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Apprentissage automatique pour la détection d'anomalies dans les données ouvertes : application à la cartographie

Abstract : In this thesis we study the problem of anomaly detection in the open data used by the Qucit company, both the business data of its customers, as well as those allowing to contextualize them.We are looking for data that reflects an anomaly in reality. Initially, we were interested in detecting defective bicycles in the trip data of New York’s bike share system. Characteristics describing the behaviour of each observed bicycle are clustered. Abnormal behaviors are extracted from this clustering and compared to monthly reports indicating the number of bikes repaired; this is an aggregate learning problem. The results of this first work were unsatisfactory due to the paucity of data. This first part of the work then gave way to a problem focused on the detection of buildings within satellite images. We are looking for anomalies in the geographical data that do not reflect reality. We propose a method of merging segmentation models that improves the error metric by up to +7% over the standard method. We assess the robustness of our model to the removal of buildings from labels to determine the extent to which omissions are likely to alter the results. This type of noise is commonly encountered within the OpenStreetMap data, regularly used by Qucit, and the robustness observed indicates that it could be corrected.
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Submitted on : Tuesday, April 16, 2019 - 11:15:18 AM
Last modification on : Saturday, June 25, 2022 - 10:38:59 AM


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  • HAL Id : tel-02100741, version 1



Rémi Delassus. Apprentissage automatique pour la détection d'anomalies dans les données ouvertes : application à la cartographie. Traitement des images [eess.IV]. Université de Bordeaux, 2018. Français. ⟨NNT : 2018BORD0230⟩. ⟨tel-02100741⟩



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