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PEOPLE@HES-SO – Annuaire et Répertoire des compétences
PEOPLE@HES-SO – Annuaire et Répertoire des compétences

PEOPLE@HES-SO
Annuaire et Répertoire des compétences

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Gressin Adrien

Gressin Adrien

Professeur HES associé

Compétences principales

Photogrammétrie

Télédétection

LiDAR

Remote sensing applications

Machine learning / Deep learning

Computer Vision

  • Contact

  • Enseignement

  • Recherche

  • Publications

  • Conférences

Contrat principal

Professeur HES associé

Téléphone: +41 24 557 63 79

Bureau: D03

Haute école d'Ingénierie et de Gestion du Canton de Vaud
Route de Cheseaux 1, 1400 Yverdon-les-Bains, CH
HEIG-VD
Institut
insit - Institut d’ingénierie du territoire
MSc UNIGE/HES-SO en Développement territorial - HES-SO Master
  • Photogrammétrie
BSc HES-SO en Géomatique - Haute école d'Ingénierie et de Gestion du Canton de Vaud
  • Photogrammétrie
  • Télédétection

En cours

Relevé Automatique de Réseaux

Rôle: Requérant(e) principal(e)

Description du projet :

Le projet RAR (Relevé automatique des réseaux pour la mise à jour du cadastre souterrain) a pour objectif la mise à jour rapide et à moindre coût des réseaux d’eau potable, de gaz… par relevé lors des fouilles permettant d’avoir accès aux éléments des réseaux. Il s’intéresse plus particulièrement à l’automatisation de la reconnaissance d’objets, grâce à des techniques d’apprentissage automatique (apprentissage profond).

Equipe de recherche au sein de la HES-SO: Gressin Adrien

Durée du projet: - 08.07.2021

Statut: En cours

IQS - Morges

Rôle: Requérant(e) principal(e)

Description du projet :

La région de Morges fait face à une imperméabilisation du sol, liée à la densification du tissu urbain. Elle a ainsi décidé de mettre en place un outil simple et efficace pour prendre en compte la qualité des sols dans les processus d’aménagement et ainsi garantir une gestion des sols aussi durable que possible pour suivre les recommandations du PNR68. Pour réaliser un prototype de cet outil, la région Morges a lancé un projet regroupant différents partenaires, avec d’une part la HEIA-FR et HEIPA pour l’expertise pédologique, et d’autre part la HEIG-VD pour la partie géomatique.

Partant du constat que la mise en place d’un indice de qualité pédologique nécessite de disposer d’une carte de qualité des sols et que les données pédologiques sont insuffisantes, les partenaires du projet ont proposé de construire un modèle à partir de l’ensemble des données et technologies disponibles en matière de géomatique et télédétection.

Equipe de recherche au sein de la HES-SO: Gressin Adrien , Boivin Pascal , Favre Boivin Fabienne

Statut: En cours

2025

Lexique de la photogrammétrie
Chapitre de livre ArODES

Emmanuel Cledat, Jean-François Hangouët, Arnaud Le Bris, Marc Poupée, Adrien Gressin, Bertrand Cannelle, Sylvie Daniel, Christian Larouche, Elisabeth Simonetto, Maxime Seguin

Lexique de l'Association francophone de topographie  (50 p.). 2025,  Saint-Mandé, France : Association francophone de topographie (AFP) Saint-Mandé, France : Société Française de Photogrammétrie et de Télédétection

Lien vers la publication

2024

Sphaeroptica :
Article scientifique ArODES
a tool for pseudo-3D visualization and 3D measurements on arthropods

Aurore Mathys, Yann Pollet, Adrien Gressin, Xavier Muth, Jonathan Brecko, Wouter Dekoninck, Didier Vandenspiegel, Sébastien Jodogne, Patrick Semal

PLOS ONE,  2024, 19, 10, e0311887

Lien vers la publication

Résumé:

Natural history collections are invaluable reference collections. Digitizing these collections is a transformative process that improves the accessibility, preservation, and exploitation of specimens and associated data in the long term. Arthropods make up the majority of zoological collections. However, arthropods are small, have detailed color textures and share small, complex and shiny structures, which poses a challenge to conventional digitization methods. Sphaeroptica is a multi-images viewer that uses a sphere of oriented images. It allows the visualization of insects including their tiniest features, the positioning of landmarks, and the extraction of 3D coordinates for measuring linear distances or for use in geometric morphometrics analysis. The quantitative comparisons show that the measures obtained with Sphaeroptica are similar to the measurements derived from 3D μCT models with an average difference inferior to 1%, while featuring the high resolution of color stacked pictures with all details like setae, chaetae, scales, and other small and/or complex structures. Shaeroptica was developed for the digitization of small arthropods but it can be used with any sphere of aligned images resulting from the digitization of objects or specimens with complex surface and shining, black, or translucent texture which cannot easily be digitized using structured light scanner or Structure-from-Motion (SfM) photogrammetry.

2023

Solar potential on facades at urban scale :
Article scientifique ArODES
an integrated approach combining solar and digital building modelling

Gilles Desthieux, Adrien Gressin, Blaise Raybaud, Jens Ingensand

Journal of Physics: Conference Series ; Proceedings of CISBAT2023, the built environment in transititon, Hybrid International Scientific Conference,  2600, 4, 042004

Lien vers la publication

Résumé:

The paper presents an integrated approach to improve the solar radiation modelling on facades in large-scale built-up areas. The modelling of the built environment must first be improved in terms of level of detail. Thus, from aerial oblique images, a digital twin of the urban scene is created, allowing to process the facades as textured objects and to detect windows using an artificial intelligence image processing. Reflected radiation is significant on vertical surfaces, but complex to model on large areas. The developed model is based on a simplified radiosity approach, which reduces the volume of analysis, storage and thus the computation time, while producing reliable results. A demonstration of the integrated tool is presented for an urban area in Geneva (1 km2), including a solar energy balance assessment for one of the buildings using the results from the solar modelling. The perspective is to generalise the approach to a larger scale and to complete the solar cadastre of the roofs of the Greater Geneva area with the facades.

2024

Filling the gap between using AI and updating an underground network VECTOR database :
Conférence ArODES
skeleton extraction from classified point clouds

Antoine Carreaud, Fabio Mariani, Adrien Gressin

Proceedings of the IGARSS 2024 - IEEE International Geoscience and Remote Sensing Symposium

Lien vers la conférence

Résumé:

This scientific study addresses the challenge of converting labeled 3D models into vector representations enriched with topology and attributes, which is a crucial step for updating vector databases such as underground networks. Relying on Convolutional Neural Networks for detection, the proposed skeletonization method employs a processing pipeline involving object separation, barycenter computation for point-like objects, and cylinder fitting for linear objects. The method ensures an accurate representation of the 3D model in vector format (points, lines, and polylines) with topology and attributes. Experimental validation, conducted on 200 excavation sites, attests to an accuracy of 85 % within a 10 cm threshold. Despite the limitations of partially recognized neighbors, the method was considered very satisfactory for updating a real underground network database in Geneva (Switzerland).

2023

Matching fragmented lithic archaeological artefacts
Conférence

Muth Xavier, Adrien Bosson, Gressin Adrien

21st Swiss Geoscience Meeting, 17.11.2023 - 18.11.2023, Mendrisio

Automating image labeling for remote sensing using cadastral database and video game engine simulation
Conférence ArODES

Antoine Carreaud, Yves Deillon, Adrien Gressin

Proceedings of the IGARSS 2023

Lien vers la conférence

Résumé:

In the remote sensing field, utilization of deep learning algorithms, such as Convolutional Neural Networks (CNNs) for automated detection is a commonly adopted approach, as reported by [1]. These techniques have demonstrated significant power and efficacy, largely due to the availability of increasingly large datasets and the rapid advancement in computing technology. However, the preparation of these datasets necessitates a substantial amount of manual labor, which is often outsourced to cost-efficient labor forces. In this paper, we present two methods developed to automate the labeling work for semantic segmentation and object detection tasks. We will analyze the results in terms of accuracy and time saved, and show how we've successfully applied them to two real-life projects.

CIMEMountainBot :
Conférence ArODES
a telegram bot to collect mountain images and to communicate information with mountain guides

Maryam Lotfian, Jens Ingensand, Adrien Gressin, Christophe Claramunt

Proceedings of the International Symposium on Web and Wireless Geographical Information Systems W2GIS 2023

Lien vers la conférence

Résumé:

Advancements in technology have led to an increase in the number of Volunteer Geographic Information (VGI) applications, and new smartphone functionalities have made collecting VGI data easier. However, getting volunteers to install and use new VGI applications can be challenging. This article introduces a possible solution by using existing applications, that people use on a daily basis, for VGI data collection. Accordingly, a prototype of a Telegram chatbot is developed to collect mountain images from volunteers, while also providing them with information such as weather conditions and avalanche risk in a given location. The article concludes that using existing platforms like Telegram has benefits, but it is important to consider the specific goals, participants’ needs, and interface of a project, and strikes a balance between creating a new application and using existing ones.

Geolocation of a panoramic camera by reference pairing
Conférence ArODES

Chahine-Nicolas Zede, Adrien Gressin

Proceedings of the 2023 Joint Urban Remote Sensing Event (JURSE)

Lien vers la conférence

Résumé:

Panoramic cameras are now available to a large audience. They provide good results on photogrammetry application, but they are still limited by their positioning. This project aims to geolocate a commercial 360° camera in an urban environment, by extracting points in fisheye images and match them with reference from a LiDAR (Light Detection and Ranging) dataset. Such reference points are located on the horizon line, visible from the camera point of view. Matching points are then introduced as Ground Control Points to improve the camera positioning accuracy. A fully automatic solution for position refinement, based on LiDAR data is proposed in this paper.

2022

Automating the underground cadastral survey :
Conférence ArODES
a processing chain proposal

Antoine Carreaud, Fabio Mariani, Adrien Gressin

Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Lien vers la conférence

Résumé:

In order to ensure the proper functioning and evolution of underground networks (water, gas, etc.) over time, municipal services need to maintain accurate and up-to-date maps. Such maps are generally updated using traditional data acquisition methods (total station or GNSS), which are time-consuming, expensive, and require several teams of surveyors in the field. In this context, an important topic of research is the automation of the updating of the underground cadastre in order to save time, money, and human effort. In this paper, we present a new method that we developed ranging from the choice of the acquisition system, the tests carried out in the field to the detection of objects and the automatic segmentation in a 3D point cloud. We have chosen to use a convolutional neural network on images for the detection of objects that are part of the underground cadastre. As the next step, objects are projected to obtain a 3D point cloud segmented based on the object type. The vectorization step is still under development so that objects can be converted to vector format and therefore be used for updating the cadastre. The results based on excavation sites with well-represented objects in our training database are excellent, approaching 96% accuracy. However, the detection of rare objects is much less good and thus remains a topic for future research. Overall, the complete processing chain allowing to automate as much as possible the update of an underground cadastre is presented in this paper.

Toward a low-cost, multispectral, high accuracy mapping system for vineyard inspection
Conférence ArODES

Sami Beniaouf, Romain Mabillard, Adrien Gressin

Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Lien vers la conférence

Résumé:

Confronted with the climate change challenge and the territorial constraints, agriculture has to modernize itself. The use of georeferenced data and remote-sensing imagery is a major step in this direction. This precision mapping of crops requires powerful and accurate acquisition systems, while remaining financially attractive. The development of multispectral sensors and low-cost GNSS makes it possible to consider systems that will be able to map at the plant scale. However, these positioning systems do not yet guarantee a precise overlap of data acquired at different times. Thus, we propose in this paper a method to register terrestrial image data, acquired on vineyard plots. Our method seeks to avoid image registration problems, such as illumination changes, by detecting the vine stocks, reconstructing them in 3D, and registering them individually. The 3D detection method is based on an image-based object detection method (Faster R-CNN) and a structure-from-motion reconstruction of object-masked images. The results that we obtained on a vineyard plot, allowed us to validate the method, with a precision of less than 10 cm, making it possible to map the vine by stock.

2021

Fast image labelling using 3D reconstruction
Conférence ArODES

Antoine Carreaud, Krysztof Lis, Adrien Gressin

Proceedings of Symposium 19 : Geoscience and Geoinformation Remote Sensing, Swiss Geoscience Meeting

Lien vers la conférence

Semi-automated soil quality monitoring for land management and decision makers
Conférence

Magalie Matteodo, Gondret Karine, Bullinger Géraldine, Favre Boivin Fabienne, Gressin Adrien, Boivin Pascal

Eurosoil 2021, 23.08.2021 - 27.08.2021, Genève

2020

About photogrammetric uav-mapping :
Conférence ArODES
which accuracy for which application?

Adrien Gressin, Julien Vallet, Maximin Bron

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; Proceedings of International Society for Photogrammetry and Remote Sensing (ISPRS), 31 August - 2 September 2020, Online

Lien vers la conférence

Résumé:

UAV surveys have become more and more popular over the last few years, driven by manufacturers and software suppliers who promise high accuracy at low cost. But, what are the real possibilities offered by this kind of sensor? In this article, we investigate in detail the possibilities offered by photogrammetric UAV mapping solutions through numerous practical experiments and compare them to a reference high grade LiDAR-Photogrammetric acquisition. This paper first focuses on aerial triangulation and dense matching accuracy comparison of different data acquisition units (2 types of camera) and processing softwares (1 open source and 2 proprietary softwares). Finally, the opportunities offered by these different approaches are studied in detail on standard aerial applications such as power lines detection, forest and urban areas mapping, in comparison with our reference dataset.

Airborne and mobile lidar :
Conférence ArODES
which sensors for which application ?

Julien Vallet, Adrien Gressin, Philipp Clausen, Jan Skaloud

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; Proceedings of International Society for Photogrammetry and Remote Sensing (ISPRS), 31 August - 2 September 2020, Online

Lien vers la conférence

Résumé:

The UAV mapping industry expanded tremendously during the last five years. Thanks to miniaturization, automation and advertising, this technology may give a wrong impression that mapping of certain quality is as simple as clicking few buttons on a PC. Moreover, with a large and continuously increasing offer of hardware and software, the identification of the right tools is not easy, especially when aiming at certain standard. In this respect, the mapping with LiDAR is more delicate than with a camera due to a lower level of redundancy within the process of orientation/georeferencing and somewhat higher threshold on the size/weight per performance ratio within these sensors. This fact motivated us to present a practical benchmark evaluating a popular small LiDAR sensor in realistic conditions for intrinsic parameters such as noise or capacity to penetrate canopy, as well as the “low-weight” inertial technology in terms of geometrical influences on the resulting point cloud. The practical limitations are indeed considerably lower than those specified by the manufacturers or tested in laboratory conditions. These should be considered together with other “mapping-productivity” factors that are summarized in the last part of this study.

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