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.
For more details see:
GnssMapper depends on the GeoPandas package and its underlying dependencies, including PyGeos. If you do not have these installed, we recommend following the instructions.
- 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
Distribution are available from the Python Package Index
$ pip install gnssmapperReport bugs, suggest features or view the source code at https://github.com/Indicative-Data-Science/gnssmapper.
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. .. code-block:: pycon
>>> 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)
Processed GNSS data for use in the mapping algorithm. Each observation is a single segment linestring from the receiver towards the relevant satellite, along with signal features. .. code-block:: pycon
>>> 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 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:: .. code-block:: pycon
>>> 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:: .. code-block:: pycon
- >>> gm.predict(map,obs)
lower_bound mid_point upper_bound
0 47.359955 52.545442 57.73093
GnssMapper can simulate observations if given a map, based on fresnel attenuation of the rays. .. code-block:: pycon
>>> 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>
https://github.com/Indicative-Data-Science/gnssmapper/tree/master/examplefiles has an example gnsslogger file and 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 .. code-block:: pycon
>>> pilot_log = gpd.read_file("zip://./examplefiles/pilot_study.geojson.zip", driver="GeoJSON") >>> import geopandas as gpd >>> pilot_log.time = pilot_log.time.astype('datetime64') >>> pilot_log.svid = pilot_log.svid.astype('string')