R. Ravanelli

Exploiting performance of different low-cost sensors for small amplitude oscillatory motion monitoring: Preliminary comparisons in view of possible integration

We address the problem of low amplitude oscillatory motion detection through different low-cost sensors: a LIS3LV02DQ MEMS accelerometer, a Microsoft Kinect v2 range camera, and a uBlox 6 GPS receiver. Several tests were performed using a one-direction vibrating table with different oscillation frequencies (in the range 1.5–3 Hz) and small challenging amplitudes (0.02 m and 0.03 m). A Mikrotron EoSens high-resolution camera was used to give reference data. A dedicated software tool was developed to retrieve Kinect v2 results. The capabilities of the VADASE algorithm were employed to process uBlox 6 GPS receiver observations. In the investigated time interval (in the order of tens of seconds) the results obtained indicate that displacements were detected with the resolution of fractions of millimeters with MEMS accelerometer and Kinect v2 and few millimeters with uBlox 6. MEMS accelerometer displays the lowest noise but a significant bias, whereas Kinect v2 and uBlox 6 appear more stable. The results suggest the possibility of sensor integration both for indoor (MEMS accelerometer

Monitoring the Impact of Land Cover Change on Surface Urban Heat Island through Google Earth Engine: Proposal of a Global Methodology, First Applications and Problems

All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development G ...

Implementation and assessment of two density-based outlier detection methods over large spatial point clouds

Several technologies provide datasets consisting of a large number of spatial points, commonly referred to as point-clouds. These point datasets provide spatial information regarding the phenomenon that is to be investigated, adding value through knowledge of forms and spatial relationships. Accurate methods for automatic outlier detection is a key step. In this note we use a completely open-source workflow to assess two outlier detection methods, statistical outlier removal (SOR) filter and local outlier factor (LOF) filter. The latter was implemented ex-novo for this work using the Point Cloud Library (PCL) environment. Source code is available in a GitHub repository for inclusion in PCL builds.Two very different spatial point datasets are used for accuracy assessment. One is obtained from dense image matching of a photogrammetric survey (SfM) and the other from floating car data (FCD) coming from a smart-city mobility framework providing a position every second of two public transportation bus tracks.Outliers were simulated in the SfM dataset, and manually detected and selected in the FCD dataset. Simulation in SfM was carried out in order to create a controlled set with two classes of outliers: clustered points (up to 30 points per cluster) and isolated points, in both cases at random distances from the other points. Optimal number of nearest neighbours (KNN) and optimal thresholds of SOR and LOF values were defined using area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Absolute differences from median values of LOF and SOR (defined as LOF2 and SOR2) were also tested as metrics for detecting outliers, and optimal thresholds defined through AUC of ROC curves.Results show a strong dependency on the point distribution in the dataset and in the local density fluctuations. In SfM dataset the LOF2 and SOR2 methods performed best, with an optimal KNN value of 60; LOF2 approach gave a slightly better result if considering clustered outliers (true positive rate: LOF2 = 59.7% SOR2 = 53%). For FCD, SOR with low KNN values performed better for one of the two bus tracks, and LOF with high KNN values for the other; these differences are due to very different local point density. We conclude that choice of outlier detection algorithm very much depends on characteristic of the dataset’s point distribution, no one-solution-fits-all. Conclusions provide some information of what characteristics of the datasets can help to choose the optimal method and KNN values.