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Des mesures au sol aux images satellite : quelles données pour étudier la pollution lumineuse ? / Christophe Plotard in XYZ, n° 174 (mars 2023)

Public [article]  inXYZ > n° 174 (mars 2023) . - pp 33 - 38 Titre : Des mesures au sol aux images satellite : quelles données pour étudier la pollution lumineuse ? Type de document : Article/Communication Auteurs : Christophe Plotard, Auteur ; Philippe Deverchère, Auteur ; Sarah Potin, Auteur ; Sébastien Vauclair, Auteur Année de publication : 2023 Article en page(s) : pp 33 - 38 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] analyse comparative [Termes IGN] carte thématique [Termes IGN] données de terrain [Termes IGN] échelle d'intensité [Termes IGN] flux lumineux [Termes IGN] image à basse résolution [Termes IGN] image à très haute résolution [Termes IGN] image NPP-VIIRS [Termes IGN] image satellite [Termes IGN] impact sur l'environnement [Termes IGN] intensité lumineuse [Termes IGN] inventaire [Termes IGN] modèle numérique de surface [Termes IGN] modélisation 3D [Termes IGN] photomètre [Termes IGN] pollution lumineuse [Termes IGN] prise de vue nocturne [Termes IGN] radiance Résumé : (Auteur) Le développement de l’éclairage artificiel nocturne est à l’origine d’une pollution lumineuse aux effets néfastes pour la biodiversité, la santé humaine, la consommation énergétique et l’observation astronomique. Pour analyser les différentes formes de cette pollution, le bureau d’études DarkSkyLab s’appuie sur plusieurs types de données tels que des mesures depuis le sol, des images satellitaires et aériennes, ou des inventaires de points d’éclairage. Cet article en présente les principaux aspects, de même que divers outils, méthodes et indicateurs conçus pour permettre leur traitement, leur modélisation et leur représentation cartographique. Numéro de notice : A2023-069 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/03/2023 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102863 [article]

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Siamese KPConv: 3D multiple change detection from raw point clouds using deep learning / Iris de Gelis in ISPRS Journal of photogrammetry and remote sensing, vol 197 (March 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 197 (March 2023) . - pp 274 - 291 Titre : Siamese KPConv: 3D multiple change detection from raw point clouds using deep learning Type de document : Article/Communication Auteurs : Iris de Gelis, Auteur ; Sébastien Lefèvre, Auteur ; Thomas Corpetti, Auteur Année de publication : 2023 Article en page(s) : pp 274 - 291 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage profond [Termes IGN] bâtiment [Termes IGN] détection de changement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] modèle numérique de surface [Termes IGN] réseau neuronal siamois [Termes IGN] semis de points [Termes IGN] végétation [Termes IGN] zone urbaine Résumé : (auteur) This study is concerned with urban change detection and categorization in point clouds. In such situations, objects are mainly characterized by their vertical axis, and the use of native 3D data such as 3D Point Clouds (PCs) is, in general, preferred to rasterized versions because of significant loss of information implied by any rasterization process. Yet, for obvious practical reasons, most existing studies only focus on 2D images for change detection purpose. In this paper, we propose a method capable of performing change detection directly within 3D data. Despite recent deep learning developments in remote sensing, to the best of our knowledge there is no such method to tackle multi-class change segmentation that directly processes raw 3D PCs. Thereby, based on advances in deep learning for change detection in 2D images and for analysis of 3D point clouds, we propose a deep Siamese KPConv network that deals with raw 3D PCs to perform change detection and categorization in a single step. Experimental results are conducted on synthetic and real data of various kinds (LiDAR, multi-sensors). Tests performed on simulated low density LiDAR and multi-sensor datasets show that our proposed method can obtain up to 80% of mean of IoU over classes of changes, leading to an improvement ranging from 10% to 30% over the state-of-the-art. A similar range of improvements is attainable on real data. Then, we show that pre-training Siamese KPConv on simulated PCs allows us to greatly reduce (more than 3,000 ) the annotations required on real data. This is a highly significant result to deal with practical scenarios. Finally, an adaptation of Siamese KPConv is realized to deal with change classification at PC scale. Our network overtakes the current state-of-the-art deep learning method by 23% and 15% of mean of IoU when assessed on synthetic and public Change3D datasets, respectively. The code is available at the following link: https://github.com/IdeGelis/torch-points3d-SiameseKPConv. Numéro de notice : A2023-147 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2023.02.001 Date de publication en ligne : 17/02/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.02.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102805 [article]

The potential of combining satellite and airborne remote sensing data for habitat classification and monitoring in forest landscapes / Anna Iglseder in International journal of applied Earth observation and geoinformation, vol 117 (March 2023)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 117 (March 2023) . - n° 103131 Titre : The potential of combining satellite and airborne remote sensing data for habitat classification and monitoring in forest landscapes Type de document : Article/Communication Auteurs : Anna Iglseder, Auteur ; Markus Immitzer, Auteur ; Alena Dostalova, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 103131 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte thématique [Termes IGN] cartographie écologique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données Copernicus [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] habitat (nature) [Termes IGN] habitat forestier [Termes IGN] image radar moirée [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] modèle numérique de surface [Termes IGN] paysage forestier [Termes IGN] protection de la biodiversité [Termes IGN] site Natura 2000 [Termes IGN] Vienne (capitale Autriche) Résumé : (auteur) Mapping and monitoring of habitats are requirements for protecting biodiversity. In this study, we investigated the benefit of combining airborne (laser scanning, image-based point clouds) and satellite-based (Sentinel 1 and 2) data for habitat classification. We used a two level random forest 10-fold leave-location-out cross-validation workflow to model Natura 2000 forest and grassland habitat types on a 10 m pixel scale at two study sites in Vienna, Austria. We showed that models using combined airborne and satellite-based remote sensing data perform significantly better for forests than airborne or satellite-based data alone. For frequently occurring classes, we reached class accuracies with F1-scores from 0.60 to 0.87. We identified clear difficulties of correctly assigning rare classes with model-based classification. Finally, we demonstrated the potential of the workflow to identify errors in reference data and point to the opportunities for integration in habitat mapping and monitoring. Numéro de notice : A2023-128 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.103131 Date de publication en ligne : 12/01/2023 En ligne : https://doi.org/10.1016/j.jag.2022.103131 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102512 [article]

Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities / Pavlos Tsagkis in Sustainable Cities and Society, vol 89 (February 2023)  

Public [article]  inSustainable Cities and Society > vol 89 (February 2023) . - n° 104337 Titre : Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities Type de document : Article/Communication Auteurs : Pavlos Tsagkis, Auteur ; Efthimios Bakogiannis, Auteur ; Alexandros Nikitas, Auteur Année de publication : 2023 Article en page(s) : n° 104337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] automate cellulaire [Termes IGN] Corine (base de données) [Termes IGN] croissance urbaine [Termes IGN] données localisées libres [Termes IGN] étalement urbain [Termes IGN] Grèce [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de surface [Termes IGN] modèle orienté agent [Termes IGN] OpenStreetMap [Termes IGN] planification urbaine [Termes IGN] réseau neuronal artificiel Résumé : (auteur) Urban development if not planned and managed adequately can be unsustainable. Urban growth models have been a powerful toolkit to help tackling this challenge. In this paper, we use a machine learning approach, to apply an urban growth model to five of the largest cities in Greece. Specifically, we first develop a methodology to collect, organise, handle and transform historical open spatial data, concerning various impact factors, into machine learning data. Such factors involve social, economic, biophysical, neighbouring-related and political driving forces, which must be transformed into tabular data. We also provide an artificial neural network (ANN) model and the methodology to train and evaluate it using goodness-of-fit metrics, which in turn provide the best weights of impact factors. Finally, we execute a prediction for 2030, presenting the results and output maps for each of the five case study cities. As our study is based on pan-European datasets, our model can be used for any area within Europe, using the open-source utility developed to support it. In this sense, our work provides local policy-makers and urban planners with an instrument that could help them analyse various future development scenarios and take the right decisions going forward. Numéro de notice : A2023-116 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article DOI : 10.1016/j.scs.2022.104337 Date de publication en ligne : 05/12/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104337 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102486 [article]

GIS-based planning of buffer zones for protection of boreal streams and their riparian forests / Heikki Mykrä in Forest ecology and management, vol 528 (January-15 2023)  

Public [article]  inForest ecology and management > vol 528 (January-15 2023) . - n° 120639 Titre : GIS-based planning of buffer zones for protection of boreal streams and their riparian forests Type de document : Article/Communication Auteurs : Heikki Mykrä, Auteur ; M.J. Annala, Auteur ; Anu Hilli, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 120639 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] Alnus incana [Termes IGN] Betula pendula [Termes IGN] cours d'eau [Termes IGN] données lidar [Termes IGN] érosion hydrique [Termes IGN] forêt ripicole [Termes IGN] humidité du sol [Termes IGN] modèle numérique de surface [Termes IGN] modèle RUSLE [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] protection de la biodiversité [Termes IGN] Salix (genre) [Termes IGN] zone boréale [Termes IGN] zone tampon Résumé : (auteur) Forested buffer zones with varying width have been suggested as the most promising approach for protecting boreal riparian biodiversity, reducing erosion, and minimizing nutrient leaching from managed forestry areas. Yet, less optimal fixed-width approach is still largely used, likely because of its simple design and implementation. We examined the efficiency of varying-width buffer zones based on depth-to-water (DTW) index in protecting stream riparian plant communities. We further compared the economic costs of DTW-based buffer to commonly used 5, 10 and 15 m fixed-width buffers. We also included an additional buffer based on a combination of DTW and erosion risk (Revised Universal Soil Loss Equation, RUSLE) into these comparisons to see the extent and cost of a buffer that should maximize the protection of the linked aquatic environment. Plant species richness increased with increasing soil moisture and species preferring moist conditions, nutrient-rich soils and high pH were clearly more abundant adjacent to stream in areas with high predicted soil moisture than in dry areas. Differences in species richness were paralleled by differences in community composition and higher beta diversity of plant communities in wet than in dry riparian areas. There were also several indicator species typical for moist and nutrient-rich soils for wet riparian areas. Riparian buffer zones based on DTW were on average larger than 15 m wide fixed-width buffers. However, the cost for DTW-based buffer was lower than for fixed-width buffer zones when the cost was normalized by area. Simulated selective cutting decreased the costs, but cutting possibilities were variable among streams and depended on the characteristics of forest stands. Our results thus suggest a high potential of DTW in predicting wet areas and variable-width buffer zones based on these areas in the protection of riparian biodiversity and stream ecosystems. Numéro de notice : A2023-029 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.1016/j.foreco.2022.120639 Date de publication en ligne : 13/11/2022 En ligne : https://doi.org/10.1016/j.foreco.2022.120639 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102148 [article]

Estimation of lidar-based gridded DEM uncertainty with varying terrain roughness and point density / Luyen K. Bui in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 7 (January 2023)  

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A hierarchical deformable deep neural network and an aerial image benchmark dataset for surface multiview stereo reconstruction / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 61 n° 1 (January 2023)  

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Machine learning remote sensing using the random forest classifier to detect the building damage caused by the Anak Krakatau Volcano tsunami / Riantini Virtriana in Geomatics, Natural Hazards and Risk, vol 14 n° 1 (2023)  

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Mitigating the risk of wind damage at the forest landscape level by using stand neighbourhood and terrain elevation information in forest planning / Roope Ruotsalainen in Forestry, an international journal of forest research, vol 96 n° 1 (January 2023)  

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Modeling the gravitational effects of ocean tide loading at coastal stations in the China earthquake gravity network based on GOTL software / Chuandong Zhu in Journal of applied geodesy, vol 17 n° 1 (January 2023)  

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Multi-information PointNet++ fusion method for DEM construction from airborne LiDAR data / Hong Hu in Geocarto international, vol 38 n° 1 ([01/01/2023])  

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Hybrid XGboost model with various Bayesian hyperparameter optimization algorithms for flood hazard susceptibility modeling / Saeid Janizadeh in Geocarto international, vol 37 n° 25 ([01/12/2022])  

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Geographic information system data considerations in the context of the enhanced bathtub model for coastal inundation / Lauren Lyn Williams in Transactions in GIS, vol 26 n° 7 (November 2022)  

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Terrain representation using orientation / Gene Trantham in Cartography and Geographic Information Science, vol 49 n° 6 (November 2022)  

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A deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas / Hossein Pourazar in Geocarto international, vol 37 n° 23 ([15/10/2022])  

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