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.

When constructing a new building, the Virginia Uniform Statewide Building Code requires the structure to be assigned a Type of Construction based on its combustibility and level of protection against fire. In addition, the building code assigns a Use Group that identifies the occupancy based on how the building is used, i.e., mercantile, assembly, business, industrial and storage. In some cases, buildings may have multiple Types of Construction and Use Groups. Historic building permit applications capture this data as a snapshot in time from the date of initial construction. Building code designations for Type of Construction and Use Group have changed over time and buildings may have undergone tenant and construction updates. Therefore, current designations may be different from the data gathered from historic permit applications.Contact: Land Development Services, Brett MartinData accessibility: PublicUpdate frequency: MonthlyCreation date: 04/19/2019Feature class name: LDSAMGR.BUILDING_CONSTRUCTION_TYPE

The attribute tables holds the following information:LEED_RATE - Gold, Silver, Platinum based on official LEED certification. Includes all buildings in the SOMA district. Includes LEED buildings that are not owned by PSU.BUILDINGID - Building acronym. Do not use this as a building unique identifier. LONGNAME - Official long name of building.Owned_Leas - Owned means PSU owns, manages and operates building for PSU use. Leased means the building is being leased (Crown Plaza, Pepco and UTS). Partnership means that PSU is in contract or agreement to use the space (CLSB). Other means PSU does not own or operate building. This data may change up to 2-3 times a year. The Campus Planning Office maintains this information. BLDID_AIM - The Building identification number matches the unique identifier of the Asset Information Managment database (AssetWorks, AimCAD, or the work order request system). Photo - Intended to be an attribute hotlink field, data has not been updated to match the new capital projects website address. Housing - Yes means the building is housing, includes private and PSU housing. Non-PSU housing data sourced from the City of Portland buildings database. ShortName - Shortened building name to accomodate labeling. Seismic - Indicates whether a building has had any seismic retrofit. For additional information ask Capital Archivist Bryce Henry.All other attribute data is sourced from the City of Portland buildings GIS database and additional metadata can be found here: http://www.civicapps.org/datasets/building-footprints-portland

This dataset is derived from the 2003 PDF map by the City of Toronto depicting building construction dates by parcels. The 2021 Toronto Parcels were used to attach the built date. Please Note: This is not an official City of Toronto dataset and should be used for reference, teaching and consultation purposes only and not for analysis

Initial Data Capture: Building were originally digitized using ESRI construction tools such as rectangle and polygon. Textron Feature Analyst was then used to digitize buildings using a semi-automated polygon capture tool as well as a fully automated supervised learning method. The method that proved to be most effective was the semi-automated polygon capture tool as the fully automated process produced polygons that required extensive cleanup. This tool increased the speed and accuracy of digitizing by 40%.Purpose of Data Created: To supplement our GIS viewers with a searchable feature class of structures within Ventura County that can aid in analysis for multiple agencies and the public at large.Types of Data Used: Aerial Imagery (Pictometry 2015, 9inch ortho/oblique, Pictometry 2018, 6inch ortho/oblique) Simi Valley Lidar Data (Q2 Harris Corp Lidar) Coverage of Data:Buildings have been collected from the aerial imageries extent. The 2015 imagery coverage the south county from the north in Ojai to the south in thousand oaks, to the east in Simi Valley, and to the West in the county line with Santa Barbara. Lockwood Valley was also captured in the 2015 imagery. To collect buildings for the wilderness areas we needed to use the imagery from 2007 when we last flew aerial imagery for the entire county. 2018 Imagery was used to capture buildings that were built after 2015.Schema: Fields: APN, Image Date, Image Source, Building Type, Building Description, Address, City, Zip, Data Source, Parcel Data (Year Built, Basement yes/no, Number of Floors) Zoning Data (Main Building, Out Building, Garage), First Floor Elevation, Rough Building Height, X/Y Coordinates, Dimensions. Confidence Levels/Methods:Address data: 90% All Buildings should have an address if they appear to be a building that would normally need an address (Main Residence). To create an address, we do a spatial join on the parcels from the centroid of a building polygon and extract the address data and APN. To collect the missing addresses, we can do a spatial join between the master address and the parcels and then the parcels back to the building polygons. Using a summarize to the APN field we will be able to identify the parcels that have multiple buildings and delete the address information for the buildings that are not a main residence.Building Type Data: 99% All buildings should have a building type according to the site use category code provided from the parcel table information. To further classify multiple buildings on parcels in residential areas, the shape area field was used to identify building polygons greater than 600 square feet as an occupied residence and all other buildings less than that size as outbuildings. All parcels, inparticular parcels with multiple buildings, are subject to classification error. Further defining could be possible with extensive quality control APN Data: 98% All buildings have received APN data from their associated parcel after a spatial join was performed. Building overlapping parcel lines had their centroid derived which allowed for an accurate spatial join.Troubleshooting Required: Buildings would sometimes overlap parcel lines making spatial joining inaccurate. To fix this you create a point from the centroid of the building polygon, join the parcel information to the point, then join the point with the parcel information back to the building polygon.

This dataset contains buildings within Montgomery County. Building ruins, buildings under construction, and parking garages are also included. Overhead rooftops, or canopies, are shown with a separate feature code and features running under are not clipped out. Each feature is attributed with height in feet and roof type of either gable or flat. This data was captured for use in general mapping at a scale of 1:1200.Captured March 2023

The BIM Software Market is booming, projected to reach [estimated 2033 value in billions] by 2033, growing at a CAGR of 13.90%. Discover key trends, drivers, and leading companies shaping this dynamic sector. Learn more about market segmentation, regional analysis, and future projections for BIM software adoption. Recent developments include: July 2024 - Esri and Autodesk have deepened their partnership to enhance data interoperability between Geographic Information Systems (GIS) and Building Information Modeling (BIM), with ArcGIS Pro now offering direct-read support for BIM and CAD elements from Autodesk's tools. This collaboration aims to integrate GIS and BIM workflows more seamlessly, potentially transforming how architects, engineers, and construction professionals work with geospatial and design data in the AEC industry., June 2024 - Hexagon, the Swedish technology giant, has acquired Voyansi, a Cordoba-based company specializing in Building Information Modelling (BIM), to enhance its portfolio of BIM solutions. This acquisition not only strengthens Hexagon's position in the global BIM market but also recognizes the talent in Argentina's tech sector, particularly in Córdoba, where Voyansi has been developing design, architecture, and engineering services for global construction markets for the past 15 years., April 2024 - Hyundai Engineering has partnered with Trimble Solution Korea to co-develop a Building Information Modeling (BIM) process management program, aiming to enhance construction site productivity through advanced 3D modeling technology. This collaboration highlights the growing importance of BIM in the construction industry, with the potential to optimize steel structure and precast concrete construction management, shorten project timelines, and reduce costs compared to traditional construction methods.. Key drivers for this market are: Governmental Mandates and International Standards Encouraging BIM Adoption, Boosting Project Performance and Productivity. Potential restraints include: Governmental Mandates and International Standards Encouraging BIM Adoption, Boosting Project Performance and Productivity. Notable trends are: Government Mandates Fueling BIM Growth.

The dataset contains locations and attributes of building construction and alteration permits applied for and approved by the District of Columbia Department of Buildings. These data are shared via an automated process where addresses are batch matched (geocoded) to the District's Master Address Repository. Users may find that some data points will contain 0,0 for X,Y coordinates resulting in inconsistent spatial locations. Addresses for these data points could not be automatically geocoded and will need to be manually geocoded to 'best fit' locations in DC.

Buildings. The dataset contains polygons representing planimetric buildings, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO). These features were originally captured in 1999 and updated in 2005. The following planimetric layers were updated: - Building Polygons (BldgPly) - Bridge and Tunnel Polygons (BrgTunPly) - Metro Entrance Points (MetroEntPt) - Obscured Area Polygons (ObsAreaPly) - Railroad Lines (RailRdLn) - Road, Parking, and Driveway Polygons (RoadPly) - Sidewalk Polygons (SidewalkPly) - Under Construction Areas (UnderConstPly) - Wooded Areas (WoodPly) The following planimetric layers are new: - Horizontal and Vertical Control Points (GeoControlPt) - Hydrography Center Lines (HydroCenterLineLn).

Buildings_BACO

   File Geodatabase Feature Class


   Thumbnail Not Available

   Tags

   Planimetric, BES, Building, Footprints, Structure, Cadastral, Housing, Homes




   Summary


   High resolution planimetric building data.


   Description


   Baltimore County building footprints circa 1997. This dataset was obtained from the Baltimore County Goverment; there was only limited supporting documentation. A limited assesment was conducted that compared the building footprints to high resolution aerial imagery (Emerge) flown in 1999. This assessment found that a considerable number of building footprints were missing. Those that did exist appeared to agree spatially with the Emerge imagery. The following building types are included in this dataset: residential, commercial/industrial, other structures, miscellaneous buildings, buildings under construction, toll booth plazas, rail stations, water towers, storage tanks, silos.


   Credits


   Baltimore County Government


   Use limitations


   BES research only.


   Extent



   West -76.897990  East -76.334462 

   North 39.727055  South 39.189542 




   Scale Range

   There is no scale range for this item.

GIS for Construction Planning Market Outlook

According to our latest research, the GIS for Construction Planning market size reached USD 6.4 billion in 2024, and it is expected to grow at a robust CAGR of 13.2% during the forecast period, reaching approximately USD 18.2 billion by 2033. This dynamic growth is primarily driven by the increasing integration of geospatial technologies in construction workflows, the rising demand for efficient project management solutions, and the global emphasis on sustainable urban development. The market is witnessing significant traction as construction firms and stakeholders recognize the value of Geographic Information Systems (GIS) in optimizing site selection, resource allocation, and risk mitigation.

One of the primary growth factors for the GIS for Construction Planning market is the rapid digital transformation occurring within the construction industry. As project complexity increases and timelines become tighter, construction companies are leveraging GIS solutions to gain real-time spatial insights, enhance collaboration, and streamline operations. The adoption of Building Information Modeling (BIM) integrated with GIS is also playing a pivotal role, enabling more accurate planning, design, and execution of construction projects. This integration empowers stakeholders to visualize project data in a geospatial context, facilitating better decision-making and reducing costly reworks. Additionally, the proliferation of smart cities and infrastructure modernization projects worldwide is significantly boosting the demand for advanced GIS tools in construction planning.

Another significant driver is the growing regulatory emphasis on environmental sustainability and risk management in construction projects. Governments and regulatory bodies are mandating comprehensive environmental impact assessments and risk analyses before granting approvals for new developments. GIS platforms provide a robust framework for conducting these assessments by enabling spatial analysis of environmental factors, potential hazards, and socio-economic impacts. As a result, construction firms are increasingly adopting GIS to ensure compliance with regulations, minimize environmental footprints, and enhance community engagement. The ability of GIS to integrate diverse datasets and generate actionable insights is proving invaluable in navigating the complex regulatory landscape of the construction sector.

Furthermore, advancements in cloud computing, IoT, and mobile technologies are accelerating the adoption of GIS in construction planning. Cloud-based GIS solutions offer scalability, flexibility, and real-time data access, making them ideal for large-scale, multi-site construction projects. The integration of IoT devices enables continuous monitoring of construction sites, asset tracking, and predictive maintenance, all of which feed valuable data into GIS platforms. These technological innovations are not only improving project efficiency but also enabling proactive risk management and resource optimization. As construction firms increasingly embrace digital transformation, the demand for sophisticated GIS solutions is expected to surge, further propelling market growth.

From a regional perspective, North America currently dominates the GIS for Construction Planning market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The strong presence of leading technology providers, high levels of investment in infrastructure, and early adoption of advanced digital tools have positioned North America as a key growth engine. Meanwhile, Asia Pacific is projected to witness the highest CAGR during the forecast period, driven by rapid urbanization, government-led smart city initiatives, and expanding construction activities in emerging economies such as China and India. Europe continues to demonstrate steady growth, fueled by stringent environmental regulations and a focus on sustainable development.

Component Analysis

The GIS for Cons

The ZIP file consist of GIS files with information about the excavations, findings and other metadata about the archaeological survey.

The paper addresses the integration of Geographic Information Systems (GIS) and Historic Building Information Modeling (HBIM) as a framework for structuring and managing information related to networks of public historical buildings. The approach aims to support administrators and decision-makers in assessing both performance and condition of these assets. To this end, the study proposes a methodological workflow encompassing: (i) the development of an interoperable GIS-BIM database that aggregates heterogeneous data at multiple scales, including district, building, and construction component levels, with the latter enhanced by data derived from experimental measurements of both onsite and laboratory tests; (ii) the creation of tailored data exchange routines facilitating bidirectional transfer between GIS and BIM environments, enabling parametric modeling of building elements based on unified descriptors drawn from predefined glossaries; and (iii) the semantic enrichment of HBIM models, incorporating descriptions of decay patterns and enabling integrated visualization of both “as-built” and “as-damaged” conditions within the GIS platform. The methodology is applied to a real case study focusing on the historical building assets of a municipality in southern Italy, within a specialized context, namely systematizing knowledge to support seismic vulnerability assessment. This case study demonstrates the outcomes and potential applications of the proposed framework, contributing to the debate on its implications for enhancing contemporary heritage evaluation and management practices. Centralized relational database is set for GIS-BIM interoperability. GIS-based inventory approach of networked construction materials, components, and performances is established by unified performance descriptors and diagnostic tests. GIS-BIM data exchange routines by programming codes and algorithms are developed in Python. Dynamo “As-built” and “as-damaged” HBIM models are integrated in GIS environment multi-data seismic vulnerability assessment

November 2024

The dataset contains locations and attributes of building construction and alteration permits applied for and approved by the District of Columbia Department of Buildings. These data are shared via an automated process where addresses are batch matched (geocoded) to the District's Master Address Repository. Users may find that some data points will contain 0,0 for X,Y coordinates resulting in inconsistent spatial locations. Addresses for these data points could not be automatically geocoded and will need to be manually geocoded to 'best fit' locations in DC.

This dataset contains the a subset of the main approved building permits and is filtered to contain only new building permits.For more information about building and construction in the City of Saint Paul, please visit: https://www.stpaul.gov/departments/safety-inspections/building-and-construction Note: We have identified an issue with the time-related data in our datasets. The times are displayed correctly as Central time when viewing the data in the City’s open information portal. Upon downloading or exporting the data, any date/time columns are converted to Coordinated Universal Time (UTC). This results in the times getting converted to of either 5 hours (during Daylight savings time) or 6 hours (for Standard time) ahead of our Central time.

To correct this issue, determine if it is Standard time or Daylight Savings time. Central Daylight Time (CDT) runs from the second Sunday in March to the first Sunday in November. Central Standard Time (CST) is the remainder of the year. If it is CDT, subtract 5 hours from UTC time and if it is CST, then subtract 6 hours. This issue comes from the ESRI platform and is unable to be modified at this time.

The feature class accurately represents building structures, including Residential, Commercial, Institutional, Garages, Other structures, Buildings under Construction, Water Towers, Storage Tanks and Silos.

The City of Saint Paul's Department of Safety and Inspections requires homeowners or licensed contractors to obtain a building permit before the following changes are made on one or two-family residences, multi-family residences, or buildings for commercial, industrial, or institutional use:Building a new structureAdding an addition to current structureRemodeling or repairing a structureFor more information about the requirements and the application process, please visit: https://www.stpaul.gov/departments/safety-inspections/building-and-construction/construction-permits-and-inspections/building-permits-inspections Note: We have identified an issue with the time-related data in our datasets. The times are displayed correctly as Central time when viewing the data in the City’s open information portal. Upon downloading or exporting the data, any date/time columns are converted to Coordinated Universal Time (UTC). This results in the times getting converted to of either 5 hours (during Daylight savings time) or 6 hours (for Standard time) ahead of our Central time.

To correct this issue, determine if it is Standard time or Daylight Savings time. Central Daylight Time (CDT) runs from the second Sunday in March to the first Sunday in November. Central Standard Time (CST) is the remainder of the year. If it is CDT, subtract 5 hours from UTC time and if it is CST, then subtract 6 hours. This issue comes from the ESRI platform and is unable to be modified at this time.

This dataset contains information on building use and square footage detail for all “Building” construction permits.

Notes: The City’s Customer Self Service Portal can be used to search for individual permits. For more information on properties, including assessor information, please visit the Boulder County webpages: Open Data and Property Search.

The following supporting file can be used with this dataset for extra context:

Construction Permit Data Dictionary

This dataset details construction commencement reports received in Gapyeong-gun, providing essential information for construction administration. It includes city/county name, report date, road name and lot number addresses, construction use, latitude, longitude, and data base date. The data helps identify the location and usage distribution of construction starts, supporting urban planning, building permits, and policy management. With included latitude and longitude, it is also valuable for spatial analysis and integration with GIS, enhancing understanding of construction trends and improving transparency and efficiency in Gapyeong-gun’s construction administration.