# A Filtering-Based Approach for Improving Crowdsourced GNSS Traces in a Data Update Context

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Methodology

#### 3.1. Pre-Processing Filtering

#### 3.2. Filtering Secondary Human Behaviour

#### 3.3. Filtering Outliers

#### Definition of Intrinsic and Extrinsic Indicators for Describing GNSS Points

_{i}and Vi represent, respectively, the distance and the speed between points i, and i − 1. Similarly, SpeedRate represents the velocity change rate as suggested by [51].

## 4. Experimental Results

#### 4.1. Test Data Description

#### 4.2. Detection of Secondary Human Behaviour

^{2}and 0.13. The method was successful in various types of SHB. Both simple and very complex SHBs were eliminated. Typical results are shown in Figure 7.

#### 4.3. Detection of Outliers

## 5. Discussion and Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Example of a Global Navigation Satellite System (GNSS) trace following a path and making a picnic activity during the displacement from a point A to a point B: (

**a**) Original trace; (

**b**) expected result of our approach.

**Figure 3.**Illustration of the relevance of the elongation criterion for the secondary human behaviour (SHB) approach: (

**a**) Computed polygons of a real SHB; (

**b**) computed polygons of roundtrip also resulting in a self-intersecting trace.

**Figure 4.**AngleMean calculation; ${\mathsf{\alpha}}_{\mathrm{i}}$ represents the angle between the segment starting at point i and the segment starting at point i − 1.

**Figure 5.**Examples of speed distributions: (

**a**) Skewed left and high kurtosis (K); (

**b**) normal and high K; (

**c**) skewed right and low K. One figure represents one single trace.

**Figure 11.**Outliers detection for high sinuosity paths: (

**a**) False-positive outliers (

**b**) and correctly detected non-outlier points.

Indicators | Description | Formula |
---|---|---|

AngleMean | Average value of 3 direction change (see Figure 4) | $\text{}\left({\mathsf{\alpha}}_{\mathrm{i}-1}+{\mathsf{\alpha}}_{\mathrm{i}}+{\mathsf{\alpha}}_{\mathrm{i}+1}\right)/3\text{}$ |

DistDiffN | Normalized distance | (${\mathrm{D}}_{\mathrm{i}-1}-{\mathrm{D}}_{\mathrm{i}}$)$/({\mathrm{D}}_{\mathrm{i}-1}+{\mathrm{D}}_{\mathrm{i}})\text{}$ |

DistDiffMed | Relation between distance and median distance of a trace | $\text{}\frac{{\mathrm{D}}_{\mathrm{i}-1}+{\mathrm{D}}_{\mathrm{i}}}{2\mathrm{Median}\left(\mathrm{Trace}\right)}\text{}$ |

DistMean | Mean distance | $\text{}({\mathrm{D}}_{\mathrm{i}-1}+{\mathrm{D}}_{\mathrm{i}})/\text{}$2 |

SpeedDiffN | Normalized speed | (${\mathrm{V}}_{\mathrm{i}-1}-{\mathrm{V}}_{\mathrm{i}}$)$/({\mathrm{V}}_{\mathrm{i}-1}+{\mathrm{V}}_{\mathrm{i}})\text{}$ |

SpeedMean | Mean speed | $\text{}({\mathrm{V}}_{\mathrm{i}-1}+{\mathrm{V}}_{\mathrm{i}})/\text{}$2 |

SpeedRate | Speed rate | $\text{}({\mathrm{V}}_{\mathrm{i}-1}+{\mathrm{V}}_{\mathrm{i}})/{\mathrm{V}}_{\mathrm{i}}\text{}$ |

DiffElev | Maximal height difference | max|${\mathrm{Z}}_{\mathrm{i}+1}$ − ${\mathrm{Z}}_{\mathrm{i}}$, ${\mathrm{Z}}_{\mathrm{i}}$ − ${\mathrm{Z}}_{\mathrm{i}-1}$| |

Indicators | Description | Formula |
---|---|---|

DiffElevDTM | Correlation between elevation (GNSS and DTM) | |ZDTM − ZGNSS| |

Slope | Gradient of line | tan(Ɵ), −90° < Ɵ < 90° |

Obstacles | Proximity of obstacles | true if close to obstacles, false otherwise |

Curvature | Convexity of slope | 1/R |

Vegetation | Type of forest | f (Landcover) |

CanopyCover | Point in the forest? | f (Landcover), boolean |

InBuildingWater | Point in building or water? | f (Topographic data), boolean |

Algorithm | Precision | Recall | F1 |
---|---|---|---|

PART | 0.67 | 0.78 | 0.72 |

OneR | 0.72 | 0.69 | 0.7 |

RIPPER | 0.79 | 0.79 | 0.79 |

M5Rules | 0.75 | 0.72 | 0.73 |

Rule Number | Description |
---|---|

Rule 1 | IF DistDiffMed >= 1.05 AND AngleMean >= 87.54 → outlier OR |

Rule 2 | IF AngleMean >= 71.25 AND SpeedRate >= 1.50 → outlier OR |

Rule 3 | IF AngleMean >= 74.80 AND DistDiffN <= 0.21→ outlier OR |

Rule 4 | IF AngleMean >= 83.15 AND SpeedRate <= 0.85→ outlier OR |

Rule 5 | IF AngleMean >= 56.43 AND DistMean >= 8847.31 → outlier |

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## Share and Cite

**MDPI and ACS Style**

Ivanovic, S.S.; Olteanu-Raimond, A.-M.; Mustière, S.; Devogele, T.
A Filtering-Based Approach for Improving Crowdsourced GNSS Traces in a Data Update Context. *ISPRS Int. J. Geo-Inf.* **2019**, *8*, 380.
https://doi.org/10.3390/ijgi8090380

**AMA Style**

Ivanovic SS, Olteanu-Raimond A-M, Mustière S, Devogele T.
A Filtering-Based Approach for Improving Crowdsourced GNSS Traces in a Data Update Context. *ISPRS International Journal of Geo-Information*. 2019; 8(9):380.
https://doi.org/10.3390/ijgi8090380

**Chicago/Turabian Style**

Ivanovic, Stefan S., Ana-Maria Olteanu-Raimond, Sébastien Mustière, and Thomas Devogele.
2019. "A Filtering-Based Approach for Improving Crowdsourced GNSS Traces in a Data Update Context" *ISPRS International Journal of Geo-Information* 8, no. 9: 380.
https://doi.org/10.3390/ijgi8090380