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e-CAPTURE data: High-resolution UAV dataset of riparian vegetation structure and large woody debris distribution along the Tagliamento River (NE Italy) Description This dataset was produced within the e-CAPTURE project (Eco-geomorphic CArbon Pumping from rivers To blUe caRbon Ecosystems), funded by the Italian Ministry of University and Research under PRIN 2022 — NRRP Mission 4, Component 2, Investment 1.1. The dataset comprises high-resolution UAV remote sensing products acquired along two reaches of the Tagliamento River (NE Italy)— a braided reach near Spilimbergo and a meandering reach near Latisana — between February 2024 and March 2025. The data were collected to characterise riparian vegetation structure, biomass distribution, and the spatial organisation of large woody debris (LWD) within the fluvial corridor, in support of the Eco-Geomorphic Carbon Pump (EGCP) conceptual framework developed by the project. The datasets were used to develop and validate machine-learning approaches (U-Net and DeepLabv3+ deep learning models) for the automated detection and mapping of large woody debris in braided river environments, providing key information on woody biomass recruitment, transport, and potential burial — processes central to the quantification of carbon fluxes within the river corridor. Data include: - **LiDAR point clouds** acquired with DJI Matrice platforms equipped with L1 and L2 sensors, covering both the active channel zone and the riparian vegetation belt. *Available upon request to the authors.* - **Digital Terrain Models (DTM) and Digital Surface Models (DSM)** at 10 cm and 20 cm resolution. - **RGB orthomosaics** at 2.5–3 cm resolution from UAV optical surveys (DJI Matrice + P1 camera, 35 mm lens). Data collection: UAV surveys were conducted by ARPA Valle d'Aosta in coordination with Politecnico di Torino (PoliTO) and Università degli studi di Trento, as part of coordinated field campaigns involving all project operational units. --- Keywords riparian vegetation, large woody debris, UAV, LiDAR, braided river, Tagliamento, eco-geomorphic carbon pump, deep learning, remote sensing, fluvial geomorphology, carbon cycle, PRIN 2022 License Creative Commons Attribution 4.0 International (CC BY 4.0) Related publications - Han, Q.; Belcore, E.; Morra di Cella, U.; Salerno, L.; Camporeale, C. (2026). Detection of Large Woody Debris in Braided-Rivers RGB-UAV Dataset: A Comparative Study. *Remote Sensing*, 18, 900. DOI: [10.3390/rs18060900](https://doi.org/10.3390/rs18060900) Funding PRIN 2022 — National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.1 — "Fund for the National Research Program and for Projects of National Interest (NRP)" — Project e-CAPTURE. Principal Investigator: Prof. Carlo Vincenzo Camporeale, Politecnico di Torino.