Available Projects
Airborne laser scanning (ALS) is a widely adopted remote sensing technology, renowned for its efficient and precise modeling of forests. This is attributed to its capability to accurately describe the geometric features of trees within a forest. However, automating the identification of individual trees and their species from ALS data poses a formidable challenge. Traditional closed-form clustering algorithms yield inaccurate segmentation results, and deep learning-based methods demand substantial amounts of labeled training data, which is impractical to establish manually.
This project aims to tackle the challenges associated with object labeling and accuracy by employing unsupervised and self-supervised approaches. Unsupervised methods are utilized to obtain a preliminary segmentation of the ALS data. Subsequently, these roughly segmented tree examples will be hand labeled and employed to train a classifier, facilitating the identification of well-segmented tree individuals. In the final step, these labels will be used to calibrate and refine state of the art segmentation and classification algorithms, employing a semi-supervised approach.
This project investigates the application of guided super-resolution techniques for monitoring canopy dynamics on the EPFL campus, supporting the university’s Climate and Sustainability Strategy 2030. Using high-resolution LiDAR and RGB data acquired in June 2024 as a pseudo ground-truth, the study explores how lower-resolution satellite and airborne imagery can be enhanced through guided methods to improve canopy index estimation. By combining super-resolution with canopy estimation pipelines based on deep learning or indices, the project aims to establish a workflow that is both accurate and reproducible across multiple years.
This project utilizes deep learning techniques, to improve the accuracy of LiDAR point clouds in remote sensing tasks. By establishing 3D correspondences, the project aims to refine the point cloud accuracy and enable more precise terrain mapping, object recognition, change detection, and environmental monitoring.
The objectives include analyzing and implementing state-of-the-art techniques and evaluating their effectiveness on real large scale LiDAR point cloud datasets. Challenges like noisy data, occlusions, and computational efficiency will be addressed by leveraging novel data representations, training metrics and network architectures.
In this project, you will contribute on improving a real car-mounted laser scanning system.
The project will focus on adapting a deep learning methodology developed in our lab and able to recognize correspondences (i.e. recognizable points scanned multiple times in a point cloud). These correspondences can then be leveraged to refine the estimation of the trajectory of the vehicle. The end goal is to improve the robustness of the trajectory estimation and point cloud generation pipeline when GNSS signal degradation occurs, allowing for more accurate 3D digitization of scanned areas.
