This dataset features a map of building types for Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. A random forest classification was used to map the predominant type of buildings within a pixel. We distinguish single-family residential buildings, multi-family residential buildings, commercial and industrial buildings and lightweight structures. Building types were predicted for all pixels where building density > 25 %. Please refer to the publication for details.

Temporal extent

Sentinel-2 time series data are from 2018. Sentinel-1 time series data are from 2017.

Data format

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 type values are categorical, according to the following scheme:

0 - No building

1 - Commercial and industrial buildings

2 - Single-family residential buildings

3 - Lightweight structures

4 - Multi-family residential buildings

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).
A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

The dataset was generated by FORCE v. 3.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission.

Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

This dataset is a categorical mapping of estimated mean building heights, by Census block group, in shapefile format for the conterminous United States. The data were derived from the NASA Shuttle Radar Topography Mission, which collected “first return” (top of canopy and buildings) radar data at 30-m resolution in February, 2000 aboard the Space Shuttle Endeavor. These data were processed here to estimate building heights nationally, and then aggregated to block group boundaries. The block groups were then categorized into six classes, ranging from “Low” to “Very High”, based on the mean and standard deviation breakpoints of the data. The data were evaluated in several ways, to include comparing them to a reference dataset of 85,000 buildings for the city of San Francisco for accuracy assessment and to provide contextual definitions for the categories.

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.

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.

Temporal extent The acquisition dates of the different data sources vary to some degree: - Independent variables: Sentinel-2 data are from 2018; Sentinel-1 data are from 2017. - Dependent variables: the 3D building models are from 2012-2020 depending on data provider. - Settlement mask: the ESM is based on a mosaic of imagery from 2014-2016. Considering that net change of building stock is positive in Germany, the building height map is representative for ca. 2015.

Data format 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.

Further information For further information, please see the publication or contact David Frantz (david.frantz@geo.hu-berlin.de). A web-visualization of this dataset is available here.

Publication 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: https://doi.org/10.1016/j.rse.2020.112128

Acknowledgements The dataset was generated by FORCE v. 3.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission. The European Settlement Mask was obtained from the European Commission. 3D building models were obtained from Berlin Partner für Wirtschaft und Technologie GmbH, Freie und Hansestadt Hamburg / Landesbetrieb Geoinformation und Vermessung, Landeshauptstadt Potsdam, Bezirksregierung Köln / Geobasis NRW, and Kompetenzzentrum Geodateninfrastruktur Thüringen. This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

Funding This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

Human encroachment into wildlands has resulted in a rapid increase in wildland–urban interface (WUI) expansion, exposing more buildings and population to wildfire risks. More frequent mapping of structures and WUIs at a finer spatial resolution is needed for WUI characterization and hazard assessment. However, most approaches rely on high-resolution commercial satellite data with a particular focus on urban areas. We developed a deep learning framework tailored for building footprint detection in the transitional wildland–urban areas. We leveraged meter scale aerial imageries publicly available from the National Agriculture Imagery Program (NAIP) every 2 years. Our approach integrated Mobile-UNet and generative adversarial network. The deep learning models trained over three counties in California performed well in detecting building footprints across diverse landscapes, with an F1 score of 0.62, 0.67, and 0.75 in the interface WUI, intermix WUI, and rural regions, respectively. The bi-annual mapping captured both housing expansion and wildfire-caused building damages. The 30 m WUI maps generated from these finer footprints showed more granularity than the existing census tract-based maps and captured the transition of WUI dynamics well. More frequent updates of building footprint and improved WUI mapping will improve our understanding of WUI dynamics and provide guidance for adaptive strategies on community planning and wildfire hazard reduction.

Download In State Plane Projection Here. The pavement boundaries were traced from aerial photography taken between April 13 and April 26, 2002 and then updated from photography taken between March 15 and April 25, 2018. This dataset should meet National Map Accuracy Standards for a 1:1200 product. Lake County staff reviewed this dataset to ensure completeness and correct classification. In the case of a divided highway, the pavement on each side is captured separately. Island features in cul-de-sacs and in roads are included as a separate polygon.These building outlines were traced from aerial photography taken between April 13 and April 26, 2002 and then updated from successive years of photography. The most recent aerial photography was flown between March 11 and April 12, 2017. This dataset should meet National Map Accuracy Standards for a 1:1200 product. All the enclosed structures in Lake County with an area larger than 100 square feet as of April 2014 should be represented in this coverage. It should also be noted that a single polygon in this dataset could be composed of many structures that share walls or are otherwise touching. For example, a shopping mall may be captured as one polygon. Note that the roof area boundary is often not identical to the building footprint at ground level. Contributors to this dataset include: Municipal GIS Partners, Inc., Village of Gurnee, Village of Vernon Hills.

The Bing Maps team at Microsoft released a U.S.-wide vector building dataset in 2018, which includes over 125 million building footprints for all 50 states in GeoJSON format. This dataset is extracted from aerial images using deep learning object classification methods. Large-extent modelling (e.g., urban morphological analysis or ecosystem assessment models) or accuracy assessment with vector layers is highly challenging in practice. Although vector layers provide accurate geometries, their use in large-extent geospatial analysis comes at a high computational cost. We used High Performance Computing (HPC) to develop an algorithm that calculates six summary values for each cell in a raster representation of each U.S. state: (1) total footprint coverage, (2) number of unique buildings intersecting each cell, (3) number of building centroids falling inside each cell, and area of the (4) average, (5) smallest, and (6) largest area of buildings that intersect each cell. These values are represented as raster layers with 30m cell size covering the 48 conterminous states, to better support incorporation of building footprint data into large-extent modelling. This Project is funded by NASA’s Biological Diversity and Ecological Forcasting program; Award # 80NSSC18k0341

Building footprint means the perimeter of a building at the outer edge of the outside walls of the building. Generated with digitizing of 2014 aerial imagery. Anticipated update 2021-2022. 1. Restriction on the use of Material on this websiteUsage and/or downloading this data indicates Your acceptance of the terms and conditions below.The data here controlled and operated by the Corporation of the County of Lennox and Addington (referred to the “County” herein) and is protected by copyright. No part of the information herein may be sold, copied, distributed, or transmitted in any form without the prior written consent of the County. All rights reserved. Copyright 2023 by the Corporation of the County of Lennox and Addington.2. DisclaimerThe County makes no representation, warranty or guarantee as to the content, accuracy, currency or completeness of any of the information provided on this website. The County explicitly disclaims any representations, warranties and guarantees, including, without limitation, the implied warranties of merchantability and fitness for a particular purpose.3. Limitation of LiabilityThe County is not responsible for any special, indirect, incidental or consequential damages that may arise from the use of or the inability to use, any web pages and/or the materials contained on the web page whether the materials are provided by the County or by a third party. Without limiting the generality of the foregoing, the County assumes no responsibility whatsoever for: any errors omissions, or inaccuracies in the information provided, regardless of how caused; or any decision made or action taken or not taken by the reader or other third party in reliance upon any information or data furnished on any web page.The Data is provided "as is" without warranty or any representation of accuracy, timeliness or completeness. The burden for determining accuracy, completeness, timeliness, merchantability and fitness for or the appropriateness for use rests solely on the requester. Lennox and Addington County makes no warranties, express or implied, as to the use of the Data. There are no implied warranties of merchantability or fitness for a particular purpose. The requester acknowledges and accepts the limitations of the Data, including the fact that the Data is dynamic and is in a constant state of maintenance, corrections and update.

The global construction mapping services market is experiencing robust growth, driven by increasing urbanization, infrastructure development projects, and the widespread adoption of advanced technologies like drones and LiDAR. The market is segmented by application (before, during, and after construction) and type of surveying (aerial and terrestrial). The "during construction" application segment is projected to dominate due to its crucial role in monitoring progress, ensuring accuracy, and mitigating risks throughout the construction lifecycle. Aerial surveying, leveraging drones and advanced sensors, is witnessing significant traction, offering cost-effective and efficient data acquisition compared to traditional terrestrial methods. This trend is further fueled by the decreasing cost of drone technology and improved data processing capabilities. Key market players are continuously innovating and investing in solutions that integrate various data sources, providing clients with comprehensive, high-resolution 3D models and insightful analytics for informed decision-making. The market is also witnessing increased demand for integrated solutions combining surveying, modeling, and project management software, creating opportunities for synergistic partnerships and service offerings. Despite the positive growth trajectory, certain restraints exist. These include the initial high investment costs associated with advanced technologies, concerns regarding data security and privacy, and the need for skilled professionals proficient in data acquisition and analysis. However, these challenges are being addressed through advancements in technology, the development of user-friendly software, and the increasing availability of training and educational programs. The market is expected to exhibit a steady CAGR throughout the forecast period (2025-2033), primarily driven by the ongoing growth in infrastructure spending globally and the increasing adoption of Building Information Modeling (BIM) methodologies. Regional growth will vary, with North America and Europe maintaining a significant market share due to established infrastructure and technology adoption, but with strong growth prospects in Asia-Pacific driven by rapid urbanization and industrialization. To illustrate, let's assume a 2025 market size of $10 billion, a conservative estimate given the rapid growth in the sector. With a reasonable CAGR of 8%, this implies significant expansion throughout the forecast period.

This dataset is built from the Overture 2023-07-26-alpha.0 version of open map data by the Overture Maps Foundation. This dataset compiles building footprints and their attributes for individual cities for convenient and lightweight spatial analytics.Credits: Overture Maps FoundationLicense: https://opendatacommons.org/licenses/odbl/

Area layers of US, Australia, and Canada building footprints for use with GIS mapping software, databases, and web applications.

This paper uses data on residential buildings from the nationwide vector database. Attribute information on buildings (location, volume, function, etc.) provides opportunities to estimate the number of residents. The recalculation of the population from the urban units into new spatial units was based on the area-weighted aggregation method. The location of buildings constituted a limiting variable, while the total square meterage (calculated as the area of the buildings and the number of their floors) constituted the binding variable. The introduction of additional binding variables related to the type of building and its location, as well as various methods of determining the square meterage per building type, resulted in the creation of a total of 19 maps of Cracow’s population. The results of the recalculation of population were related to demographic data compiled by the organisation Statistics Poland (GUS) relating to the 1x1 km grid. Comparison of the results with demographic data relating to other reference units allowed the reduction of subjective interpretation and the refining of input data conversion methods. As a result, correct methods for segmenting buildings were identified, useful optimisation criteria were selected, and the accuracy of population maps developed based on the database was calculated. For the input data, based solely on the amount of population in urban units, the calculated value of the mean absolute percentage error (MAPE) in the 1x1 km grid was 310.8%, and for the root mean square error (RMSE) was 1476 people. In the dasymetric method, directly associating the population with the volume of buildings, the errors fell to 21.9% and 632 people, respectively. Among the remaining 18 variants introducing the segmentation of buildings from the database, the best result was obtained for the variant based on minimizing the RMSE, associating the number of residents to single-family buildings (2.88 people/building) and associating the number of residents to the square footage in multi-family buildings (37.1m2/person) (MAPE=19.2%, RMSE=556 people).

Polygon layer representing the physical area of buildings. Building polygon collection is split into two “classes”. Simple buildings are buildings which are 3 storeys or less. These building polygons will be collected at the outer extents of the roof. Complex buildings are collected as multiple overlapping polygons representing the extents of individual roof elevations. Rooftop structures (i.e. Elevator rooms, air conditioning structures) may be collected as well. These polygons Part of the City’s topographic mapping products, the data is collected from high resolution aerial photography. The data is a representation of the physical features that are visually identifiable in an aerial photograph.

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

A relatively permanent walled and roofed construction.

Data Dictionary for building_pnt: https://docs.topo.linz.govt.nz/data-dictionary/tdd-class-building_pnt.html

This layer is a component of the Topo250 map series. The Topo250 map series provides topographic mapping for the New Zealand mainland and Chatham Islands, at 1:250,000 scale.

Further information on Topo250: http://www.linz.govt.nz/topography/topo-maps/topo250

Wildland-urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated due to the lack of building data. Convolutional neural networks (CNNs) can extract building locations from remote sensing data, but their accuracy in WUI areas is unknown. Additionally, CNNs are computationally intensive and technically complex making it challenging for end-users, such as those who use or create WUI maps, to apply. We identified buildings pre- and post-wildfire and estimated building destruction for three California wildfires: Camp, Tubbs, and Woolsey. We used a CNN model from Esri to detect buildings from high-resolution imagery. This dataset represents the state-of-the-art of what is readily available for potential WUI mapping.

This web map features a vector basemap of OpenStreetMap (OSM) data created and hosted by Esri. Esri produced this vector tile basemap in ArcGIS Pro from a live replica of OSM data, hosted by Esri, and rendered using a creative cartographic style emulating a blueprint technical drawing. The vector tiles are updated every few weeks with the latest OSM data. This vector basemap is freely available for any user or developer to build into their web map or web mapping apps.OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this new vector basemap available available to the OSM, GIS, and Developer communities.

The market for map drawing services is projected to grow from XXX million in 2025 to XXX million by 2033, at a CAGR of XX%. This growth is attributed to the increasing demand for accurate and detailed maps for various applications, including navigation, urban planning, and environmental monitoring. The rising adoption of GIS technology and the proliferation of mobile devices have further fueled the demand for map drawing services. The market is segmented by type (indoor mapping and outdoor mapping) and application (factory and other). Indoor mapping services are expected to witness significant growth, driven by the increasing adoption of indoor navigation systems in commercial and public spaces. Outdoor mapping services are also expected to grow, driven by the increasing demand for detailed maps for urban planning, environmental monitoring, and disaster management. The market is highly fragmented, with a number of small and medium-sized players. However, there are a few large players, such as BUILDING MAPS, Old Dominion Map. Co., and North 45 West Inc., that hold a significant market share.

Building structures include parking garages, ruins, monuments, and buildings under construction along with residential, commercial, industrial, apartment, townhouses, duplexes, etc. Buildings equal to or larger than 9.29 square meters (100 square feet) are captured. Buildings are delineated around the roof line showing the building "footprint." Roof breaks and rooflines, such as between individual residences in row houses or separate spaces in office structures, are captured to partition building footprints. This includes capturing all sheds, garages, or other non-addressable buildings over 100 square feet throughout the city. Atriums, courtyards, and other “holes” in buildings created as part of demarcating the building outline are not part of the building capture. This includes construction trailers greater than 100 square feet. Memorials are delineated around a roof line showing the building "footprint."Bleachers are delineated around the base of connected sets of bleachers. Parking Garages are delineated at the perimeter of the parking garage including ramps. Parking garages sharing a common boundary with linear features must have the common segment captured once. A parking garage is only attributed as such if there is rooftop parking. Not all rooftop parking is a parking garage, however. There are structures that only have rooftop parking but serve as a business. Those are captured as buildings. Fountains are delineated around the base of fountain structures.

This web map shows the building ownership (owned or leased) of each building on the MIT campus.

When you build with TomTom Maps APIs and map data sets, you build with a partner that combines three decades of mapping experience with the speed and soul of a start-up. We’re proud of our roots, and we never stop looking ahead – working together with you to bring the best, freshest map data and tech to people all over the world. When change happens in the real world, our transactional mapmaking ecosystem allows us to detect, verify and deliver it to the map fast – ensuring your customers, drivers and users always enjoy the most up-to-date map data. That same speed and flexibility extends to how we help you build your mapping app: You’re in control of your map data, choosing what you want to include in your final product.