Belvedere Glacier long-term monitoring Open Data - Pointclouds, DEMs, Orthophotos
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Introduction This dataset contains extensive, long-term monitoring data on the Belvedere Glacier, a debris-covered glacier located on the east face of Monte Rosa in the Anzasca Valley of the Italian Alps. The data is derived from photogrammetric 3D reconstruction of the full Belvedere Glacier and includes: dense point clouds obtained with UAV SfM-MVS covering the entire glacier body high-resolution orthophotos high-resolution DEMs Since 2015, in-situ survey of the glacier have been conducted annually using fixed-wing UAVs until 2020 and quadcopters from 2021 to 2022 to remotely sense the glacier and build high-resolution photogrammetric models. A set of ground control points (GCPs) were materialized all over the glacier area, both inside the glacier and along the moraines, and surveyed (nearly-) yearly with topographic-grade GNSS receivers (Ioli et al., 2022). For the period from 1977 to 2001, historical analog images, digitalized with photogrammetric scanners and acquired from aerial platforms, were used in combination with GCPs obtained from recent photogrammetric models (De Gaetani et al., 2021). Before downloading them, you can explore the photogrammetric point clouds of the Belvedere Glacier within web app based on Potree from https://thebelvedereglacier.it/ (use a web browser from a desktop/laptop for the best experience). Additionally, from here you can also visualize and download the coordinates of the GCPs measured by GNSS every year since 2015. Data organization The data are organized by year in compressed zip folders named belvedere_YYYY.zip, which can be downloaded independently. Each folder contains all data available for that year (i.e., photogrammetric point clouds, orthophotos, and DEMs) and the corresponding metadata. Metadata is provided as a .json file, which contains all the main information for data usage. Point clouds are saved in compressed LAS format (.laz) and they can be inspected, e.g., with CloudCompare. Orthophotos and DEMs are georeferenced images (.tif) that can be inspected with any GIS software (e.g., QGIS). Large point clouds are subdivided into regular tiles, which are numbered in a progressive row-wise order from the bottom-left corner of the point cloud bounding box. All the files are named according to the following naming schema: "belv_YYYY_surveyplatform_datatype[_resolution][vertical_datum][-tile_number].extension" where: YYYY: is the year of the survey surveyplatform: can be either "uav" for the UAV-based photogrammetry survey or "histo" for the historical aerial datasets. datatype: can be either "pcd" for point clouds, "orthophoto" for orthophotos, or "dsm" for DSMs. resolution: on-ground resolution of each pixel in meters. This applies only to raster data (orthophotos and DSMs) vertical_datum: if the DSM is given in orthometric coordinates, the label "ortho" is present in the filename; otherwise, the height of the dataset is supposed to be ellipsoidal. tile: tile number, if the data is tiled to avoid large files. Data Usage This dataset can be used to estimate glacier velocities, volume variations, study geomorphological processes such as the process of moraine collapse, or derive other information on glacier dynamics. If you have any requests on the data provided, data acquisition, or the raw data themselves, you are encouraged to contact us. Contributions The monitoring activity carried out on the Belvedere Glacier was designed and conducted jointly by the Department of Civil and Environmental Engineering (DICA) of Politecnico di Milano and the Department of Environment, Land and Infrastructure Engineering (DIATI) of Politecnico di Torino. The DREAM projects (DRone tEchnnology for wAter resources and hydrologic hazard Monitoring), involving teachers and students from Alta Scuola Politecnica (ASP) of Politecnico di Torino and Milano, contributed to the campaign from 2015 to 2017. Acknowledgements The authors thank CGR SpA for digitizing the historical images (1977, 1991, 2001, 2009) and making them available to the authors for the photogrammetric processing. The authors thank all students and collaborators contributing to the Alta Scuola Politecnica projects DREAM 1, DREAM 2, and DREAM 3 (DRone tEchnnology for wAter resources and hydrologic hazard Monitoring). If you use the data, please, cite these our pubblications: Open data description, monitoring project, and recent data acquisition strategy: Gaspari, F., Barbieri, F., Fascia, R., Ioli, F., Pinto, L., & Migliaccio, F. (2025). Strategies for Glacier Retreat Communication with 3D Geovisualization and Open Data Sharing. ISPRS International Journal of Geo-Information, 14(2), 75. https://doi.org/10.3390/ijgi14020075 UAV datasets: Ioli, F.; Bianchi, A.; Cina, A.; De Michele, C.; Maschio, P.; Passoni, D.; Pinto, L. Mid-Term Monitoring of Glacier’s Variations with UAVs: The Example of the Belvedere Glacier. Remote Sensing, 14, 28 (2022). https://doi.org/10.3390/rs14010028 Historical aerial datasets: De Gaetani, C.I.; Ioli, F.; Pinto, L. Aerial and UAV Images for Photogrammetric Analysis of Belvedere Glacier Evolution in the Period 1977–2019. Remote Sensing, 13, 3787 (2021). https://doi.org/10.3390/rs13183787 Short-term Belvedere monitoring: Ioli, F., Dematteis, N., Giordan, D., Nex, F., Pinto, L., Deep Learning Low-cost Photogrammetry for 4D Short-term Glacier Dynamics Monitoring. PFG (2024). https://doi.org/10.1007/s41064-023-00272-w
引言 本数据集包含意大利阿尔卑斯山安扎斯卡谷蒙特罗莎东坡覆碛冰川贝尔韦代雷冰川的大规模长期监测数据。数据源自整个贝尔韦代雷冰川的摄影测量三维重建，涵盖： 1. 基于无人机（Unmanned Aerial Vehicle, UAV）结构光运动恢复-多视图立体（Structure from Motion-Multi View Stereo, SfM-MVS）技术获取的覆盖全冰川体的密集点云； 2. 高分辨率正射影像； 3. 高分辨率数字高程模型（Digital Elevation Model, DEMs）。 自2015年起，研究团队每年开展冰川原位勘测：2015至2020年使用固定翼无人机，2021至2022年改用四旋翼无人机，以遥感方式获取冰川数据并构建高分辨率摄影测量模型。研究团队在冰川全域（包括冰体内部及冰碛沿线）布设了一系列地面控制点（ground control points, GCPs），并使用地形级GNSS接收机近乎每年开展一次测量（Ioli等，2022）。 针对1977年至2001年的历史时期，研究结合使用了经摄影测量扫描仪数字化、由航空平台获取的历史模拟影像，以及源自近期摄影测量模型的地面控制点（De Gaetani等，2021）。 在下载数据前，可通过https://thebelvedereglacier.it/ 访问基于Potree开发的网页应用，在线浏览贝尔韦代雷冰川的摄影测量点云（建议使用台式机或笔记本电脑的浏览器以获得最佳体验）。此外，用户还可在此平台查看并下载2015年以来每年通过GNSS测量获取的地面控制点坐标。 数据组织形式 本数据集按年份整理为压缩ZIP包，命名格式为`belvedere_YYYY.zip`，支持单独下载。每个压缩包包含对应年份的全部可用数据（即摄影测量点云、正射影像与数字高程模型）及相应元数据。元数据以JSON文件格式提供，包含数据使用所需的全部核心信息。点云以压缩LAS格式（.laz）存储，可通过CloudCompare等工具查看。正射影像与数字高程模型为地理参考栅格图像（.tif），可通过任意地理信息系统（Geographic Information System, GIS）软件（如QGIS）打开浏览。 大型点云被拆分为规则瓦片，瓦片编号从点云包围盒（bounding box）的左下角开始，按行优先顺序递增编排。 所有文件均遵循以下命名规范： `belv_YYYY_surveyplatform_datatype[_resolution][vertical_datum][-tile_number].extension` 其中： - YYYY：勘测年份 - surveyplatform：数据采集平台，可取值为`uav`（基于无人机的摄影测量勘测）或`histo`（历史航空数据集） - datatype：数据类型，可取值为`pcd`（点云）、`orthophoto`（正射影像）或`dsm`（数字表面模型，Digital Surface Model, DSM） - resolution：像素的地面分辨率（单位：米），仅适用于栅格数据（正射影像与DSM） - vertical_datum：垂直基准面，若DSM采用正高坐标系，文件名中将包含标签`ortho`；否则默认数据集高度为椭球高 - tile：瓦片编号，用于拆分大型文件时的瓦片标识 数据用途 本数据集可用于估算冰川流速、体积变化，研究冰碛崩塌等地貌过程，或获取冰川动力学相关的其他信息。若您对所提供的数据、数据采集流程或原始数据有任何需求，欢迎联系我们。 贡献单位 贝尔韦代雷冰川的监测活动由米兰理工大学土木与环境工程系（DICA）与都灵理工大学环境、土地与基础设施工程系（DIATI）联合设计并开展。2015至2017年的监测工作由都灵理工大学与米兰理工大学高等理工学院（ASP）的师生参与的DREAM项目（水资源与水文灾害监测无人机技术项目，DRone tEchnnology for wAter resources and hydrologic hazard Monitoring）提供支持。 致谢 作者感谢CGR SpA对历史影像（1977、1991、2001、2009年）进行数字化并授权用于摄影测量处理。 作者感谢所有参与DREAM 1、DREAM 2与DREAM 3项目（即前述DREAM项目）的学生与合作者。 引用说明 若您使用本数据集，请引用以下文献： 1. 开放数据描述、监测项目与近期数据采集策略：Gaspari, F., Barbieri, F., Fascia, R., Ioli, F., Pinto, L., & Migliaccio, F. (2025). Strategies for Glacier Retreat Communication with 3D Geovisualization and Open Data Sharing. ISPRS International Journal of Geo-Information, 14(2), 75. https://doi.org/10.3390/ijgi14020075 2. 无人机数据集：Ioli, F.; Bianchi, A.; Cina, A.; De Michele, C.; Maschio, P.; Passoni, D.; Pinto, L. Mid-Term Monitoring of Glacier’s Variations with UAVs: The Example of the Belvedere Glacier. Remote Sensing, 14, 28 (2022). https://doi.org/10.3390/rs14010028 3. 历史航空数据集：De Gaetani, C.I.; Ioli, F.; Pinto, L. Aerial and UAV Images for Photogrammetric Analysis of Belvedere Glacier Evolution in the Period 1977–2019. Remote Sensing, 13, 3787 (2021). https://doi.org/10.3390/rs13183787 4. 贝尔韦代雷冰川短期监测：Ioli, F., Dematteis, N., Giordan, D., Nex, F., Pinto, L. Deep Learning Low-cost Photogrammetry for 4D Short-term Glacier Dynamics Monitoring. PFG (2024). https://doi.org/10.1007/s41064-023-00272-w
