Laia Albors
| Position | |
|---|---|
| PhD Candidate | laia.albors@upc.edu |
| Office | Phone |
|---|---|
| D5-120 |
Biography
Laia Albors received her B.Sc. in Data Science and Engineering from the Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Barcelona, Spain, in 2021, and her M.Sc. in Computer Vision from the Universitat Autònoma de Barcelona (UAB) in 2022. She previously worked as a Junior Research Engineer at the Barcelona Supercomputing Center (BSC) within the Emerging Technologies for Artificial Intelligence group. She is currently pursuing a Ph.D. in Computer Vision at UPC, supported by an FPU scholarship from the Spanish Ministry of Science, Innovation and Universities. Her research interests focus on ecological and environmental applications of deep learning and computer vision.
Journal Articles top
| , “BiomSHARP: Biomass Super-Resolution for High Accuracy Prediction”, IEEE Transactions on Geoscience and Remote Sensing, vol. 63, pp. 1-18, 2025. |
| , “Comparison of Conventional Machine Learning and Convolutional Deep Learning models for Seagrass Mapping using Satellite Imagery”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, pp. 1-19, 2025. |
| , “Performance of Individual Tree Segmentation Algorithms in Forest Ecosystems Using UAV LiDAR Data”, Drones, vol. 8, no. 12, 2024. |
Conference Papers top
| , “Comparative Analysis of Tree Segmentation Techniques on High- and Low-Density LiDAR data”, in International Conference on Advanced Remote Sensing (ICARS 2025), Basel, Switzerland, 2025. |
| , “Análisis multiresolución espacial del estado de conservación de un bosque de laurisilvas con sensores pasivos y activos de teledetección”, in XX Congreso de la Asociación Española de Teledetección, Cádiz, España, 2024. |
| , “Generación de mapas de comunidades y hábitats bentónicos mediante el modelo Deep Learning U-Net utilizando imágenes satelitales multiespectrales de muy alta resolución”, in XX Congreso de la Asociación Española de Teledetección, Cádiz, España, 2024. |
Projects top
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Collaborative heterogeneous 3D scene representations for Urban Mobility | National | Sep 2025 | Aug 2028 |