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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

GIS shapefiles of all buildings and disturbance detected across Antarctica, manually digitised from Google Earth images. The data set includes point locations for Automated Weather Stations (AWS), lighthouses, flight routes, maintained traverse routes, camp and hut sites, historic sites and monuments, and sites of current and former stations where mapping was not possible.

The following provides descriptions of the attributes within the GIS layers: 'STATION' refers to the name of the Research Station or Base

'NAME' refers to a named building within a station (e.g. 'Brookes Hut' which is part of 'DAVIS' within the 'STATION' attributes.

'Ice_free' refers to if a building is located on ice or in an ice-free environment '0' = a building on ice. '1' = on an ice-free environment.

'STATUS' refers to the use of the buildings: 1 = Closed site 2 = Lighthouse or camp 3 = Field hut or refuge 4 = Summer/seasonal only 5 = Year round operation.

These data were the output of: Brooks, S. T., Jabour, J., van den Hoff, J. and Bergstrom, D. M. Our footprint on Antarctica competes with nature for rare ice-free land. Nature Sustainability, doi:10.1038/s41893-019-0237-y (2019).

This dataset was last updated on the 30 October 2019 with six additional footprint locations added.

The digital indoor mapping market is experiencing robust growth, driven by increasing demand for location-based services within buildings and a surge in smart building technologies. The market's expansion is fueled by several key factors: the rising adoption of smartphones and mobile navigation apps, the proliferation of indoor positioning technologies (such as Bluetooth beacons, Wi-Fi positioning, and UWB), and the growing need for improved indoor navigation in large, complex venues like airports, hospitals, and shopping malls. Businesses are leveraging digital indoor maps for enhanced customer experience, improved operational efficiency, asset tracking, and emergency response capabilities. We estimate the market size in 2025 to be around $2.5 billion, with a Compound Annual Growth Rate (CAGR) of approximately 15% projected through 2033. This growth reflects the continuous innovation in mapping technologies and the expanding applications across various sectors. Significant restraints to market expansion include the high initial investment costs associated with implementing and maintaining indoor mapping systems, the challenges related to data accuracy and consistency across different buildings, and the need for interoperability between different indoor mapping platforms. However, these challenges are being addressed through technological advancements and standardization efforts. The market is segmented by deployment type (cloud-based vs. on-premise), application (navigation, asset tracking, marketing, etc.), and end-user industry (retail, healthcare, transportation, etc.). Key players, including Google, Apple, TomTom, Baidu Maps, and others, are actively investing in research and development, driving innovation and competition within the market. The future of the digital indoor mapping market looks promising, with continued growth expected as technology matures and adoption rates increase across various sectors.

This layer represents the footprints (area and perimeter) of buildings throughout all of Pend Oreille County, Washington. Great care was taken to map these features with a high degree of accuracy. This data is for reference purposes only.Building footprints for Pend Oreille County were created using a variety of information. A data set of computer-generated building footprints produced by Microsoft Maps served as a starting point for a manual review. The review compared the data set with information from aerial images, Bing Street View, Google Street View, and the Pend Oreille County Assessor’s Office. These sources contained information captured between 2011 and 2021.Throughout the prosses new footprints were added and outdated footprints were removed. Also building footprint categories were designated to each structure based off appearance and context. The categories were:Residence (1) - houses, cabins, yurts, apartment buildings, and multiple family dwellingsManufactured/Mobile (2) - manufactured homes, manufactured structures, trailer homes, and mobile homes (RVs were excluded)Agricultural (3) - greenhouses, stockyard shelters, livestock barns, machinery storage, crop storage, and feed storageShed (4) - garden sheds and equipment shedsPole Building/Utility Building/Garage (5) - out buildings, shops, storage buildings, barns (non-agricultural), pole barns, and kwanzaa hutsCommercial (6) - businesses, stores, lodging, bars, and restaurantsIndustrial (7) - lumber mills, mines, buildings associated with railroads, and buildings associated with power generation.Other (8) - local government buildings, schools, USFS buildings, municipal buildings, churches, public buildings, and unidentified buildings

Building footprint and height data were obtained from the latest global 3D building database. The building footprint data originated from Microsoft and Google datasets. Building height information was estimated using an XGBoost machine learning regression model that integrates multi-source remote sensing features. The height estimation model was trained using datasets from ONEGEO Map, Microsoft, Baidu, and EMU Analytics, utilizing 2020 data for the final estimations. Validation of this database demonstrates that the height estimation models perform exceptionally well at a global scale across both the Northern and Southern Hemispheres. The estimated heights closely match reference height data, especially for landmark buildings, showcasing superior accuracy compared to other global height products. The 3D building data that support this dataset are available in Zenodo DOI:10.5194/essd-16-5357-2024 (Che, Y., Li, X., Liu, X., Wang, Y., Liao, W., Zheng, X., Zhang, X., Xu, X., Shi, Q., Zhu, J., Yuan, H., and Dai, Y. 3D-GloBFP: the first global three-dimensional building footprint dataset. Earth System Science Data)Based on the 3D building database, we verify the locations and boundaries of individual cultural heritage sites and their buffer zones using UNESCO's heritage map platform (https://whc.unesco.org/), and categorize heritage into three groups for data extraction:Broad Scale Sites: For sites encompassing continuous building clusters or portions of cities (e.g., City of Bath), we extract buildings within the designated buffer zones provided by the UNESCO platform.Single Building Sites: For individual monuments or structures (e.g., Tower of London), we precisely extract the building footprints based on their exact coordinates.Multiple Dispersed Buildings: For sites consisting of multiple, non-contiguous structures (e.g., Wooden Churches of Southern Małopolska, Poland), we identify each location using the platform’s data and verify them through Google Maps before extracting the relevant buildings.A few linear heritage sites, such as extensive archaeological routes spanning over a thousand kilometers, are excluded due to the complexities associated with their vast spatial extent and the variability of climate conditions across different segments.The effective data coverage varies across continents: Europe and North America have an effective rate of 82.5%, Asia and the Pacific 68.3%, Latin America and the Caribbean 75.7%, Arab States 76.5%, and Africa 49.2%. This variability reflects differences in data availability. In less developed regions, remote sensing data tends to overlook non-urban heritage sites, and soil and rock structures common in Africa and Southeast Asia are more difficult to detect using satellite remote sensing techniques, leading to lower effective data coverage in these regions.

BackgroundAs the world’s most rapidly urbanizing country, China now faces mounting challenges from growing inequalities in the built environment, including disparities in access to essential infrastructure and diverse functional facilities. Yet these urban inequalities have remained unclear due to coarse observation scales and limited analytical scopes. In this study, we present the first building-level functional map of China, covering 110 million individual buildings across 109 cities using 69 terabytes of 1-meter resolution multi-modal satellite imagery. The national-scale map is validated by government reports and 5,280,695 observation points, showing strong agreement with external benchmarks. This enables the first nationwide, multi-dimensional assessment of inequality in the built environment across city tiers, geographical regions, and intra-city zones.About dataBased on the Paraformer framework that we proposed previously, we produced the first nationwide building-level functional map of urban China, processing over 69 TB of satellite data, including 1-meter Google Earth optical imagery (https://earth.google.com), 10-meter nighttime lights (SGDSAT-1) (https://sdg.casearth.cn/en), and building height data (CNBH-10m) (https://zenodo.org/records/7827315). Labels were derived from: (1) Building footprint data, including the CN-OpenData (https://doi.org/10.11888/Geogra.tpdc.271702) and the East Asia Building Dataset (https://zenodo.org/records/8174931); and (2) Land use and AOI data used for constructing urban functional annotation are retrieved from OpenStreetMap (https://www.openstreetmap.org) and EULUC-China dataset (https://doi.org/10.1016/j.scib.2019.12.007). The first 1-meter resolution national-scale land-cover map used to conduct the accessibility analysis is available in our previous study: SinoLC-1 (https://doi.org/10.5281/zenodo.7707461). The housing inequality and infrastructure allocation analysis was conducted based on the 100-meter gridded population dataset from China's seventh census (https://figshare.com/s/d9dd5f9bb1a7f4fd3734?file=43847643).This version of the data includes (1) Building-level functional maps of 109 Chinese cities, and (2) In-situ validation point sets. The building-level functional maps of 109 Chinese cities are organized in the ESRI Shapefile format, which includes five components: “.cpg”, “.dbf”, “.shx”, “.shp”, and “.prj” files. These components are stored in “.zip” files. Each city is named “G_P_C.zip,” where “G” explains the geographical region (south, central, east, north, northeast, northwest, and southwest of China) information, “P” explains the provincial administrative region information, and “C” explains the city name. For example, the building functional map for Wuhan City, Hubei Province is named “Central_Hubei_Wuhan.zip”.Furthermore, each shapefile of a city contains the building functional types from 1 to 8, where the corresponding relationship between the values and the building functions is shown below:Residential buildingCommercial buildingIndustrial buildingHealthcare buildingSport and art buildingEducational buildingPublic service buildingAdministrative buildingAbout validationGiven the importance of accurate mapping for downstream analysis, we conducted a comprehensive evaluation using government reports and in situ validation data outlined in the Data Section. This evaluation comprised two parts. First, a statistical-level evaluation was performed for each city based on official reports from the China Urban-Rural Construction Statistical Yearbook (https://www.mohurd.gov.cn/gongkai/fdzdgknr/sjfb/tjxx/jstjnj/index.html) and China Statistical Yearbook (https://www.stats.gov.cn/sj/ndsj/2023/indexch.htm). Second, a building-level geospatial evaluation was conducted by using 5.28 million field-observed points from Amap Inc. (provided in this data version of "Validation_in-situ_points.zip"), and a confusion matrix was calculated to compare the in situ points with the mapped buildings at the same location. The "Validation_in-situ_points.zip" includes the original point sets of each city, named as the city name (e.g., Wuhan.shp and corresponding “.cpg”, “.dbf”, “.shx”, and “.prj” files).

The 3D Global Building Footprints (3D-GloBFP) dataset is the first global-scale building height dataset at the individual building footprint level for the year 2020, generated through the integration of multisource Earth Observation (EO) data and the extreme gradient boosting (XGBoost) model. The reliability and accuracy of 3D-GloBFP have been validated across 33 subregions, achieving R² values ranging from 0.66 to 0.96 and root-mean-square errors (RMSEs) between 1.9 m and 14.6 m.

This version supplements building footprints and height attributes for some countries in South America, Asia, Africa, and Europe, based on building footprints provided by Microsoft (https://github.com/microsoft/GlobalMLBuildingFootprints), Open Street Map (https://osmbuildings.org/), Google-Microsoft Open Buildings - combined by VIDA (https://source.coop/repositories/vida/google-microsoft-open-buildings), and EUBUCCO (https://eubucco.com/).

The dataset is divided into spatial grid-based tiles, each stored as an individual ShapeFile (.shp) containing estimated building heights (in meters) in attribute tables. See world_grid.shp and readme.txt for details on the spatial grid and file naming.

Data download links are provided in data_links.txt.

This dataset includes building footprints from the Overture Buildings theme. Sources include OSM, Microsoft Global ML Buildings, Google Open Buildings, and Esri Community Maps. Read more at https://docs.overturemaps.org/guides/buildings/

Components: Hardware: Includes mobile mapping systems, sensors, and other equipment Software: Includes software for data collection, processing, and visualization Services: Includes data collection, processing, and analysis servicesSolutions: Location-based: Provides location-based information and services Indoor mapping: Creates maps of indoor spaces Asset management: Helps manage assets and track their location 3D mapping: Creates 3D models of buildings and infrastructureApplications: Land surveys: Used for surveying land and creating maps Aerial surveys: Used for surveying areas from the air Real estate & construction: Used for planning and designing buildings and infrastructure IT & telecom: Used for network planning and management Recent developments include: One of the pioneers in wearable mobile mapping technology, NavVis, revealed the NavVis VLX 3, their newest generation of wearable technology. As the name suggests, this is the third version of their wearable VLX system; the NavVis VLX 2 was released in July of 2021, which is over two years ago. In their news release, NavVis emphasises the NavVis VLX 3's improved accuracy in point clouds by highlighting the two brand-new, 32-layer lidars that have been "meticulously designed and crafted" to minimise noise and drift in point clouds while delivering "high detail at range.", According to the North American Mach9 Software Platform, mobile Lidar will produce 2D and 3D maps 30 times faster than current systems by 2023., Even though this is Mach9's first product launch, the business has already begun laying the groundwork for future expansion by updating its website, adding important engineering and sales professionals, relocating to new headquarters in Pittsburgh's Bloomfield area, and forging ties in Silicon Valley., In order to make search more accessible to more users in more useful ways, Google has unveiled a tonne of new search capabilities for 2022 spanning Google Search, Google Lens, Shopping, and Maps. These enhancements apply to Google Maps, Google Shopping, Google Leons, and Multisearch., A multi-year partnership to supply Velodyne Lidar, Inc.'s lidar sensors to GreenValley International for handheld, mobile, and unmanned aerial vehicle (UAV) 3D mapping solutions, especially in GPS-denied situations, was announced in 2022. GreenValley is already receiving sensors from Velodyne., The acquisition of UK-based GeoSLAM, a leading provider of mobile scanning solutions with exclusive high-productivity simultaneous localization and mapping (SLAM) programmes to create 3D models for use in Digital Twin applications, is expected to close in 2022 and be completed by FARO® Technologies, Inc., a global leader in 4D digital reality solutions., November 2022: Topcon donated to TU Dublin as part of their investment in the future of construction. Students learning experiences will be improved by instruction in the most cutting-edge digital building techniques at Ireland's first technical university., October 2022: Javad GNSS Inc has released numerous cutting-edge GNSS solutions for geospatial applications. The TRIUMPH-1M Plus and T3-NR smart antennas, which employ upgraded Wi-Fi, Bluetooth, UHF, and power management modules and integrate the most recent satellite tracking technology into the geospatial portfolio, are two examples of important items.. Key drivers for this market are: Improvements in GPS, LiDAR, and camera technologies have significantly enhanced the accuracy and efficiency of mobile mapping systems. Potential restraints include: The initial investment required for mobile mapping equipment, including sensors and software, can be a barrier for small and medium-sized businesses.. Notable trends are: Mobile mapping systems are increasingly integrated with cloud platforms and AI technologies to process and analyze large datasets, enabling more intelligent mapping and predictive analytics.

Summary:

The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.

These data are associated with an existing GitHub Repository, https://github.com/tnc-ny-science/NYC_GreenRoofMapping, and as needed and appropriate pending future work, versioned updates will be released here.

Terms of Use:

The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.

Associated Files:

As of this release, the specific files included here are:

• GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).
• GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.

Column Information for the datasets:

Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.

• fid - Unique identifier
• bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh. Associated metadata for fields from that dataset are available at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md.
• bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (http://gis.nyc.gov/taxmap/map.htm) and PLUTO/MapPLUTO (https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.
• gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).
• bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).
• prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).
• cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.
• doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.
• heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.
• feat_code - Code describing the type of building, pulled from the Building Footprint Data.
• groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.
• qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'
• notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.
• classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)
• digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)
• newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)
• original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.
• address - Address based on MapPLUTO, joined to the dataset based on bbl.
• borough - Borough abbreviation pulled from MapPLUTO.
• ownertype - Owner type field pulled from MapPLUTO.
• zonedist1 - Zoning District 1 type pulled from MapPLUTO.
• spdist1 - Special District 1 pulled from MapPLUTO.
• bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)

For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):

• xcoord - Longitude in decimal degrees.
• ycoord - Latitude in decimal degrees.

Acknowledgements:

This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.

The India Geospatial Analytics Market is experiencing robust growth, projected to reach $1.38 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 14.82% from 2025 to 2033. This expansion is fueled by several key drivers. Firstly, increasing government initiatives promoting digitalization and infrastructure development create significant demand for geospatial data and analytics across sectors like agriculture, utilities, and defense. Secondly, the rising adoption of advanced technologies such as AI, Machine Learning, and IoT enhances the capabilities of geospatial analytics, leading to more accurate insights and improved decision-making. Furthermore, the growing need for efficient resource management, precise urban planning, and enhanced disaster response mechanisms further propel market growth. Segmentation reveals strong contributions from surface analysis and network analysis within the 'By Type' category, while the 'By End-user Vertical' segment is dominated by Agriculture, Utility & Communication, and Defense & Intelligence sectors, reflecting their significant reliance on location-based intelligence. However, challenges exist. Data security and privacy concerns, particularly with sensitive location data, pose a restraint. The high cost of implementation and the requirement for specialized expertise also hinder wider adoption. Despite these challenges, the market's positive trajectory is anticipated to continue, driven by increasing data availability, improved technological capabilities, and growing awareness of the value of geospatial insights across various industries. The competitive landscape includes both global giants like Google and Esri, as well as domestic players like Esri India and Matrix Geo Solutions, indicating a dynamic market with opportunities for both established companies and emerging businesses. The forecast period of 2025-2033 promises further significant expansion, making the India Geospatial Analytics Market an attractive investment opportunity. Recent developments include: January 2023: Eris India, a company providing Geographic Information System (GIS) software and solutions, announced that the company is developing a policy map to offer data to help states and policymakers in decision-making. The Policy Maps have been designed to provide meaningful insights into various government functions., July 2022: Google announced a new partnership in India with local authorities and organizations in order to provide customized features for the diverse needs of the people in the country. Also, Google is building helpful maps that provide more visual and accurate navigation.. Key drivers for this market are: Increasing Demand of Location Based Service, Growing Availability of Spatial Data. Potential restraints include: Increasing Demand of Location Based Service, Growing Availability of Spatial Data. Notable trends are: Increasing Demand of Location Based Service.

Google Plans to Map the Interior World in 3-DTango’s CloudThe company is hosting four I/O sessions on Tango this year, up from one in 2015."With I/O it feels like they’re really doubling down on it," said Andrew Nakas, who has been building Tango applications for two years. "I can do things now I had no expectation I could do back then in 2014."Kris Kitchen, an inventor, built an application for the blind using Tango and a backpack-sized speaker called a SubPac. Tango maps a space and passes that data to the SubPac, which vibrates differently according to the proximity of objects. That gives blind people an additional sense -- touch -- alongside hearing to get around.For Tango applications like this to reach the most people, 3-D data will need to be easily shareable among devices. That would mean one person could map a museum, and another person could build an application based on the original map, or extend it, saving effort.Google is working on this by building a system that allows Tango devices to share maps with other devices. It may also weave all these maps together and store the information in its data centers so it can be accessed by even more devices.

The Governor's Office of Information Technology (OIT) is managing the Colorado Google Flood Crisis Map Colorado Google Flood Crisis Map. In partnership with the Department of Public Safety, OIT is overseeing the Statewide Digital Trunked Radio System (DTRS) which bridges state, county, local and tribal communications. Since the flooding emergency began, the DTRS system has processed more than 4.7 million radio calls and dispatched more than 150 mobile radio units to the Colorado National Guard and various search and rescue teams. Additionally, the DTRS team has deployed technicians to conduct repairs and damage assessments to the state’s 200+ DTRS towers, some of which are located in the flood zones. OIT’s Geographic Information Systems team is assisting in the coordination of aggregating data with Federal Emergency Management Agency (FEMA) and other agencies. For more information, visit www.colorado.gov/oit.

Images were acquired from approximately 80 m above ground surface on the 12th of February 2021, using a Phantom 4 Advanced drone with an FC330 camera. The images are in file input_images.zip.

The mission planning software DJI GS Pro was used to automatically acquire images at suitable locations across the survey area to enable the reconstruction of a three dimensional model.

Images 422 to 531 were imported to the photogrammetry software Pix4D (version 4.6.4). The created Pix4D project is Station12Feb2021_limited.p4d, and the processing report is Station12Feb2021_limited_report.pdf.

Four three-dimensional ground control points were used to improve the positioning of the model. No two dimensional control points or check points were used.

These points were in ITRF 2000@2000 datum (UTM Zone 49S), with co-ordinates as per the table below:

Label, Type, X(m), Y(m), Z(m), Accuracy Horz(m), Accuracy Vert(M) BM05, 3D GCP, 478814.460, 2648561.910, 38.558, 0.050, 0.100 EW-05, 3D GCP, 478635.540, 2648617.260, 27.260, 0.050, 0.100 FuelFlange, 3D GCP, 478970.810, 2648642.250, 21.920, 0.050, 0.100 MeltbellFootingA, 3D GCP, 478680.270, 2648466.547, 35.850, 0.050, 0.100

BM-05 is a survey benchmark near the Casey flagpoles, see https://data.aad.gov.au/aadc/survey/display_station.cfm?station_id=600 EW-05 is a 44 gallon drum used as a groundwater extraction well by the remediation project Fuel Flange is the last fuel flange located on the elevated fuel line prior to the fuel line “dipping” under the wharf road. Meltbell footing A is a concrete footing for the Casey melt bell (surveyed in 2019/20).

No point cloud processing (e.g. removal of errant points) was done prior to orthomosaic and model generation.

The resulting orthomosaic (Station12Feb2021_limited_transparent_mosaic_group1.tif) has an average ground sampling distance of 2.9 cm, and covers an area of approximately 15.8 hectares, encompassing the majority of buildings along “main street” at Casey. The quarry, biopiles, helipad, and upper fuel farm area are all visible.

Contour lines were generated in Pix4D at 0.5 m intervals.

Due to the limited number of ground control points, and their imprecision, the estimated residual mean squared error across three dimensions is 0.17 m (17cm), and will be worse on the periphery of the imaged area.

The orthomosaic was exported from ArcGIS to a Google Earth file (CaseyStation Orthomosaic Feb 12 2021.kmz) using XTools Pro Version 17.2.

A map was created in ArcGIS showing the orthomosaic with a background showing contour lines obtained from the AADC data product windmill_is.mdb.

The map was exported in .jpg and .pdf format at 250 dpi. Casey Station Orthomosaic Feb 12 2021.pdf Casey Station Orthomosaic Feb 12 2021.jpg

The Pix4D folder structure has been copied across (with the exception of the temp folder) and is included in this dataset.

Pix4D Folder Structure:

Station12Feb2021_limited.zip 1_intitial • Contains Pix4D files created during the project • Contains the final processing report (as .pdf) 2_densification • Contains the 3D mesh as an .obj file • Contains the point cloud as a .LAS and .PLY file • Contains the point cloud as a .p4b file 3_dsm_ortho • Contains the digital surface model as a georeferenced .tif file • Contains the orthomosaic as a georeferenced .tif file

A text readable log file from the project processing is in the file Station12Feb2021_limited.log

This web map is designed to provide an enriched geospatial platform to ascertain the flood potential status of our local place of residence and other land-use activities. Information on the flood risk distribution can be extracted by 5 major magnitudes (very high, high, moderate, low, and very low). The buildings, roads, and rail tracks that are susceptible to flooding based on the identified magnitudes are also included in the web map. In addition, the historical or flood inventory layer, which contains information on the previous flooding disasters that have occurred within the river basin, is included.

This web map is the result of extensive research using available data, open source and custom datasets that are extremely reliable.The collaborative study was done by Dr. Felix Ndidi Nkeki (GIS-Unit, BEDC Electricity PLC, 5, Akpakpava Road, Benin City, Nigeria and Department of Geography and Regional Planning, University of Benin, Nigeria), Dr. Ehiaguina Innocent Bello (National Space Research and Development Agency, Obasanjo Space Centre, FCT-Abuja, Nigeria) and Dr. Ishola Ganiy Agbaje (Centre for Space Science Technology Education, Obafemi Awolowo University, Ile-Ife, Nigeria). The study results are published in a reputable leading world-class journal known as the International Journal of Disaster Risk Reduction. The methodology, datasets, and full results of the study can be found in the paper.

The major sources of data are: ALOS PALSAR DEM; soil data from Harmonised World Soil Database-Food and Agriculture Organisation of the United Nations (FAO); land-use and surface geologic datasets from CSSTE, OAU Campus, Ile-Ife, Nigeria and Ibadan Urban Flood Management Project (IUFMP), Oyo State, Nigeria; transport network data was extracted from Open Street Map; building footprint data was mined from Google open building; and finally, rainfall grid data was downloaded from the Centre for Hydrometeorology and Remote Sensing (CHRS).

Large-scale and up-to-date maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In addition, as a fine-grained indicator of human activities, BRA could contribute to urban planning and energy modeling to provide benefits to human well-being. However, existing large-scale BRA datasets, such as those from Microsoft and Google, do not include China, hence there are no full-coverage maps of BRA in China. To this end, we produce the multi-annual China building rooftop area dataset (CBRA) with 2.5 m resolution from 2016-2021 Sentinel-2 images. The CBRA is the first full-coverage and multi-annual BRA data in China. The CBRA achieves good performance with the F1 score of 62.55% (+10.61% compared with the previous BRA data in China) based on 250,000 testing samples in urban areas, and the recall of 78.94% based on 30,000 testing samples in rural areas.

The CBRA is organized as GeoTIFF (.tif) raster file format with a single band and GCS_WGS_1984 coordinate system. The pixel values are 0 and 255, with 0 representing the background and 255 representing the building rooftop area. Furthermore, to facilitate the use of the data, the CBRA is split into 215 tiles of spatial grid, named “CBRA_year_E/W**N/S**.tif”, where “year” is the sampling year, the “E/W**N/S**” is the latitude and longitude coordinates found in the upper left corner of the tile data.

Version 2.0: In version 1.0, there were empty raster images (because they didn't contain buildings). In version 2.0, these raster images were removed.

The size of the India Geospatial Analytics market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 14.82% during the forecast period.Geospatial analytics in the India market uses GIS and other technologies to analyze spatial data and provide valuable insights. Actually, geospatial analytics is a practice, which involves gathering, processing, and interpreting data on locations and their attributes that go with them. This includes geographic coordinates, images, or sensor readings. It helps business and governments make informed decisions regarding resource management, urban planning, transportation, environment monitoring, and disaster response. Increasing government initiatives, growth in private sector adoption, and the advancements of AI and machine learning are making the Indian market more and more driven forward. Recent developments include: January 2023: Eris India, a company providing Geographic Information System (GIS) software and solutions, announced that the company is developing a policy map to offer data to help states and policymakers in decision-making. The Policy Maps have been designed to provide meaningful insights into various government functions., July 2022: Google announced a new partnership in India with local authorities and organizations in order to provide customized features for the diverse needs of the people in the country. Also, Google is building helpful maps that provide more visual and accurate navigation.. Key drivers for this market are: Increasing Demand of Location Based Service, Growing Availability of Spatial Data. Potential restraints include: High Initial Cost in Implementing Geospatial Analytics Solutions. Notable trends are: Increasing Demand of Location Based Service.

Traditionally, zoning plans have been represented on a 2D map. However, visualizing a zoning plan in 2D has several limitations, such as visualizing heights of buildings. Furthermore, a zoning plan is abstract, which for citizens can be hard to interpret. Therefore, the goal of this research is to explore how a zoning plan can be visualized in 3D and how it can be visualized it is understandable for the public. The 3D visualization of a zoning plan is applied in a case study, presented in Google Earth, and a survey is executed to verify how the respondents perceive the zoning plan from the case study. An important factor of zoning plans is interpretation, since it determines if the public is able to understand what is visualized by the zoning plan. This is challenging, since a zoning plan is abstract and consists of many detailed information and difficult terms. In the case study several techniques are used to visualize the zoning plan in 3D. The survey shows that visualizing heights in 3D gives a good impression of the maximum heights and is considered as an important advantage in comparison to 2D. The survey also made clear including existing buildings is useful, which can help that the public can recognize the area easier. Another important factor is interactivity. Interactivity can range from letting people navigate through a zoning plan area and in the case study users can click on a certain area or object in the plan and subsequently a menu pops up showing more detailed information of a certain object. The survey made clear that using a popup menu is useful, but this technique did not optimally work. Navigating in Google Earth was also being positively judged. Information intensity is also an important factor Information intensity concerns the level of detail of a 3D representation of an object. Zoning plans are generally not meant to be visualized in a high level of detail, but should be represented abstract. The survey could not implicitly point out that the zoning plan shows too much or too less detail, but it could point out that the majority of the respondents answered that the zoning plan does not show too much information. The interface used for the case study, Google Earth, has a substantial influence on the interpretation of the zoning plan. The legend in Google Earth is unclear and an explanation of the zoning plan is lacking, which is required to make the zoning plan more understandable. This research has shown that 3D can stimulate the interpretation of zoning plans, because users can get a better impression of the plan and is clearer than a current 2D zoning plan. However, the interpretation of a zoning plan, even in 3D, still is complex.

Accessibility dashboard for the University of Exeter showing the following features:Campus buildingsAcademic buildingsUniversity accommodationLibrariesStudent's Guild buildingsSports facilitiesOther buildingsAccessibility features (SDG 10)Wheelchair accessible buildingsDisabled parking spacesDisabled toiletsLiftsAccessibility spaces (e.g. AccessAbility rooms, disability support services)Disabled refuge system call pointsWheelchair access rampsGender neutral toiletsHealth and wellbeing features (SDG 3)First aid locationsOutdoor wellbeing spacesDefibrillatorsFire assembly pointsSafer walking routesThe dashboard can be customised to suit an individual user's needs, by toggling the features listed above on and off. This is to allow the map to hold a lot of information and functionality, without being too cluttered upon initial loading. Whilst the map has the potential to become quite busy if all of the feature layers are turned on, the map is not intended to be used in this way, hence why the default is for the map to load with only buildings and safer walking routes visible.By clicking on an individual building or feature, a pop-up containing further information and photographs will appear. For buildings, there will be an option for the user to open Google Maps in a new tab, to allow them to easily navigate to any building from their current location. This was chosen as there is currently not a function within ArcGIS Online Dashboards to allow the user to input their own location for directions, but the dashboard could be updated appropriately if this function became available at a later date. For accessibility and wellbeing features, the pop-ups contain useful information such as the floor the feature is located on, whether there are any booking requirements to use the feature, whether the feature is fully functional, etc. This is to allow users with disabilities to be able to determine which buildings on campus are suitable for them - for example, a user in a wheelchair can click on accessible parking spaces to see whether they are able to get into the building without assistance from the parking space, allowing them to make any necessary arrangements prior to their arrival.