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I'm an engineer with a PhD in computer science applied to geography. To complete my PhD I worked both in a company (Thales TTS) and in academic labs (IGN, COGIT and MATIS). I have many centers of interest, in particular in computer science and in the open source community. My PhD work was about inverse procedural street modelling and massive point cloud management. My postdoc dealt with 19th century Paris maps. More precisely, I'm interested in reconstruction (from data-driven (geometric) to model-driven) and modelling, especially procedural/parametric/grammar models. I have also keen interests in remote sensing: images and extensively on Lidar (calibration, object recognition, management...). I recently passed a fine woodworking exam and have interest in digital fabrication.
May 2017 - present
- Creating a new formation for Oslandia from scratch: Open source tools for point clouds. Theory and context, main tools (CloudCompare, Meshlab, PCL, PDAL), experiential approach. Tailored for each client (needs, datasets, exercises).
August 2016 - February 2017
- PostDoc Position
- Historical (1800-) collaborative geocoding for Paris. Collaborative editing and knowledge extraction from historical map, fuzzy modeling of time and addresses, interactive web edit.
The latest developments in the field of digital humanities have increasingly enabled the construction of large data sets which can be easily accessed and used. These data sets often contain indirect spatial information, such as historical addresses. Historical geocoding is the process of transforming indirect spatial information into direct locatio...
Our modern world produces an increasing quantity of data, and especially geospatial data, with advance of sensing technologies, and growing complexity and organisation of vector data. Tools are needed to efficiently create and edit those vector geospatial data. Procedural generation has been a tool of choice to generate strongly organised data, yet...
Streets are large, diverse, and used for several (and possibly conflicting) transport modalities as well as social and cultural activities. Proper planning is essential and requires data. Manually fabricating data that represent streets (street reconstruction) is error-prone and time consuming. Automatising street reconstruction is a challenge beca...
Cities are structured by roads. Having up to date and detailed maps of these is thus an important challenge for urban planing, civil engineering and transportation. Those maps are traditionally created manually, which represents a massive amount of work, and may discard recent or temporary changes. For these reasons, automated map building has been...
World population is raising, especially the part of people living in cities. With increased population and complex roles regarding their inhabitants and their surroundings, cities concentrate difficulties for design, planning and analysis. These tasks require a way to reconstruct/model a city. Traditionally, much attention has been given to buildin...
Lidar datasets are becoming more and more common. They are appreciated for their precise 3D nature, and have a wide range of applications, such as surface reconstruction, object detection, visualisation, etc. For all this applications, having additional semantic information per point has potential of increasing the quality and the efficiency of the...
World urban population is growing fast, and so are cities, inducing an urgent need for city planning and management. Increasing amounts of data are required as cities are becoming larger, "Smarter", and as more related applications necessitate those data (planning, virtual tourism, traffic simulation, etc.). Data related to cities then become large...
World urban population is growing fast, and so are cities, inducing an urgent need for city planning and management. Increasing amounts of data are required as cities are becoming larger, "smarter", and as more related applications necessitate those data (planning, virtual tourism, traffic simulation, etc.). Data related to cities then become larg...
In addition to more traditional geographical data such as images (rasters) and vectors, point cloud data are becoming increasingly available. Such data are appreciated for their precision and true three-Dimensional (3D) nature. However, managing point clouds can be difficult due to scaling problems and specificities of this data type. Several metho...
We propose a new paradigm to effortlessly get a portable geometric Level Of Details (LOD) for a point cloud inside a Point Cloud Server. The point cloud is divided into groups of points (patch), then each patch is reordered (MidOc ordering) so that reading points following this order provides more and more details on the patch. This LOD have then m...
Streets are large, diverse, and used for conflicting transport modalities as well as social and cultural activities. Proper planning is essential and requires data. Manually fabricating data that represent streets (street reconstruction) is error-prone and time consuming. Automatising street reconstruction is a challenge because of the diversity, s...
In addition to the traditional Geographic Information System (GIS) data such as images and vectors, point cloud data has become more available. It is appreciated for its precision and true three-Dimensional (3D) nature. However, managing the point cloud can be difficult due to scaling problems and specificities of this data type. Several methods ex...
Our group gathers about twenty researchers around a project with two fundamental and distinct goals that we believe important to keep connected in order to: - study territorial evolutions at different scales or levels (from the cadastral parcel to the national territory), - build tools to accurately and flexibly answer such questions. http://geohistoricaldata.org/