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18 results for “Building height”
Thermal Bridges on Building Rooftops - Hyperspectral (RGB + Thermal + Height) drone images of Karlsruhe, Germany, with thermal bridge annotations
<p><strong>Overview:</strong></p> <p>The dataset of <strong>Thermal Bridges on Building Rooftops (TBBR dataset)</strong> consists of annotated combined RGB and thermal drone images with a height map. All images were converted to a uniform format of 3000x4000 pixels, aligned, and cropped to <strong>2680x3370</strong> to remove empty borders. See the "Usage" section below for details about the stored formats made available here.</p> <p>The raw images for our dataset were recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times.</p> <p>All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings.</p> <p>The dataset contains <strong>926 images</strong> with a total of <strong>6,927 annotations</strong> of thermal bridges on rooftops, split into train and test subsets with 723 (5,614) and 203 (1,313) images (annotations), respectively. The annotations only include thermal bridges that are visually identifiable with the human eye. Because of the aforementioned image overlap, each thermal bridge is annotated multiple times from different angles.</p> <p>For the annotation of the thermal images the image processing program <em>VGG Image Annotator </em>from the Visual Geometry Group, version 2.0.10, was used. The thermal bridge annotations are outlined with polygon shapes. These polygon lines were placed as close as possible but outside the area of significant temperature increase. If a detected thermal bridge was partially covered by another building component located in the foreground, the thermal bridge was also marked across the covering in case of minor coverings. Adjacent thermal bridges, which affect different rooftop components, were annotated separately. For example, a window with poor insulation of the window reveal located in the area of a poorly insulated roof is annotated individually. There is no overlap between annotated areas. While each image contains annotations, they also include thermal bridges present that are not annotated.</p> <p><strong>Usage:</strong></p> <p>Each compressed archive file represents one of the six flight paths. For the related publication the final path (Flug1_105Media) was used as a hold-out test sample. The archives contain Numpy files (one per image) of shape (2680, 3370, 5), where the final dimension is the colour channel in the format [B, G, R, Thermal, Height].</p> <p>Archives were compressed using <a href="https://facebook.github.io/zstd/">ZStandard</a> compression. They can be decompressed in a terminal by running e.g.</p> <pre><code class="language-bash">tar -I zstd -xvf Flug1_105Media.tar.zst</code></pre> <p>these will be decompressed into the file structure:</p> <pre><code>images/ └── Flug1_105Media/ └── DJI_0004_R.npy └── DJI_0006_R.npy └── ...</code></pre> <p>Corresponding annotations are provided in the COCO JSON format. There is one file for training (Flug1_100Media - Flug1_104Media blocks) and one for test (Flug1_105Media block). They contain a single class (thermal bridge) and expect the folder structure shown below.</p> <p>Note: The annotation files contain <em>relative</em> paths to numpy files, in case of problems please convert to <em>absolute</em> paths (i.e. insert the containing directory before each file path in the JSON annotation files).</p> <p>We provide the <a href="https://github.com/Helmholtz-AI-Energy/TBBRDet"><strong>TBBRDet software</strong></a> which includes a dataloader and dataset inspection tools which make use of the <a href="https://github.com/facebookresearch/detectron2">Detectron2</a> and <a href="https://github.com/open-mmlab/mmdetection">MMDetection</a> libraries.</p> <p>We recommend the following folder structure for use:</p> <pre><code>├── train/ │ ├── Flug1_100-104Media_coco.json │ └── images/ │ ├── Flug1_100Media/ │ │ ├── DJI_XXXX_R.npy │ │ └── ... │ ├── ... │ └── Flug1_104Media/ │ ├── DJI_XXXX_R.npy │ └── ... └── test/ ├── Flug1_105Media_coco.json └── images/ └── Flug1_105Media/ ├── DJI_XXXX_R.npy └── ...</code></pre> <p><strong>Metadata:</strong></p> <p>The experimental metadata was structured with the <strong>Spatio Temporal Asset Catalog (STAC)</strong> specification family. This specification provides a standardized way for describing geospatial assets. It defines related JSON object types of Item, Catalog, and Catalog, extending on Collection as the basis.</p> <p>One STAC Collection JSON object provides information about the recorded images and the environmental conditions during recordings. It also contains information about the overall bounding box of the entire area in which images were recorded.</p> <p>This object links to related STAC Item JSON objects containing information about the recorded city blocks and the cameras. The objects for the city blocks contain the GeoJSON geometry of the respective block and the<br> corresponding bounding box. The objects containing the camera information are based on an existing STAC extension for camera related metadata.</p> <p>Metadata of the archived NumPy files for each image was structured using the <strong>Data Package</strong> schema from the <strong>Frictionless Standards</strong>. This standard describes a collection of data files. Therefore, metadata about all containerized NumPy files of the six flight paths (Flug1_100Media - Flug1_104Media blocks and Flug1_105Media block) is provided within a JSON-based file.</p> <p>Note that <strong>camera1</strong> corresponds to the <strong>RGB camera</strong> and <strong>camera2</strong> the <strong>thermal</strong>.</p> <p><strong>FAIR Digital Objects:</strong></p> <p>All files are represented in a standardized way as <strong>FAIR Digital Objects<br> (FAIR DOs)</strong> to enable machine actionable decisions on the data in spirit of<br> the FAIR principles.</p> <p><strong>Persistent Identifier (PID):</strong></p> <p>Persistent Identifiers (PIDs) are resolvable with the <a href="https://hdl.handle.net/">Handle.Net Registry (HNR)</a>.</p> <table> <thead> <tr> <th scope="col">File</th> <th scope="col">Persistent Identifier (PID)</th> </tr> </thead> <tbody> <tr> <td>Flug1_100-104Media_coco.json</td> <td>21.11152/6ea60288-d895-414e-80c0-26c9fdd662b2</td> </tr> <tr> <td>Flug1_105Media_coco.json</td> <td>21.11152/58d43ddc-5e29-4980-8675-ae579b50a1e2</td> </tr> <tr> <td>Flug1_100.tar.zst</td> <td>21.11152/6858a0b5-cc60-40e9-afef-8c2dd8b35e8e</td> </tr> <tr> <td>Flug1_101.tar.zst</td> <td>21.11152/e670f510-7e00-4d3a-9b90-3bac7a7c069e</td> </tr> <tr> <td>Flug1_102.tar.zst</td> <td>21.11152/3ab9f444-05f6-445e-a691-62fae4021bea</td> </tr> <tr> <td>Flug1_103.tar.zst</td> <td>21.11152/365fd8cf-8e86-41b8-9d0e-b816fdd01d29</td> </tr> <tr> <td>Flug1_104.tar.zst</td> <td>21.11152/041a6111-644a-4617-afb3-3c421a88e8e3</td> </tr> <tr> <td>Flug1_105.tar.zst</td> <td>21.11152/f48bf4e7-3879-4216-8f64-45a060b8f658</td> </tr> <tr> <td>Flug1_100-105_frictionless_standards.json</td> <td>21.11152/7b58b3b5-75eb-4417-ac4d-abe025e159f6</td> </tr> <tr> <td>Flug1_collection_stac_spec.json</td> <td>21.11152/ba370aa3-6422-428c-9ff7-c2ef429df603</td> </tr> <tr> <td>Flug1_100_stac_spec.json</td> <td>21.11152/09cb76fc-b8cb-4116-a22a-68c5bdfa77b0</td> </tr> <tr> <td>Flug1_101_stac_spec.json</td> <td>21.11152/24a55398-b96b-43dd-b0fb-cd8ce302c7ce</td> </tr> <tr> <td>Flug1_102_stac_spec.json</td> <td>21.11152/721234ac-4b5a-4d02-9944-82a08ef2db35</td> </tr> <tr> <td>Flug1_103_stac_spec.json</td> <td>21.11152/ebaeb5bc-0514-47c9-bcd2-98f0253843d8</td> </tr> <tr> <td>Flug1_104_stac_spec.json</td> <td>21.11152/9854677c-77c5-4a0b-916b-57dd9ec20198</td> </tr> <tr> <td>Flug1_105_stac_spec.json</td> <td>21.11152/cfd0fc0e-f5ea-464e-a57f-28e882924860</td> </tr> <tr> <td>Flug1_camera1_stac-spec.json</td> <td>21.11152/976fcf28-f924-4a21-b53d-5d054ad8198d</td> </tr> <tr> <td>Flug1_camera2_stac-spec.json</td> <td>21.11152/37833c54-1d36-42e4-858d-831447122863</td> </tr> </tbody> </table>
CNBH-10 m: A first Chinese building height at 10 m resolution
<p>Building height is a crucial variable in the study of urban environments, regional climates, and human-environment interactions. However, high-resolution data on building height, especially at the national scale, are limited. Fortunately, high spatial-temporal resolution earth observations, harnessed using a cloud-based platform, offer an opportunity to fill this gap. We describe an approach to estimate 2020 building height for China at 10 m spatial resolution based on all-weather earth observations (radar, optical, and night light images) using the Random Forest (RF) model. Results show that our building height simulation has a strong correlation with real observations at the national scale (RMSE of 6.1 m, MAE = 5.2 m, <em>R</em> = 0.77). The Combinational Shadow Index (CSI) is the most important contributor (15.1%) to building height simulation. Analysis of the distribution of building morphology reveals significant differences in building volume and average building height at the city scale across China. <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/macao">Macau</a> has the tallest buildings (22.3 m) among Chinese cities, while Shanghai has the largest building volume (298.4 10<sup>8</sup> m<sup>3</sup>). The strong correlation between modelled building volume and socio-economic parameters indicates the potential application of building height products. The building height map developed in this study with a resolution of 10 m is open access, provides insights into the 3D morphological characteristics of cities and serves as an important contribution to future urban studies in China.</p>
Building height map of Germany
<p>Urban areas have a manifold and far-reaching impact on our environment, and the three-dimensional structure is a key aspect for characterizing the urban environment. </p> <p>This dataset features a map of building height predictions for entire Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. We utilized machine learning regression to extrapolate building height reference information to the entire country. The reference data were obtained from several freely and openly available 3D Building Models originating from official data sources (building footprint: cadaster, building height: airborne laser scanning), and represent the average building height within a radius of 50m relative to each pixel. Building height was only estimated for built-up areas (European Settlement Mask), and building height predictions <2m were set to 0m.</p> <p><strong>Temporal extent</strong><br> The acquisition dates of the different data sources vary to some degree:<br> - Independent variables: Sentinel-2 data are from 2018; Sentinel-1 data are from 2017.<br> - Dependent variables: the 3D building models are from 2012-2020 depending on data provider.<br> - Settlement mask: the ESM is based on a mosaic of imagery from 2014-2016.<br> Considering that net change of building stock is positive in Germany, the building height map is representative for ca. 2015. </p> <p><strong>Data format</strong><br> The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building height values are in meters, scaled by 10, i.e. a pixel value of 69 = 6.9m.</p> <p><strong>Further information</strong><br> For further information, please see the publication or contact David Frantz (david.frantz@geo.hu-berlin.de).<br> A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/building-height/">here</a>.</p> <p><strong>Publication</strong><br> Frantz, D., Schug, F., Okujeni, A., Navacchi, C., Wagner, W., van der Linden, S., & Hostert, P. (2021). National-scale mapping of building height using Sentinel-1 and Sentinel-2 time series. Remote Sensing of Environment, 252, 112128. DOI: <a href="https://doi.org/10.1016/j.rse.2020.112128">https://doi.org/10.1016/j.rse.2020.112128</a></p> <p><strong>Acknowledgements</strong><br> The dataset was generated by FORCE v. 3.1 (<a href="https://doi.org/10.3390/rs11091124">paper</a>, <a href="https://github.com/davidfrantz/force">code</a>), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the <a href="https://scihub.copernicus.eu/">European Space Agency and the European Commission</a>. The European Settlement Mask was obtained from the <a href="https://data.jrc.ec.europa.eu/dataset/8bd2b792-cc33-4c11-afd1-b8dd60b44f3b">European Commission</a>. 3D building models were obtained from <a href="https://www.businesslocationcenter.de/en/economic-atlas/download-portal/">Berlin Partner für Wirtschaft und Technologie GmbH</a>, <a href="http://suche.transparenz.hamburg.de/dataset/3d-stadtmodell-lod2-de-hamburg4?forceWeb=true">Freie und Hansestadt Hamburg / Landesbetrieb Geoinformation und Vermessung</a>, <a href="https://opendata.potsdam.de/explore/dataset/3d-gebaudemodell-lod2-citygml/information">Landeshauptstadt Potsdam</a>, <a href="https://www.bezreg-koeln.nrw.de/brk_internet/geobasis/3d_gebaeudemodelle/index.html">Bezirksregierung Köln / Geobasis NRW</a>, and <a href="https://www.geoportal-th.de/de-de/Downloadbereiche/Download-Offene-Geodaten-Th%C3%BCringen/Download-3D-Geb%C3%A4ude">Kompetenzzentrum Geodateninfrastruktur Thüringen</a>. This dataset was partly produced on <a href="https://eodc.eu">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.</p> <p><strong>Funding</strong><br> This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (<a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">MAT_STOCKS</a>, grant agreement No 741950).</p>
Estimated height of the OpenStreetMap buildings of 24 French communes using the GeoClimate Software (version 0.0.1)
<p>This repository contains:</p> <ul> <li>a folder called "_toReproduceResults" containing data, script and methodology to reproduce most of the work described in the research manuscript,</li> <li>the main output of the research work: 24 folders (each of them corresponding to a French city), containing building geometry footprints and their corresponding building height as well as averaged building height value aggregated at rectangular grid cell (100 m by 100 m). The footprint geometries comes from the OpenStreetMap project and the building height has been estimated using a RandomForest algorithm using as independent variables indicators describing the building size and shape and the building environment. The data has been produced using the GeoClimate Software (version 0.0.1).</li> </ul> <p>A more detailed description of the content can be used in the file "Metadata.csv".</p>
Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel
<p>Raw data associated with a paper submission.<br> " Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel" submitted to Additive Manufacturing.</p> <p>Contained are all the raw images used in figures, as well as csv's of any data pltoted in graphs.</p> <p>Raw images captured during printing of various processing parameters<br> EBSD scans (.ctf) of all disucssed samples </p> <p>Wall definitions (EBSD compared to paper)<br> Wall 1 - Wall A1 300 W 2750 mm/s<br> Wall 2 - Wall D 500 W 2250 mm/s<br> Wall 3 - Wall B 300 W 2250 mm/s<br> Wall 4 - Wall C 500 W 2750 mm/s<br> Wall 5 - Wall A2 300 W 2750 mm/s</p>
GLObal Building heights for Urban Studies (UT-GLOBUS)
<h1><strong>Important note: If you get a message that .zip archive is corrupt, please try updating WinRAR or <em>right-click the folder and</em> <em>select Extract All on Windows or use unzip command on Linux terminal</em>. If the issue persists, email: kamath.harsh@utexas.edu</strong></h1> <p> </p> <p><strong>Abstract</strong> </p> <p>We introduce GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for major cities worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse resolution urban canopy elevation data with a random forest model to estimate building-level information. Validation using LiDAR data from six U.S. cities showed UT-GLOBUS-derived building heights had an RMSE of 9.1 meters, and mean building height within 1-km² grid cells had an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (~55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset’s effectiveness in modeling human thermal comfort at Baltimore, MD (daytime RMSE = 2.85°C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and ecological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.</p> <p><strong>Data</strong></p> <p>We are also supplying a vector file to represent the data coverage, and this file will receive updates as data for new city is added. Building-level data is accessible in vector file format (GeoPackage: .gpkg), which can be converted into raster file format (geoTIFF). These formats are compatible with the SUEWS and SOLWEIG models for the simulation of urban energy balance and thermal comfort. The vector files employ the Universal Transverse Mercator (UTM) projection. Both the vector and raster files are compatible with GIS platforms like QGIS and ArcGIS and can be imported for analysis using programming languages such as Python. We are also providing UCPs required by the BEP-BEM urban model in the urban WRF system in binary file format. Additionally, we provide the urban fractions calculated using ESA world cover dataset (https://esa-worldcover.org/en) for WRF model in binary file format. These files can be directly incorporated into the WRF pre-processing system (WPS). The UT-GLOBUS UCPs are determined using a moving kernel with a size of 1 km2 and spacing of 300 meters in both the X and Y directions</p> <p><strong>Data coverage</strong></p> <p>The 'Coverage_xxxx.gpkg' files provide that geographical extents of cities that are included in our dataset.</p> <p><strong>How to find your city in the UT-GLOBUS dataset</strong></p> <p>Open the 'coverage' geopackage (.gpkg) files in QGIS or ArcGIS. Click on the city polygons and get the 'Label'/City name. Find a folder with the same 'Label'/City name. All the data for the periticular city will be in the folder.</p> <p><strong>How to run BEP-BEM model in WRF using UT-GLOBUS urban canopy parameters</strong></p> <div>Step 0: Before compiling WRF, go to 'dyn_em' folder and open 'module_initialize_real.F'.</div> <div>Change line 3121 (in version 4.5.2): </div> <div>From </div> <div>grid%HI_URB2D(i,k,j) = grid%URB_PARAM(i,k+117,j) </div> <div>To</div> <div>grid%HI_URB2D(i,k,j) = grid%URB_PARAM(i,k+117,j)*100.</div> <div> </div> <div>1. Change the name of the binary files 'ufrac' and 'urb_param' inside 'urb_fra' and 'GLOBUS_morph' folders, respectively to 00001-tile_x.00001-tile_y.</div> <div>Values for tile_x and tile_y can be found in the index file inside the 'urb_fra' and 'GLOBUS_morph' folders. Make sure to append zeros before tile_x and tile_y values to make 5 digits. </div> <div>Ex: tile_x = 260 and tile_y = 219; Then the binary files should be renamed as 00001-00260.00001-00209 </div> <div> </div> <div>2. Copy the 'urb_fra' and 'GLOBUS_morph' folders to WRF static data directory.</div> <div> </div> <div>3. Change the paths to 'URB_PARAM' and 'FRC_URB2D' variables inside GEOGRID.TBL file as follows:</div> <div> </div> <div>===============================</div> <div>name=URB_PARAM</div> <div> priority=1</div> <div> optional=yes</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> z_dim_name=num_urb_params</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/GLOBUS_morph/</div> <div> flag_in_output=FLAG_URB_PARAM</div> <div>===============================</div> <div>name=FRC_URB2D</div> <div> priority=1</div> <div> optional=yes</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/urb_fra/</div> <div> flag_in_output=FLAG_FRC_URB2D</div> <div>===============================</div> <div> </div> <div>4. Run geogrid.exe. If the domain covers the chosen city:</div> <div> -- 'FRC_URB2D' variable will show the urban fraction.</div> <div> -- 'URB_PARAM[91,:,:]' will show the plan area fraction.</div> <div> -- 'URB_PARAM[94,:,:]' will show the area averaged building heights.</div> <div> -- 'URB_PARAM[95,:,:]' will show the building surface to total area fraction.</div> <div> -- 'URB_PARAM[118-132,:,:]' will show the building height histograms with 5-meter bin size.</div> <div> </div> <div>5. If you see the data in 'FRC_URB2D' and 'URB_PARAM' variables after running the geogrid.exe, GLOBUS data is ingested in WPS and you can continue with ungrib and metgrid as usual.</div> <div> </div> <div>6. For running the model over the domain area which covers more that one city, UT-GLOBUS UCPs can be stitched together. For instance, if two cities are covered in the domain, step number 3 should be modified as follows:</div> <div> </div> <div>===============================</div> <div>name=URB_PARAM</div> <div> priority=1</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> z_dim_name=num_urb_params</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path=Your_WPS_static_data_folder/GLOBUS_morph_for_city-1/ </div> <div>flag_in_output=FLAG_URB_PARAM</div> <div>===============================</div> <div>name=FRC_URB2D</div> <div> priority=1</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/urb_fra_for_city-1/</div> <div> flag_in_output=FLAG_FRC_URB2D</div> <div>===============================</div> <div>name=URB_PARAM</div> <div> priority=2</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> z_dim_name=num_urb_params</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/GLOBUS_morph_for_city-2/</div> <div>===============================</div> <div>name=FRC_URB2D</div> <div> priority=2</div> <div> dest_type=continuous</div> <div> fill_missing = 0.</div> <div> interp_option=default:nearest_neighbor</div> <div> abs_path= Your_WPS_static_data_folder/urb_fra_for_city-2/</div> <div>===============================</div> <div> </div> <div><strong>References</strong></div> <div> <ol> <li>Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Liu, Z., Berner, J., Wang, W., Powers, J., Duda, M., Barker, D., Huang, X., 2021. A Description of the advanced research WRF model.</li> <li>Martilli, A., Clappier, A., Rotach, M.W., 2002. An urban surface exchange parameterisation for mesoscale models. Boundary Layer Meteorol 104, 261–304. https://doi.org/10.1023/A:1016099921195</li> <li>Sun, T., Grimmond, S., 2019. A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): Development, deployment and demonstration. Geosci Model Dev 12, 2781–2795. https://doi.org/10.5194/gmd-12-2781-2019</li> <li>Lindberg, F., Holmer, B., Thorsson, S., 2008. SOLWEIG 1.0 - Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. Int J Biometeorol 52, 697–713. https://doi.org/10.1007/s00484-008-0162-7</li> <li>Software: QGIS (https://www.qgis.org/en/site/)</li> </ol> </div>
Vegetation and Building Heights for Northampton and Accomac Counties, Virginia 2010
This dataset contains heights of vegetation and buildings above ground level as indicated by the 2010 LiDAR from the Virginia Information Technology Agency (VITA) (datasets: VCR12202 [knb-lter-vcr.202] and VCR12195 [knb-lter-vcr.203]). Ground levels were subtracted from the maximum elevations reported in the LiDAR point cloud to create a layer containing vegetation and structure heights above the ground. Some of the structures recorded include radio an cell towers, so there is a wide range of elevations represented, but most are less than 40 meters tall. Data are projected in UTM zone 18N, using the WGS84 datum, and with heights above ground-level in meters.Â
Vegetation and Building Heights for Accomack and Northampton Counties, Virginia, April 2015
This dataset gives the aboveground height of vegetation and buildings in Northampton and Accomack Counties, VA in April 2015. LiDAR data were collected April 11-24, 2015. A mosaiced DEM of ground elevations was assembled from DEM tiles from USGS. A LAS dataset of the classified LiDAR point cloud was assembled and a DEM of first-returns (representing the top of vegetation of buildings) created. The ground-elevation DEM was subtracted from the first-return DEM to yeild a difference layer that reflects the height of the vegetation or buildings above the ground level. Areas classified as Ponds, Lakes or Tidal areas were all set to 0 to avoid artifacts caused by waves etc. The resulting DEM was reprojected from State Plane to UTM coordinates and elevation units were changed from US Feet to Meters. The data contains some odd values in the seaside portion of the data. Heights > 10 meters in marsh areas are highly suspect.
The correlates of intraspecific variation in nest height and nest building duration in the Eurasian blue tit Cyanistes caeruleus
<p>Birds build nests primarily as a receptacle to lay their eggs in, but they can also provide secondary benefits including structural support, camouflage, and adjustment of the microclimate surrounding the eggs and offspring. The factors underlying intraspecific variation in nest characteristics are poorly understood. In this study, we aim to identify the environmental factors that predict nest height variation and the duration of nest building in blue tits (<em>Cyanistes caeruleus</em>), evaluating latitude, elevation, temperature, and the timing of egg-laying as predictors of nest height, while also taking into account female and male parental identity. Using 713 nest height observations collected over a period of five years along a 220km transect in Scotland, we found that if the annual mean timing of egg-laying was earlier, nests were taller. However, there was no correlation between nest height and elevation, latitude, the minimum temperature in the 14 days pre-egg-laying or the phenology of birds within a year. Female parental identity accounted for a large amount of variation in nest height, suggesting that individual behaviour has an influence on nest structure. We also found that nest building duration was shorter in years when egg laying occurred earlier in the year, and that across all observations taller nests took longer to build. Overall, our results show that blue tits are able to alter their nest characteristics based on conditions environmental gradients like latitude (in the case of building duration) and the annual mean phenological variation timing of egg laying, and that birds build relatively taller nests faster.</p>
Reference Datasets for: SHAFTS (v2022.3): a deep-learning-based Python package for Simultaneous extraction of building Height And FootprinT from Sentinel Imagery
<p>These are reference building height and footprint datasets which consist of 46 cities worldwide and support the development of SHAFTS (https://github.com/LllC-mmd/3DBuildingInfoMap).</p> <p>The snapshot of original reference datasets from 46 cities and related GitHub repository has been created as a zipped file named <strong><em>SHAFTS_220527_snapshot.zip</em></strong>.</p> <p>On 2022.5.27, we added 8 additional cities from ArcGIS Hub when compared with the previous version (https://doi.org/10.5281/zenodo.6370003).</p>
ETHOS.BUILDA: Building Footprint and Height Dataset Germany
<h2>Introduction</h2> <p>This dataset contains all buildings in Germany with their footprint polygon and height. It is a partial dump of the ETHOS.BUILDA database (version v7_20240429). ETHOS.BUILDA is a database containing building-level data for the German building stock. It is based on various data sources that are combined and enriched with machine learning approaches to generate one consistent and complete building dataset. </p> <p>ETHOS.BUILDA is made available under the <a href="http://opendatacommons.org/licenses/odbl/1.0" target="_blank" rel="noopener">Open Database License (ODbL)</a>. The licenses of the contents of the database depend on the data source. The sources of the building attributes and information on the type of processing that was done to assign the information from the raw data to the building in ETHOS.BUILDA are provided for each individual data point.</p> <h2>Data structure and file overview</h2> <p>Building data is provided per federal state, the files are named according to the <a href="https://en.wikipedia.org/wiki/NUTS_statistical_regions_of_Germany" target="_blank" rel="noopener">NUTS-1</a> region names. The building data has the following fields:</p> <table> <tbody> <tr> <td><strong>field name</strong></td> <td><strong>description</strong></td> </tr> <tr> <td>ID</td> <td>unique identifier of the building</td> </tr> <tr> <td>source</td> <td>the source of the building footprint</td> </tr> <tr> <td>footprint</td> <td>footprint polygon in WKT-format, EPSG:3035</td> </tr> <tr> <td>height_m</td> <td> <p>value: height of the building in [m], </p> <p>source: source of the height data,</p> <p>lineage: height assignment method</p> </td> </tr> </tbody> </table> <p>A mapping of the abbreviations of "source" and "lineage" of individual data points to the descriptions is provided in sources.csv and lineages.csv. There is no source entry for the source "v7_model.json" in the sources.csv file, as this refers to the internally trained machine learning model and not to an external dataset.</p> <h2>Acknowledgements</h2> <p>This work was supported by the Helmholtz Association under the program "Energy System Design". </p> <p>Furthermore, the authors would like to express their gratitude to the Federal Ministry for Economic Affairs and Climate Action (BMWK.IIB4) for providing the necessary resources to conduct this study. Our research was supported by the WAAGE Grant Program (Grant No. 03EI1044/03EE 5031D), and we appreciate their financial assistance.</p>
The correlates of intraspecific variation in nest height and nest building duration in the Eurasian blue tit Cyanistes caeruleus
Open the record for dataset details and reuse information.
Building height and roof material dataset - Dominica, Pichelin and Roseau, 2018, 2024
<table style="border-collapse: collapse; margin-left: 4.8pt; margin-right: 4.8pt; width: 99%;"> <tbody> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Science Case Name</span></strong></p> </td> <td style="padding: 5pt;"> <p> Multi-hazards in the Caribbean SIDS</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Dataset Title</span></strong></p> </td> <td style="padding: 5pt;"> <p>Building height and roof material dataset - Dominica, Pichelin and Roseau, 2018, 2024</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Dataset Description</span></strong></p> </td> <td style="padding: 5pt;"> <p>Building height predictions of a monocular depth estimation network trained on UAV imageries of Pichelin (2024) and Roseau (2018) trained on Google’s Open Building 2.5D Temporal dataset ( https://sites.research.google/gr/open-buildings/temporal/) and roof material classification using the model provided in GFDRR’s Caribbean Rooftop Classification (https://github.com/GFDRR/caribbean-rooftop-classification):</p> <p>· pichelin_network_height_rooftype_predictions.gpkg <a name="OLE_LINK3"></a>– Geopackage containing building height estimation and roof material classification of Pichelin, Dominica (2024).</p> <p>· roseau_network_predicitons.gpkg – Geopackage containing building height estimation and roof material classification of Roseau, Dominica (2018).</p> <p> </p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Key Methodologies</span></strong></p> </td> <td style="padding: 5pt;"> <p>A monocular depth estimation network called MiDaS (https://pytorch.org/hub/intelisl_midas_v2/) and the roof type and material classification network of GFDRR based on ResNet-50.</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Temporal Domain</span></strong></p> </td> <td style="padding: 5pt;"> <p><span lang="NL">2018 and 2024</span></p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Spatial Domain</span></strong></p> </td> <td style="padding: 5pt;"> <p>Roseau and Pichelin, Dominica</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Key Indicators</span></strong></p> </td> <td style="padding: 5pt;"> <p>Building height and roof material</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Data Format</span></strong></p> </td> <td style="padding: 5pt;"> <p>A vector as a Geopackage (.gpkg)</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Source Data</span></strong></p> </td> <td style="padding: 5pt;"> <p>Openaerialmap UAV image of Roseau (2018), UAV images of Pichelin (2024) captured using a DJI drone, and Google’s Open Building 2.5D Temporal dataset</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Accessibility</span></strong></p> </td> <td style="padding: 5pt;"> <p><strong>10.5281/zenodo.17100331</strong></p> <p><strong>Disclaimer</strong><em> – This dataset is provided solely for research purposes. It is based on available data and has not been verified or approved by any local authority. The dataset represents the outcome of the applied methodology and research judgments, and does not reflect the official policies or positions of the authorities of Dominica. </em></p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Stakeholder Relevance</span></strong></p> </td> <td style="padding: 5pt;"> <p>The building height and roof material dataset can be used for hazard mapping and risk management. For example, they can be integrated into the digital twin environment of buildings-at-risk.</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Limitations/Assumptions</span></strong></p> </td> <td style="padding: 5pt;"> <p>Temporal alignment of images used and building height dataset the network was trained on.</p> </td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Additional Information</span></strong></p> </td> <td style="padding: 5pt;">NA</td> </tr> <tr> <td style="padding: 5pt;"> <p><strong><span lang="NL">Contact Information</span></strong></p> </td> <td style="padding: 5pt;"> <p>Bergado, John Ray (UT-ITC)</p> </td> </tr> </tbody> </table> <p> </p>
Dataset: Impact of tall buildings and building-height variability on flow within and above a complex urban canopy in London - Part I
<p>This dataset contains the first part of a series of wind-tunnel flow measurements in a 1:200 scale model of a part of the London Borough of Southwark. The data published here accompanies analyses in Hertwig et al. (2019):</p> <p>D. Hertwig, H.L. Gough, S. Grimmond, J.F. Barlow, C.W. Kent, W.E. Lin, A.G. Robins, P. Hayden (2019), "Wake Characteristics of Tall Buildings in a Realistic Urban Canopy", Boundary-Layer Meteorology, DOI: 10.1007/s10546-019-00450-7.</p> <p>Flow measurements (based on 2D Laser Doppler Anemometry) were carried out in the boundary-layer wind-tunnel facility of the Environmental Flow (EnFlo) Research Centre, University of Surrey, UK, using the EPSRC <a href="http://www.magic-air.uk/">MAGIC</a> project model domain and scale model (<a href="https://www.tandfonline.com/doi/full/10.1080/09613218.2018.1468158">Song et al., 2018</a>).</p> <p>This repository contains:</p> <ol> <li>wt_ts_part_I_readme.pdf: Meta information and description of data in 'wt_ts_part_I_data.zip'</li> <li>wt_ts_part_I_data.zip: Flow measurements (time series and statistics)</li> </ol>
Improving the local climate zone classification with building height, imperviousness, and machine learning
<p>Dataset and python script for the Local Climate Zone classification analysis used in the study. </p>
Dataset: Impact of tall buildings and building-height variability on flow within and above a complex urban canopy in London - Part II
<p>This dataset contains Part II of a series of wind-tunnel flow measurements in a 1:200 scale model of a part of the London Borough of Southwark. <a href="https://doi.org/10.5281/zenodo.2563700">Part I</a> of the dataset is also published on Zenodo. The data published here (Part II) accompanies analyses in <a href="https://doi.org/10.1039/D0FD00098A">Hertwig et al. (2020)</a>:</p> <p>D. Hertwig, S. Grimmond, S. Kotthaus, C. Vanderwel, H. Gough, M. Haefferlin, A. Robins (2020), "Variability of Physical Meteorology in Urban Areas at Different Scales: Implications for Air Quality", Faraday Discussions, DOI: 10.1039/D0FD00098A .</p> <p>Flow measurements (based on 2D Laser Doppler Anemometry) were carried out in the boundary-layer wind-tunnel facility of the Environmental Flow (EnFlo) Research Centre, University of Surrey, UK, using the EPSRC <a href="http://www.magic-air.uk/">MAGIC</a> project model domain and scale model (<a href="https://www.tandfonline.com/doi/full/10.1080/09613218.2018.1468158">Song et al., 2018</a>).</p> <p>This repository contains:</p> <ol> <li>wt_ts_part_II_readme.pdf: Meta information and description of data in 'wt_ts_part_II_data.zip'</li> <li>wt_ts_part_II_data.zip: Flow measurements (time series and statistics)</li> </ol>
High-resolution global building height map (GBHM, 10 m) developed based on Sentinel imagery
<p>Due to the limited number of files on the Zenote platform, the tif files were grouped into the zip packages by longitude. In terms of file and zip package naming, lon represents longitude, lat represents latitude, A represents the east or north latitude, B represents west or south latitude, and the numbers after A and B represent the specific longitude or latitude in the lower left corner of the file.</p> <p>To compress the size of the data, the data is scaled by a factor of 100. Therefore, the pixel values need to be divided by 100 when used, i.e., a pixel value of 1500 represents 15 m.</p>
The "Suicide Guard Rail": A Structural Intervention for Hospitals Avoid Suicides by Jumping From Heights Buildings
ClinicalTrials.gov study NCT02889731. IPD Sharing: Not stated. Countries: 0. Publications: 0.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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