HAL-Université de Bretagne Occidentale
Not a member yet
75442 research outputs found
Sort by
A Dynamic Cartographic Tool Suite for Coastal Flooding Vulnerability Assessment
No full textInternational audienceIn this article we propose a generic cartographic toolkit for vulnerability assessment to hazard based on multi-criteria analysis, enabling the most vulnerable areas to be accurately mapped. To achieve this, we designed an adaptive cartographic grid, with cell size being adapted to the geographical features. We also created a synthetic map relying on an innovative method based on the Choquet integral and the MACBETH method, in collaboration with the emergency services of the Pays de la Loire region (France). Finally our toolkit includes a dynamic decision-making tool for firefighters working in these sensible areas, which might help the emergency services to intervene with a spatial resolution of 25m/25m, including interactive maps to ease decision making. In this way, this methodology provides a global view and knowledge of the issues and vulnerability upstream of the crisis. To illustrate the use of our toolkit, we use it on the coastal flooding hazard in a Batz-sur-mer, a city in Pays de la Loire. This tool will be transferable and adaptable to other study areas and other risks following this work
Optimizing Photovoltaic Detection in High-Resolution Satellite Imagery Using GIS, DeepLabv3+, and Transformer-Based Models: A Case Study of the Marrakesh-Safi Region
No full textInternational audienceSolar energy has become a major contributor to global renewable energy strategies, offering a sustainable alternative to fossil fuels. Photovoltaic (PV) systems, which convert sunlight into electricity, play a central role in this transition. As the demand for large-scale solar energy projects grows, Geographic Information Systems (GIS) and advanced deep learning models have become critical for accurately detecting and mapping PV installations, particularly from satellite imagery. However, challenges remain, especially in regions with suboptimal satellite data quality. This study focuses on the Marrakesh-Safi region of Morocco, where the potential for solar energy is high but hindered by limitations in available satellite imagery. We employ advanced transformer-based models, including Mask2Former, SegFormer, and DeepLabV3+, to enhance the semantic segmentation of PV systems from high-resolution satellite images. By integrating GIS with these deep learning models, we aim to improve the accuracy and scalability of PV detection, even in complex and diverse geographical settings. Our methodology involves training and testing these models on annotated satellite imagery, with performance evaluated using key metrics such as Intersection over Union (IoU), precision, recall, and F1 score. Mask2Former achieved notable results with a recall of 0.95 and an F1 score of 0.936, excelling in the detection of smaller and more complex PV layouts. DeepLabV3+ demonstrated strong overall performance, with an IoU of 0.89 and precision of 0.93, while also being the most computationally efficient model, processing 28 samples per second. This research highlights the effectiveness of integrating GIS with deep learning, particularly transformer-based architectures, for the accurate detection and mapping of PV systems. The results contribute to the broader efforts in renewable energy optimization, supporting more efficient solar energy deployment, especially in regions like Morocco where data quality poses significant challenges
Predicting Leishmaniasis Risk in Morocco Using Machine Learning, GIS, and Domain Adaptation : A case study of Beni Mellal-Khenifra Region
No full textInternational audienceLeishmaniasis remains a persistent global public health challenge, particularly in regions where ecological and socioeconomic conditions favor vector proliferation and disease transmission. In Morocco, the provinces of Beni Mellal and Khenifra are among the most severely affected, necessitating the use of advanced spatial prediction tools to guide effective disease control strategies. This study integrated machine learning techniques and Geographic Information System (GIS) technologies to develop a predictive framework for cutaneous leishmaniasis risk mapping. A spatial database was constructed by combining reported case data from 2011 to 2018 with key environmental and climatic variables including temperature, precipitation, normalized difference vegetation index (NDVI), altitude, slope, and wind speed. Three machine learning algorithms, Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were evaluated for their predictive performance, while the CORrelation ALignment (CORAL) method was applied as a domain adaptation strategy to address distributional differences between training and target regions. The results demonstrated that XGBoost achieved the highest predictive accuracy (R2 = 0.91, MSE = 0.1229, MAE = 0.2587), followed by SVR (R2 = 0.89, MSE = 0.1434, MAE = 0.2765), and RF (R2 = 0.85, MSE = 0.1925, MAE = 0.3120). Incorporating CORAL significantly improved the model generalizability and stability across ecologically diverse zones. The final risk map identified high-risk clusters in central and northern Beni Mellal and Khenifra, offering actionable insights into spatially targeted interventions. This study presents a scalable GIS-integrated machine learning framework with strong potential for application in other data-scarce high-risk regions. Future research should incorporate real-time data and advanced deep learning techniques to further enhance the predictive power
Replacing strain gages by line camera DIC in Hopkinson bar experiments
No full textInternational audienc
Differing responses of functional and taxonomic waterbird diversity to vegetation height and water level variation in a coastal wetland
No full textInternational audienceThe conservation of wetland biodiversity is a major global issue. In anthropogenic landscapes, it requires the identification of environmental conditions and management practices that sustainably maintain the diversity of the communities. We carried out a six-year survey over 18 sites (78 ha) in the Marais Breton, lowland grazing marshes on the western coast of Europe in France. We tested the influence of the spatio-temporal dynamic of vegetation heights, water depth and the proportion of flooded areas on the taxonomic and functional diversity of wading birds and ducks at the site scale. Taxonomic diversity was enhanced by higher spatial heterogeneity of water level and reduced by higher spatial heterogeneity of vegetation height. In sharp contrast, functional diversity was not influenced by spatial heterogeneity of water level and increased with spatial heterogeneity of vegetation height. Additionally, the effect of the spatio-temporal heterogeneity of water level and vegetation height was guild-dependent. Based on our results we encourage a management at the landscape scale integrating multiple land ownerships to promote a taxonomic and functional diversity rather than at the site scale only
Digital twin-based enhancement of vibration measurements for gearbox monitoring under data scarcity
No full textInternational audienc
Anticipating potential environmental risks of offshore hydrogen production powered by offshore wind farm
No full textInternational audienc
Cutaneous and subcutaneous nodular scedosporiosis in a cat
No full textInternational audienceThis article reports the clinical, diagnostic, and therapeutic aspects of a rare case of cutaneous and subcutaneous scedosporiosis in a 16-year-old domestic cat. The animal presented with a painful, ulcerated nodule at the base of the tail. Histopathology and fungal culture confirmed a pyogranulomatous dermatitis with hyaline hyphae, and molecular analysis identified Scedosporium sphaerospermum, a soil-dwelling fungus newly described. Despite targeted antifungal treatment with itraconazole and topical ciclopirox olamine, the cat developed systemic complications and died. This case emphasizes the challenges associated with diagnosing and managing opportunistic fungal infections in companion animals. It is, to the authors’ knowledge, the first case of infection caused by S. sphaerospermum
[Jurisprudence - Responsabilité civile] Victoire sur tapis vert de la responsabilité du fait d'une balle de squash… ou de la raquette qui l'a projetée
No full textInternational audienceCass. 2e civ., 27 nov. 2025, no 24-12045 , F–B (cassation CA Pau, 19 déc. 2023).[L’essentiel]La responsabilité individuelle du fait des choses de l’article 1242 du Code civil s’applique en matière de dommage causé à l’occasion d’une partie de squash, à l’exclusion de la garde collective, puisque le joueur qui a lancé la balle à l’origine du dommage exerçait seul, au moment de ce dernier, les pouvoirs d’usage, de contrôle et de direction sur la raquette qui l’a projetée. Par sa solution, la Cour de cassation sème le doute quant à la chose qui est la cause du dommage, de la balle à la raquette, en remontant ce faisant la chaîne de la causalité d’un degré. -- Balles et ballons -- Lors d’une partieI – L’engagement de la responsabilité individuelle du joueur gardien -- Responsabilité du fait des choses -- Une garde de la chose -- Le rôle causal de la choseII – La logique indemnitaire de la responsabilité individuelle -- Garde collective -- Garde individuelle -- Score fina
Victime de violences intrafamiliales durant l’enfance : quelles représentations de l’attachement à l’âge adulte ?
No full textInternational audienceIntroductionWhile the consequences of domestic violence on child and adolescent development are now well established, their long-term impact in adulthood remains relatively underexplored.ObjectiveThe present study aimed to assess adult attachment styles among individuals who were exposed to domestic violence during childhood and to identify potential moderating factors influencing this relationship.MethodA total of 225 adults completed an online questionnaire including the Children's Perception of Interparental Conflict, the Parental Bonding Instrument and the Relationship Scale Questionnaire. Among the participants, 121 reported childhood exposure to domestic violence, while 104 reported no such exposure.ResultsResults revealed a significant association between exposure to domestic violence as a child and an anxious adult attachment. A MANOVA analysis revealed significant effects of maternal control, perceived blame, and perceived threat on the attachment dimensions.ConclusionThis study provides insight into how the perception of early family experiences relates to adult attachment representations. Regression analyses revealed that anxious attachment was specifically linked to heightened perceptions of maternal control and increased perceived blame, alongside an inverse trend observed for paternal care