Pw_from_gps
GPS derived Precipitable Water Vapor (PWV) analysis tools with ML application: Flash Flood prediction in the Eastern Mediterranean area.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Stheno | 207 |  | 4 | 5 months ago | 38 | October 22, 2022 | 4 | mit | Python | |
| Gaussian process modelling in Python | ||||||||||
| Gpsmachinelearning | 96 | 7 years ago | 1 | Jupyter Notebook | ||||||
| Pilotguru | 51 | 4 years ago | 11 | C++ | ||||||
| Gather training data for training a self-driving car with just a smartphone. | ||||||||||
| Agrobot | 32 | a year ago | bsd-3-clause | Jupyter Notebook | ||||||
| Neural-Kalman GNSS/INS Navigation for Precision Agriculture | ||||||||||
| Predicting Transportation Modes Of Gps Trajectories | 24 | 4 years ago | apache-2.0 | Python | ||||||
| Understanding transportation mode from GPS (Global Positioning System) traces is an essential topic in the data mobility domain. In this paper, a framework is proposed to predict transportation modes. This framework follows a sequence of five steps: (i) data preparation, where GPS points are grouped in trajectory samples; (ii) point features generation; (iii) trajectory features extraction; (iv) noise removal; (v) normalization. We show that the extraction of the new point features: bearing rate, the rate of rate of change of the bearing rate and the global and local trajectory features, like medians and percentiles enables many classifiers to achieve high accuracy (96.5%) and f1 (96.3%) scores. We also show that the noise removal task affects the performance of all the models tested. Finally, the empirical tests where we compare this work against state-of-art transportation mode prediction strategies show that our framework is competitive and outperforms most of them. | ||||||||||
| Intelligent Vehicle Perception Based On Inertial Sensing And Artificial Intelligence | 18 | 3 years ago | n,ull | other | ||||||
| Intelligent Vehicle Perception Based on Inertial Sensing and Artificial Intelligence | ||||||||||
| Map Match | 14 | 6 years ago | 3 | Python | ||||||
| Implementation of a map matching algorithm called st-match | ||||||||||
| Minn | 10 | 3 years ago | 4 | Python | ||||||
| Manipal Indoor Navigation Network | ||||||||||
| Savesession Arkit Coreml | 7 | 2 years ago | apache-2.0 | Swift | ||||||
| A project to show the possibility to save and load session in ARkit using CoreML, the end goal is to make a guided tours app | ||||||||||
| Paris Traffic Prediction | 6 | 5 years ago | Python | |||||||
| Visualization and Prediction model of Paris traffic ( random Forests ) from data published by the OpenData-Paris project | ||||||||||
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