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8 results on '"Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG)"'

1. OSMWatchman: Learning How to Detect Vandalized Contributions in OSM Using a Random Forest Classifier

Author

Quy Thy Truong, Guillaume Touya, Cyril de Runz, Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), Laboratoire Instrumentation, Simulation et Informatique Scientifique (IFSTTAR/COSYS/LISIS), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Communauté Université Paris-Est, Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC), Université de Reims Champagne-Ardenne (URCA), Bases de données et traitement des langues naturelles (BDTLN), Laboratoire d'Informatique Fondamentale et Appliquée de Tours (LIFAT), Centre National de la Recherche Scientifique (CNRS)-Université de Tours-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université de Tours-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Université de Tours (UT)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université de Tours (UT)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
vandalism, quality, volunteered geographic information, lcsh:G1-922, [INFO]Computer Science [cs], supervised machine learning, OpenStreetMap, VGI, lcsh:Geography (General), ComputingMilieux_MISCELLANEOUS, random forest
Abstract

Though Volunteered Geographic Information (VGI) has the advantage of providing free open spatial data, it is prone to vandalism, which may heavily decrease the quality of these data. Therefore, detecting vandalism in VGI may constitute a first way of assessing the data in order to improve their quality. This article explores the ability of supervised machine learning approaches to detect vandalism in OpenStreetMap (OSM) in an automated way. For this purpose, our work includes the construction of a corpus of vandalism data, given that no OSM vandalism corpus is available so far. Then, we investigate the ability of random forest methods to detect vandalism on the created corpus. Experimental results show that random forest classifiers perform well in detecting vandalism in the same geographical regions that were used for training the model and has more issues with vandalism detection in &ldquo, unfamiliar regions&rdquo

Published
2020
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2. Ship Identification and Characterization in Sentinel-1 SAR Images with Multi-Task Deep Learning

Author

Sébastien Lefèvre, Clément Dechesne, Ronan Fablet, Rodolphe Vadaine, Guillaume Hajduch, Méthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution (MATIS), Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN)-École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), Environment observation with complex imagery (OBELIX), SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Collecte Localisation Satellites (CLS), Lab-STICC_IMTA_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département Signal et Communications (IMT Atlantique - SC), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Université de Bretagne Sud (UBS)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), IMT Atlantique (IMT Atlantique), Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES)-CentraleSupélec-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-CentraleSupélec

Subjects
Ship identification, 010504 meteorology & atmospheric sciences, Automatic Identification System, Computer science, 0211 other engineering and technologies, 02 engineering and technology, Deep neural network, computer.software_genre, 01 natural sciences, law.invention, Task (project management), law, Sentinel-1 SAR images, 14. Life underwater, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Ship characterization, business.industry, Deep learning, deep neural network, ship identification, ship characterization, multi-task learning, Identification (information), General Earth and Planetary Sciences, Artificial intelligence, Data mining, Scale (map), business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, computer
Abstract

International audience; The monitoring and surveillance of maritime activities are critical issues in both military and civilian fields, including among others fisheries monitoring, maritime traffic surveillance, coastal and at-sea safety operations, tactical situations. In operational contexts, ship detection and identification is traditionally performed by a human observer who identifies all kinds of ships from a visual analysis of remotely-sensed images. Such a task is very time consuming and cannot be conducted at a very large scale, while Sentinel-1 SAR data now provide a regular and worldwide coverage. Meanwhile, with the emergence of GPUs, deep learning methods are now established as state-of-the-art solutions for computer vision, replacing human intervention in many contexts. They have been shown to be adapted for ship detection, most often with very high resolution SAR or optical imagery. In this paper, we go one step further and investigate a deep neural network for the joint classification and characterization of ships from SAR Sentinel-1 data. We benefit from the synergies between AIS (Automatic Identification System) and Sentinel-1 data to build significant training datasets. We design 12 a multi-task neural network architecture composed of one joint convolutional network connected to three task-specific networks, namely for ship detection, classification and length estimation. The experimental assessment showed our network provides promising results, with accurate classification and length performance (classification overall accuracy: 97.25%, mean length error: 4.65 m ± 8.55 m).

Published
2019
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3. Historical Collaborative Geocoding

Author

Bertrand Duménieu, Maurizio Gribaudi, Nathalie Abadie, Benoit Costes, Julien Perret, Rémi Cura, Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), Centre de Recherches Historiques (CRH), École des hautes études en sciences sociales (EHESS)-Centre National de la Recherche Scientifique (CNRS), and Institut National de l'Information Géographique et Forestière [IGN] (IGN)

Subjects
FOS: Computer and information sciences, Matching (statistics), 060106 history of social sciences, Computer science, [SHS.INFO]Humanities and Social Sciences/Library and information sciences, Geography, Planning and Development, 0211 other engineering and technologies, lcsh:G1-922, 02 engineering and technology, Computer Science - Computers and Society, Computer Science - Databases, Computers and Society (cs.CY), citizen science, Earth and Planetary Sciences (miscellaneous), Digital Libraries (cs.DL), 0601 history and archaeology, Computers in Earth Sciences, Spatial analysis, database, collaborative, 021101 geological & geomatics engineering, Hierarchy, Thesaurus (information retrieval), Information retrieval, String (computer science), Level of detail (writing), historical dataset, Databases (cs.DB), Computer Science - Digital Libraries, 06 humanities and the arts, geohistorical objects, GIS, Field (geography), geocoding, localisation, Geocoding, history, User interface, digital humanities, [SHS.HIST]Humanities and Social Sciences/History, lcsh:Geography (General), crowd-sourced
Abstract

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 locations which can be placed on a map, thus allowing for spatial analysis and cross-referencing. There are many geocoders that work efficiently for current addresses. However, these do not tackle temporal information, and usually follow a strict hierarchy (country, city, street, house number, etc.) which is difficult&mdash, if not impossible&mdash, to use with historical data. Historical data is filled with uncertainty (pertaining to temporal, textual, and positional accuracy, as well as to the reliability of historical sources) which can neither be ignored nor entirely resolved. Our open source, open data, and extensible solution for geocoding is based on extracting a large number of simple gazetteers composed of geohistorical objects, from historical maps. Geocoding a historical address becomes the process of finding one or several geohistorical objects in the gazetteers which best match the historical address searched by the user. The matching criteria are customisable, weighted, and include several dimensions (fuzzy string, fuzzy temporal, level of detail, positional accuracy). Since our goal is to facilitate historical work, we also put forward web-based user interfaces which help geocode (one address or batch mode) and display results over current or historical maps. Geocoded results can then be checked and edited collaboratively (no source is modified). The system was tested on the city of Paris, France, for the 19th and 20th centuries. It showed high response rates and worked quickly enough to be used interactively.

Published
2018
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4. Effects of Using Different Sources of Remote Sensing and Geographic Information System Data on Urban Stormwater 2D–1D Modeling

Author

Ghassan Chebbo, Bahman Soheilian, Yi Hong, Céline Bonhomme, laboratoire Eau Environnement et Systèmes Urbains (LEESU), AgroParisTech-École des Ponts ParisTech (ENPC)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Méthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution (MATIS), Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN)-École nationale des sciences géographiques (ENSG), and Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN)

Subjects
Geographic information system, Water flow, 0208 environmental biotechnology, Stormwater, 02 engineering and technology, urban landuse, lcsh:Technology, urban stormwater modeling, lcsh:Chemistry, [SPI]Engineering Sciences [physics], TRENOE platform, General Materials Science, 2D–1D coupled modeling, multiple data sources, high-resolution LiDAR data, lcsh:QH301-705.5, Instrumentation, ComputingMilieux_MISCELLANEOUS, Remote sensing, Fluid Flow and Transfer Processes, Data collection, lcsh:T, business.industry, Process Chemistry and Technology, General Engineering, lcsh:QC1-999, 6. Clean water, 020801 environmental engineering, Computer Science Applications, Network planning and design, Lidar, lcsh:Biology (General), lcsh:QD1-999, lcsh:TA1-2040, 13. Climate action, Remote sensing (archaeology), [SDE]Environmental Sciences, Environmental science, lcsh:Engineering (General). Civil engineering (General), business, Surface runoff, lcsh:Physics
Abstract

International audience; Remote sensing (RS) and geographic information system (GIS) data is increasingly used in urban stormwater modeling. The undirected use of such data may waste economic and human resources. In order to provide guidance for practitioners to efficiently use different data collection resources, as well as give a reference for future works, this paper aims to assess the effects of using free access GIS data and ad hoc RS data on urban 2D–1D stormwater modeling. The 2D-surface Two-dimensional Runoff, Erosion, and Export model (TREX) model was published in Science of the Total Environment in 2008. The 1D-sewer CANOE (Logiciel intégré de conception et de diagnostic des réseaux d’assainissement) model was published in Journal of Hydrology in 2004. The two models are integrated in the TRENOE (TREX-CANOE) platform. The modeling approach is applied to a small urban catchment near Paris (Le Perreux sur Marne, 0.12 km2). Simulation results reveal that the detailed land-use information derived from multiple data sources is a crucial factor for accurate simulations. Nevertheless, using the very high resolution LiDAR (light detection and ranging) data is not equally significant for the water flow simulations at sewage outlets. Finally, we suggest that using the free access GIS data accompanying the urban sewer network design might be an acceptable low-cost solution for accurate urban 2D–1D stormwater modeling during moderate rainfall events. Further studies of urban stormwater modeling could focus on the development of “suitable” models with “enough” input data, depending on the management/research objectives.

Published
2017
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5. Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory

Author

Sassan Saatchi, Paula Soares, Antonio Ferraz, Margarida Tomé, Luísa Pereira, Clément Mallet, Carlos A. Silva, Stéphane Jacquemoud, Gil Gonçalves, California Institute of Technology (CALTECH), Institut de Physique du Globe de Paris (IPGP), Centre National de la Recherche Scientifique (CNRS)-Université de La Réunion (UR)-Université Paris Diderot - Paris 7 (UPD7)-IPG PARIS-Institut national des sciences de l'Univers (INSU - CNRS), Université de Montréal (UdeM), Forest Research Centre, School of Agriculture, University of Lisbon, Archaeogenetics Laboratory, Méthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution (MATIS), Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN)-École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Universidade de Coimbra [Coimbra], University of Idaho [Moscow, USA], Universidade de Lisboa (ULISBOA), and Universidade de Aveiro

Subjects
Synthetic aperture radar, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, Tree allometry, airborne laser scanning, 02 engineering and technology, vegetation cover, 01 natural sciences, multi-layered forest structure, Space research, ComputingMilieux_MISCELLANEOUS, lidar, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Biomass (ecology), biomass, [SDE.IE]Environmental Sciences/Environmental Engineering, carbon, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 15. Life on land, Field (geography), Lidar, individual tree extraction, Remote sensing (archaeology), [SDE]Environmental Sciences, General Earth and Planetary Sciences, Environmental science, Spatial variability, 3D point cloud clustering, crown delineation
Abstract

International audience; The scientific community involved in the UN-REDD program is still reporting large uncertainties about the amount and spatial variability of CO 2 stored in forests. The main limitation has been the lack of field samplings over space and time needed to calibrate and convert remote sensing measurements into aboveground biomass (AGB). As an alternative to costly field inventories, we examine the reliability of state-of-the-art lidar methods to provide direct retrieval of many forest metrics that are commonly collected through field sampling techniques (e.g., tree density, individual tree height, crown cover). AGB is estimated using existing allometric equations that are fed by lidar-derived metrics at either the individual tree-or forest layer-level (for the overstory or underneath layers, respectively). Results over 40 plots of a multilayered forest located in northwest Portugal show that the lidar method provides AGB estimates with a relatively small random error (RMSE = of 17.1%) and bias (of 4.6%). It provides local AGB baselines that meet the requirements in terms of accuracy to calibrate satellite remote sensing measurements (e.g., the upcoming lidar GEDI (Global Ecosystem Dynamics Investigation), and the Synthetic Aperture Radar (SAR) missions NISAR (National Aeronautics and Space Administration and Indian Space Research Organization SAR) and BIOMASS from the European Space Agency, ESA) for AGB mapping purposes. The development of similar techniques over a variety of forest types would be a significant improvement in quantifying CO 2 stocks and changes to comply with the UN-REDD policies.

Published
2016
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6. Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information

Author

Jacinto Estima, Linda See, Giles M. Foody, Steffen Fritz, Grega Milčinski, Matej Nikšič, Ana-Maria Olteanu-Raimond, Bin Jiang, Norman Kerle, Alexis Comber, Hai-Ying Liu, Mari Laakso, Martin Rutzinger, Peter Mooney, Andrea Pődör, Marco Painho, Lucy Bastin, Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), NOVA Information Management School (NOVA IMS), Information Management Research Center (MagIC) - NOVA Information Management School, NOVA IMS Research and Development Center (MagIC), Department of Earth Systems Analysis, Faculty of Geo-Information Science and Earth Observation, UT-I-ITC-4DEarth, Department of Earth Observation Science, National Land Survey of Finland, and Maanmittauslaitos

Subjects
Volunteered geographic information, 010504 meteorology & atmospheric sciences, Geography, Planning and Development, 0211 other engineering and technologies, lcsh:G1-922, 02 engineering and technology, Citizen science, Key issues, Crowdsourcing, 01 natural sciences, [SHS]Humanities and Social Sciences, Political science, citizen science, Realm, Earth and Planetary Sciences (miscellaneous), [INFO]Computer Science [cs], crowdsourcing, volunteered geographic information, mapping, Computers in Earth Sciences, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, business.industry, Data science, Incentive, Thematic map, Mapping, ITC-ISI-JOURNAL-ARTICLE, The Internet, business, ITC-GOLD, lcsh:Geography (General)
Abstract

See, L., Mooney, P., Foody, G., Bastin, L., Comber, A., Estima, J., ... Rutzinger, M. (2016). Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information. ISPRS International Journal of Geo-Information, 5(5), [55]. https://doi.org/10.3390/ijgi5050055 Citizens are increasingly becoming an important source of geographic information, sometimes entering domains that had until recently been the exclusive realm of authoritative agencies. This activity has a very diverse character as it can, amongst other things, be active or passive, involve spatial or aspatial data and the data provided can be variable in terms of key attributes such as format, description and quality. Unsurprisingly, therefore, there are a variety of terms used to describe data arising from citizens. In this article, the expressions used to describe citizen sensing of geographic information are reviewed and their use over time explored, prior to categorizing them and highlighting key issues in the current state of the subject. The latter involved a review of 100 Internet sites with particular focus on their thematic topic, the nature of the data and issues such as incentives for contributors. This review suggests that most sites involve active rather than passive contribution, with citizens typically motivated by the desire to aid a worthy cause, often receiving little training. As such, this article provides a snapshot of the role of citizens in crowdsourcing geographic information and a guide to the current status of this rapidly emerging and evolving subject. publishersversion published

Published
2016
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7. Sinusoidal Wave Estimation Using Photogrammetry and Short Video Sequences

Author

Ewelina Rupnik, Josef Jansa, Norbert Pfeifer, Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), Vienna University of Technology (TU Wien), Dept Geodesy & Geoinformat, and Vienna University of Technology

Subjects
Surface (mathematics), sinusoidal wave, Computer science, Acoustics, water, Phase (waves), Bundle adjustment, lcsh:Chemical technology, System of linear equations, photogrammetry, video, Biochemistry, Least squares, Article, Analytical Chemistry, 3D modeling, Sine wave, Parametric surface, lcsh:TP1-1185, Computer vision, specular, Specular reflection, Electrical and Electronic Engineering, Dispersion (water waves), Instrumentation, ComputingMilieux_MISCELLANEOUS, dynamic, business.industry, Atomic and Molecular Physics, and Optics, non-rigid, bundle adjustment, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Reflection (physics), Artificial intelligence, business
Abstract

The objective of the work is to model the shape of the sinusoidal shape of regular water waves generated in a laboratory flume. The waves are traveling in time and render a smooth surface, with no white caps or foam. Two methods are proposed, treating the water as a diffuse and specular surface, respectively. In either case, the water is presumed to take the shape of a traveling sine wave, reducing the task of the 3D reconstruction to resolve the wave parameters. The first conceived method performs the modeling part purely in 3D space. Having triangulated the points in a separate phase via bundle adjustment, a sine wave is fitted into the data in a least squares manner. The second method presents a more complete approach for the entire calculation workflow beginning in the image space. The water is perceived as a specular surface, and the traveling specularities are the only observations visible to the cameras, observations that are notably single image. The depth ambiguity is removed given additional constraints encoded within the law of reflection and the modeled parametric surface. The observation and constraint equations compose a single system of equations that is solved with the method of least squares adjustment. The devised approaches are validated against the data coming from a capacitive level sensor and on physical targets floating on the surface. The outcomes agree to a high degree.

Published
2015
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8. A Modular Modelling Framework for Hypotheses Testing in the Simulation of Urbanisation

Author

Paul Chapron, Sébastien Rey-Coyrehourcq, Clémentine Cottineau, Romain Reuillon, Denise Pumain, Centre for Advanced Spatial Analysis (CASA), University College of London [London] (UCL), Institut des Systèmes Complexes - Paris Ile-de-France (ISC-PIF), École normale supérieure - Cachan (ENS Cachan)-Université Paris 1 Panthéon-Sorbonne (UP1)-Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut Curie [Paris]-École polytechnique (X), Laboratoire des Sciences et Technologies de l'Information Géographique (LaSTIG), École nationale des sciences géographiques (ENSG), Institut National de l'Information Géographique et Forestière [IGN] (IGN)-Institut National de l'Information Géographique et Forestière [IGN] (IGN), Identités et Différenciation de l'Environnement des Espaces et des Sociétés (IDEES), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU)-Institut de Recherche Interdisciplinaire Homme et Société (IRIHS), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS), Géographie-cités (GC (UMR_8504)), Université Paris 1 Panthéon-Sorbonne (UP1)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), École normale supérieure - Cachan (ENS Cachan)-Université Paris 1 Panthéon-Sorbonne (UP1)-Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-École polytechnique (X)-Institut Curie [Paris]-Centre National de la Recherche Scientifique (CNRS), Identité et Différenciation de l’Espace, de l’Environnement et des Sociétés (IDEES), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche Interdisciplinaire Homme et Société (IRIHS), Normandie Université (NU)-Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche Interdisciplinaire Homme et Société (IRIHS), Université Paris 1 Panthéon-Sorbonne (UP1)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), École normale supérieure - Cachan (ENS Cachan)-Université Panthéon-Sorbonne (UP1)-Université Paris-Sud - Paris 11 (UP11)-Université Pierre et Marie Curie - Paris 6 (UPMC)-École polytechnique (X)-Institut Curie-Centre National de la Recherche Scientifique (CNRS), and Université Panthéon-Sorbonne (UP1)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC], Physics - Physics and Society, Information Systems and Management, Computer Networks and Communications, Computer science, Alternative hypothesis, 0211 other engineering and technologies, Evolutionary algorithm, ABM, FOS: Physical sciences, multimodelling, Physics and Society (physics.soc-ph), 02 engineering and technology, Equifinality, lcsh:TA168, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], lcsh:Technology (General), 0502 economics and business, former soviet union, Virtual Laboratory, system of cities, 050207 economics, ComputingMilieux_MISCELLANEOUS, Statistical hypothesis testing, equifinality, business.industry, 05 social sciences, 021107 urban & regional planning, [SHS.GEO]Humanities and Social Sciences/Geography, Modular design, simulation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Industrial engineering, System dynamics, Identification (information), lcsh:Systems engineering, [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA], Control and Systems Engineering, Modeling and Simulation, lcsh:T1-995, [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], business, Software
Abstract

In this paper, we present a modelling experiment developed to study systems of cities and processes of urbanisation in large territories over long time spans. Building on geographical theories of urban evolution, we rely on agent-based models to 1/ formalise complementary and alternative hypotheses of urbanisation and 2/ explore their ability to simulate observed patterns in a virtual laboratory. The paper is therefore divided into two sections : an overview of the mechanisms implemented to represent competing hypotheses used to simulate urban evolution; and an evaluation of the resulting model structures in their ability to simulate - efficiently and parsimoniously - a system of cities (the Former Soviet Union) over several periods of time (before and after the crash of the USSR). We do so using a modular framework of model-building and evolutionary algorithms for the calibration of several model structures. This project aims at tackling equifinality in systems dynamics by confronting different mechanisms with similar evaluation criteria. It enables the identification of the best-performing models with respect to the chosen criteria by scanning automatically the parameter along with the space of model structures (as combinations of modelled dynamics)., 21 pages, 3 figures, working paper

Published
2015
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