020_02_11_08_49_29.txt")
>>> log[['svid','time','Cn0DbHz','geometry']].head()
svid time Cn0DbHz geometry
0 G02 2020-02-11 08:49:27.999559028 22.34062 POINT Z (-0.13414 51.52471 114.85894)
1 G05 2020-02-11 08:49:27.999559028 26.320181 POINT Z (-0.13414 51.52471 114.85894)
2 G07 2020-02-11 08:49:27.999559028 47.322662 POINT Z (-0.13414 51.52471 114.85894)
3 G09 2020-02-11 08:49:27.999559028 35.282738 POINT Z (-0.13414 51.52471 114.85894)
4 G13 2020-02-11 08:49:27.999559028 22.712795 POINT Z (-0.13414 51.52471 114.85894)
Observations are a processed set of GNSS data for use in the mapping algorithm, generated from Receiverpoints. . A collection of 3D rays in 3D, along with signal features. A ray is a single segment linestring which represents a direct path from the receiver towards the relevant satellite, truncated at 1km in length. In order to retrieve information on the historic positions of satellites, GnssMapper downloads data from the ESA. Downloading and parsing the data is slow, so a local cache is generated, and loaded into memory as required.
>>> obs = gm.observe(pilot_log)
{'2020063', '2020045', '2020066', '2020044'} orbits are missing and must be created.
downloading sp3 file for 2020063.
creating 2020063 orbit.
saving 2020063 orbit.
....
>>> obs.head()
time svid Cn0DbHz geometry
0 2020-03-03T10:20:19 C10 NaN LINESTRING Z (3976545.346 -9309.219 4970128.21...
1 2020-03-03T10:20:19 C14 NaN LINESTRING Z (3976545.346 -9309.219 4970128.21...
2 2020-03-03T10:20:19 C21 NaN LINESTRING Z (3976545.346 -9309.219 4970128.21...
3 2020-03-03T10:20:19 C22 NaN LINESTRING Z (3976545.346 -9309.219 4970128.21...
4 2020-03-03T10:20:19 C24 NaN LINESTRING Z (3976545.346 -9309.219 4970128.21...
The expected map form is a collection of 2D polygons, with a height column. This represents a simple LOD1 3D map. It can be initialised from a 2D map with a blank height column. For the pilot study, a map of a single building was generated from Ordnance Survey's Mastermap.
>>> map_ = gpd.read_file('./examplefiles/map.geojson')
>>> map_
height geometry
0 0 POLYGON ((529552.750 182350.500, 529548.950 18...
Given a map of floorplates and a set of observations, the height of map elements can be predicted from the observations. GnssMapper implements a bootstrapped four-parameter logistic regression developed by the project. This fits a four-parameter logistic regression to the data and estimates the height based on model parameters.
>>> gm.predict(map_,obs)
lower_bound mid_point upper_bound
0 47.359955 52.545442 57.73093
In order to test the mapping algorithm, GnssMapper includes the ability to simulate observations. This uses a map (with a ground truth height) and generates a set of Observations by simulating the Receiver location and generating a signal strength based on fresnel attenuation of the rays.
>>> import geopandas as gpd
>>> import pandas as pd
>>> start = pd.Timestamp('2020-02-11T11')
>>> end = pd.Timestamp('2020-02-11T12')
>>> sim = gm.simulate(map_, "point_process", 100, start, end)
>>> sim.head()
time svid geometry fresnel Cn0DbHz
0 2020-02-11 11:49:20.360557432 C10 LINESTRING Z (529644.220 182254.036 1.000, 530... 0.0 34.165532
1 2020-02-11 11:49:20.360557432 C14 LINESTRING Z (529644.220 182254.036 1.000, 528... 116.001472 <NA>
2 2020-02-11 11:49:20.360557432 C21 LINESTRING Z (529644.220 182254.036 1.000, 529... 0.0 39.337049
3 2020-02-11 11:49:20.360557432 C24 LINESTRING Z (529644.220 182254.036 1.000, 528... 96.973759 <NA>
4 2020-02-11 11:49:20.360557432 C26 LINESTRING Z (529644.220 182254.036 1.000, 529... 59.631021 <NA>
In the folder 'examplefiles' there is a receiverpoint file created as part of a pilot study, that can be used for testing and analysis. This can be loaded using GeoPandas but note that some processing of datatypes is required
>>> import geopandas as gpd
>>> pilot_log = gpd.read_file("zip://./examplefiles/pilot_study.geojson.zip", driver="GeoJSON")
>>> pilot_log.time = pilot_log.time.astype('datetime64')
>>> pilot_log.svid = pilot_log.svid.astype('string')