http://rightsstatements.org/vocab/InC/1.0/http://rightsstatements.org/vocab/InC/1.0/
The OSM Readiness Tool (V1.0) was developed in response to a request by industry partners, SBEnrc Project 2.2 Offsite Fabrication. The problem identified was: 'What will enable us to determine if Off-Ssite Manufacture (OSM) is suitable for a building project?' International studies indicate that benefits are accrued if OSM is considered at early stages and incorporated into the design. Successful OSM adoption can be increased though planning and engagement with all OSM stakeholders throughout the entire building process. Having OSM capability and capacity information available will assist the project team with decision-making concering adopting an OSM procurement model. Timing of the decision to adopt an OSM procurement model effects project outcomes: Early adoption of the OSM procurement model, with input from a wide variety of OSM stakeholders, is considered the low risk option. A high level of stakeholder engagement provides information concerning the availability of essential OSM capability and capacity. Expertise, from a wide range of stakeholders, supports integration of OSM into the total project at the Arrange Team stage. Adopting OSM at the Tendering stage could mean additional delays for delivery and possible problems with integrating OSM specifications into the Shop Drawings. Adopting an OSM procurement strategy after the Detail Design is completed can benefit a building project, but the benefits afforded by early AEC knowledge sharing relationships will be limited, thus constraining the effectiveness of an OSM procurement model indicating a medium level of risk. Adopting OSM at the Construction stage of a project is always an option. But, the lack of early project team planning and engagement will certainly mean delays and difficulty with on-site installation. Thus, late adoption of an OSM procurement model is considered a high risk delivery strategy.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset contains 23 30m resolution raster data of continental Europe land use / land cover classes extracted from OpenStreetMap, as well as administrative areas, and a harmonized building dataset based on OpenStreetMap and Copernicus HRL Imperviousness data.
The land use / land cover classes are:
buildings.commercial
buildings.industrial
buildings.residential
cemetery
construction.site
dump.site (landfill)
farmland
farmyard
forest
grass
greenhouse
harbour
meadow
military
orchard
quarry
railway
reservoir
road
salt
vineyard
The land use / land cover data was generated by extracting OSM vector layers from https://download.geofabrik.de/). These were then transformed into a 30 m density raster for each feature type. This was done by first creating a 10 m raster where each pixel intersecting a vector feature was assigned the value 100. These pixels were then aggregated to 10 m resolution by calculating the average of every 9 adjacent pixels. This resulted in a 0—100 density layer for the three feature types. Although the digitized building data from OSM offers the highest level of detail, its coverage across Europe is inconsistent. To supplement the building density raster in regions where crowd-sourced OSM building data was unavailable, we combined it with Copernicus High Resolution Layers (HRL) (obtained from https://land.copernicus.eu/pan-european/ high-resolution-layers), filling the non-mapped areas in OSM with the Impervious Built-up 2018 pixel values, which was averaged to 30 m. The probability values produced by the averaged aggregation were integrated in such a way that values between 0—100 refer to OSM (lowest and highest probabilities equal to 0 and 100 respectively), and the values between 101—200 refer to Copernicus HRL (lowest and highest probability equal to 200 and 101 respectively). This resulted in a raster layer where values closer to 100 are more likely to be buildings than values closer to 0 and 200. Structuring the data in this way allows us to select the higher probability building pixels in both products by the single boolean expression: Pixel > 50 AND pixel <150.
This dataset is part of the OpenStreetMap+ was used to pre-process the LUCAS/CORINE land use / land cover samples (https://doi.org/10.5281/zenodo.4740691) used to train machine learning models in Witjes et al., 2022 (https://doi.org/10.21203/rs.3.rs-561383/v4)
Each layer can be viewed interactively on the Open Data Science Europe data viewer at maps.opendatascience.eu.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The influx of machine-generated data, especially from Geospatial Artificial Intelligence (GeoAI), has grown significantly in less than a decade. AI-assisted mapping, a technological innovation leveraging open GeoAI data, integrates human validation to update crowdsourced databases, such as OpenStreetMap (OSM) is , creating a sense of security and quality assurance. However, OSM contributors have expressed mixed feelings about the presence of AI content in the database, raising questions about their underlying fears and whether it is possible to differentiate between these data after integration. To explore this, first, we analyzed discussions among OSM contributors and identified concerns about data authenticity, quality, and the balance between human input and AI contributions. Secondly, we investigated the extent to which machine-generated roads can be detected within OSM.
This data represent data used in our paper titled "Towards Transparency in Collaborative Mapping: Detecting Machine-Generated Roads in OpenStreetMap". It includes OSM roads data from GeoAI and OSM human constributors.
The CycloSM Bike Map is a detailed cycling-focused web map designed to help riders navigate urban and rural environments efficiently. Built using OpenStreetMap (OSM) data, this map highlights key cycling infrastructure, including bike lanes, shared-use paths, and protected cycle tracks, while also incorporating features such as topography, road surface types, and bike-friendly amenities.Unlike standard road maps, CycloSM prioritizes bicycle accessibility, displaying information such as:Dedicated cycling facilities (e.g., bike lanes, shared-use paths, and bicycle boulevards).Traffic conditions and road hierarchy, helping cyclists avoid high-stress routes.Elevation and terrain details, useful for route planning in hilly areas.Public transit integration, showing bike-friendly transit connections.This map is ideal for commuters, recreational cyclists, and urban planners looking to analyze biking infrastructure and improve connectivity. It serves as a powerful tool for understanding how cycling fits into the broader transportation network.For more details, visit CycloSM.
The Facet extension for CKAN enhances data discovery and visualization capabilities within a CKAN instance. Designed to address specific requirements, likely within a research or data-driven organization, it introduces custom facets and allows the integration of OpenStreetMap (OSM) visualizations to dataset pages. This extension enriches the user experience by providing more tailored search filters and spatial data representation. Key Features: Custom Facets: Enables the creation of bespoke facet filters. These facets are tailored to meet the specific needs for data discovery. OSM Map Integration: Displays geospatial data associated with datasets on an OpenStreetMap, allowing users to visually explore and understand the geographic distribution of the data. ISEBEL Requirements: The extension was explicitly developed considering the requirements of ISEBEL (specific user case), suggesting the capacity to accommodate other project requirements regarding its custom facets. Technical Integration: The Facet extension integrates with CKAN by enabling and declaring facet within plugin section of your production.ini configuration file. Benefits & Impact: Using the Facet extension will allow for better control over the data discovery process and improve comprehension of datasets with a spatial data representation through the use of the OSM map integration.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Point of interest (POI) data refers to information about the location and type of amenities, services, and attractions within a geographic area. This data is used in urban studies research to better understand the dynamics of a city, assess community needs, and identify opportunities for economic growth and development. POI data is beneficial because it provides a detailed picture of the resources available in a given area, which can inform policy decisions and improve the quality of life for residents. This paper presents a large-scale, standardized POI dataset from OpenStreetMap (OSM) for the European continent. The dataset's standardization and gridding make it more efficient for advanced modeling, reducing 7,218,304 data points to 988,575 without significant resolution loss, suitable for a broader range of models with lower computational demands. The resulting dataset can be used to conduct advanced analyses, examine POI spatial distributions, conduct comparative regional studies, enhancing understanding of the economic activity, distribution, attractions, and subsequently, economic health, growth potential, and cultural opportunities. The paper describes the materials and methods used in generating the dataset, including OSM data retrieval, processing, standardization, and hexagonal grid generation. The dataset can be used independently or integrated with other relevant datasets for more comprehensive spatial distribution studies in future research.
Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
License information was derived automatically
The Open National Address Base (BANO) is an initiative of OpenStreetMap France. Its purpose is to establish as complete a free base as possible of address points at the level of France. On this page you will find access to the various issues raised by the establishment of an address base. More information on OpenStreetMap Wiki ## The data produced by BANO They are regenerated and published every night with the improvements made in OpenStreetMap during the previous day. They also integrate on D+1 the data publications in BAL format of https://address.data.gouv.fr/ (local Cadastre and BAL)
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Dataset Summary
This dataset provides the most accurate and comprehensive geospatial information on wind turbines in South Africa as of 2025. It includes precise turbine coordinates, detailed technical attributes, and spatially harmonized metadata across 42 wind farms. The dataset contains 1,487 individual turbine entries with validated information on turbine type, rated capacity, rotor diameter, commissioning year, and administrative regions. It was compiled by integrating OpenStreetMap (OSM) data, satellite imagery from Google and Bing, a RetinaNet-based deep learning model for coordinate correction, and manual verification.
Data Structure
Format: GeoJSON
Coordinate Reference System (CRS): WGS 84 (EPSG:4326
)
Number of features: 1,487
Geometry type: Point (turbine locations)
Key attributes:
id
: Unique internal identifier
osm_id
: Reference ID from OpenStreetMap
gid
, country
, type1
, name1
, type2
, name2
: Administrative region (based on GADM)
farm_name
: Name of the wind farm
commissioning_year
: Year the turbine was commissioned
number_of_turbines
: Total number of turbines at the wind farm
total_farm_capacity
: Total installed capacity of the wind farm (MW)
capacity_per_turbine
: Rated power per turbine (MW)
turbine_type
: Manufacturer and model of the turbine
geometry
: Point geometry (longitude, latitude)
Publication Abstract
Accurate and detailed spatial data on wind energy infrastructure is essential for renewable energy planning, grid integration, and system analysis. However, publicly available datasets often suffer from limited spatial accuracy, missing attributes, and inconsistent metadata. To address these challenges, this study presents a harmonized and spatially refined dataset of wind turbines in South Africa, combining OpenStreetMap (OSM) data with high-resolution satellite imagery, deep learning-based coordinate correction, and manual curation. The dataset includes 1487 turbines across 42 wind farms, representing over 3.9 GW of installed capacity as of 2025. Of this, more than 3.6 GW is currently operational. The Geo-Coordinates were validated and corrected using a RetinaNet-based object detection model applied to both Google and Bing satellite imagery. Instead of relying solely on spatial precision, the curation process emphasized attribute completeness and consistency. Through systematic verification and cross-referencing with multiple public sources, the final dataset achieves a high level of attribute completeness and internal consistency across all turbines, including turbine type, rated capacity, and commissioning year. The resulting dataset is the most accurate and comprehensive publicly available dataset on wind turbines in South Africa to date. It provides a robust foundation for spatial analysis, energy modeling, and policy assessment related to wind energy development. The dataset is publicly available.
Citation Notification
If you use this dataset, please cite the following publication (currently in the process of publication):
Kleebauer, M.; Karamanski, S.; Callies, D.; Braun, M. A Wind Turbines Dataset for South Africa: OpenStreetMap Data, Deep Learning Based Geo-Coordinate Correction and Capacity Analysis. ISPRS Int. J. Geo-Inf. 2025, 14, 232. https://doi.org/10.3390/ijgi14060232
http://dcat-ap.de/def/licenses/other-closedhttp://dcat-ap.de/def/licenses/other-closed
Dette datasæt indeholder en omfattende samling af oplysninger om turistattraktioner (POI'er) i hovedstadsregionen Rhein-Neckar.
Flettet POI-datasæt
Integration af POI-data:Datasættet indeholder POI'er baseret på data fra OpenStreetMap og Overture.
Kategorier af seværdigheder:
Omfatter data om forskellige kategorier af POI'er
Georefererede data:
Alle POI'er er georefererede, hvilket giver mulighed for rumlig analyse og kortlægning.
Dataformat Filen tilbydes i det åbne, ikke-proprietære GeoPackage (.gpkg) format.
Brugsanvisning:
Vi opfordrer alle interesserede parter til at bruge dette datasæt til at få en dybere forståelse af turistlandskabet i hovedstadsregionen Rhein-Neckar og til at bidrage til at fremme attraktiv og tilgængelig turisme.
Tilgængelighed:
Hvis du er interesseret i datasættet, vil du efter anmodning få udleveret det nødvendige password for at få adgang til dataene.Kontakt projektleder Marius Jörres på e-mail.
E-mail: marius.joerres@m-r-n.com
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The offer includes data from the mFUND project "LevelOut - Automated development of building floor plans for map services and city models": Input data from digital building models in IFC format and output data for map services and city models, such as CityGML, IndoorGMl and OSM. In the project, digital building models of publicly accessible buildings were prepared in such a way that their interior data can be automatically extracted and integrated into data sets of the urban outdoor space. The generated data can thus serve as a basis for navigation applications for people and autonomous objects. On this basis, innovative applications can improve accessibility in public spaces, increase the attractiveness of rail and public transport, make transport and work processes safer and more efficient, and enable autonomous navigation with AI. -- This offer contains data from the mFUND project LevelOut: input data of digital building models in IFC format and output data for map services and city models, such as CityGML, IndoorGMl and OSM. In the project, digital building models of publicly accessible buildings were processed in such a way that their indoor data can be automatically extracted and be integrated into data sets of the urban outdoor space. The generated data can then be used as a basis for navigation applications for people and autonomous objects. On this basis, innovative applications can contribute to improved accessibility in public spaces, increase attractiveness of rail and public transport, make transportation and work processes safer and more efficient and enable autonomous navigation with AI.
Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
License information was derived automatically
A comprehensive geospatial line layer was developed by combining OpenStreetMap (OSM) ways data, specifically filtering for permeable and likely permeable trails, with Strava hiking and biking use data. This layer was further enriched by integrating the NRCS web soil survey recreational suitability information for the states of Maine (ME), New York (NY), New Hampshire (NH), Rhode Island (RI), Connecticut (CT), Vermont (VT), and Massachusetts (MA). To ensure contextual relevance, the geospatial layer was clipped to the 2021 National Land Cover Database (NLCD) forest layer, resulting in a refined dataset where the lines exclusively represent permeable trails within forested areas. The integration of OSM, Strava, iNaturalist, NRCS, and NLCD datasets provides a comprehensive understanding of the interaction between trail permeability, recreational usage, soil suitability, and forest cover, offering valuable insights for sustainable land management practices.Citations:
OpenStreetMap contributors. (2021). OpenStreetMap Data. Retrieved from https://www.openstreetmap.orgStrava Metro. (2022). Strava Hiking and Biking Use Data. Retrieved from https://metro.strava.com/USDA NRCS. (2023). Web Soil Survey. Retrieved from https://websoilsurvey.sc.egov.usda.gov/Multi-Resolution Land Characteristics (MRLC) Consortium. (2021). National Land Cover Database 2021. Retrieved from https://www.mrlc.gov/data/nlcd-2021-land-cover-conusiNaturalist 2022 observations. iNaturalist (2022). Retrieved from https://www.inaturalist.org/
This report provides the results of seven technical integration workshops sponsored by the Oil Sands Monitoring (OSM) program for the 2018-2019 fiscal year held between the end of October 2018 and early February 2019. The seven workshops covered the following topoics : terrestrial biological monitoring; groundwater; surface water and aquatic biology; atmospheric deposition, geospatial science, mercury, and predictive modelling. This report is a summary of detailed notes taken during each workshop.
https://doi.org/10.17026/fp39-0x58https://doi.org/10.17026/fp39-0x58
First responders and recovery planners need accurate and quickly derived information about the status of buildings as well as newly built ones to both help victims and to make decisions for reconstruction processes after a disaster. Deep learning and, in particular, convolutional neural network (CNN)-based approaches have recently become state-of-the-art methods to extract information from remote sensing images, in particular for image-based structural damage assessment. However, they are predominantly based on manually extracted training samples. In the present study, we use pre-disaster OpenStreetMap building data to automatically generate training samples to train the proposed deep learning approach after the co-registration of the map and the satellite images. The proposed deep learning framework is based on the U-net design with residual connections, which has been shown to be an effective method to increase the efficiency of CNN-based models. The ResUnet is followed by a Conditional Random Field (CRF) implementation to further refine the results. Experimental analysis was carried out on selected very high resolution (VHR) satellite images representing various scenarios after the 2013 Super Typhoon Haiyan in both the damage and the recovery phases in Tacloban, the Philippines. The results show the robustness of the proposed ResUnet-CRF framework in updating the building map after a disaster for both damage and recovery situations by producing an overall F1-score of 84.2%.
The dataset covers aspects such as transportation, healthcare, and education, which is of great significance for understanding the capacity, accessibility, and support capabilities of transportation, healthcare, and education development in the Qinghai-Tibet Plateau region. By utilizing statistical data from schools and hospitals at various levels and road network data, the dataset provides a comprehensive evaluation of the transportation and infrastructure support capabilities of the Qinghai-Tibet Plateau. The dataset covers the entire Qinghai-Tibet Plateau and is calculated and analyzed at the township unit scale, ensuring the accuracy and practicality of the data. The dataset can provide references for transportation planning, healthcare resource allocation, and education service optimization in the Qinghai-Tibet Plateau region. Through these data, it is possible to identify the weak areas in transportation and infrastructure in the Qinghai-Tibet Plateau and provide more refined references for solving local issues. Transportation Support Capability Evaluation Dataset: This dataset integrates township settlement data obtained from field surveys and road data provided by OpenStreetMap (OSM). It uses the Cost Distance tool in ArcGIS to generate cost raster data. The dataset focuses on township settlements and roads as core elements, aiming to quantify the accessibility and support capabilities of the regional transportation network and provide a scientific basis for transportation planning. The high accuracy and timeliness of township settlement data, combined with the timely updates of OSM road data, ensure the overall quality and reliability of the transportation data. Healthcare Data: This dataset covers the medical service time, population-weighted medical service time, and comprehensive costs of primary, secondary, and tertiary hospitals in the Qinghai-Tibet region. The hospital coordinate data is sourced from Baidu Maps, while the number of health technicians per capita and healthcare expenditure per capita are obtained from the National Health Commission. Based on hospital coordinates and cost raster data in the study area, the Cost Distance tool in ArcGIS is used to calculate the shortest time cost from each raster point to hospitals at various levels. The population-weighted medical service time is calculated through the cost raster fitted to the OSM road network. The comprehensive cost is calculated by integrating three indicators: population-weighted medical service time, the number of health technicians per capita, and healthcare expenditure per capita. The entropy weight method is used to determine the weight of each indicator, and the comprehensive cost values are sorted and classified before being linked to the county-level administrative units in ArcGIS. Education Data: This dataset focuses on the schooling time, population-weighted schooling time, and comprehensive costs of primary schools in the Qinghai-Tibet region. The data on primary school directories, the number of teachers per student, and educational expenditure per student are sourced from the Education Departments of the Tibet Autonomous Region and Qinghai Province, as well as local education bureaus. The population density data is provided by the WORLDPOP website and is corrected using multiple sources of data, including the seventh national population census and land use data, to generate a population density raster with a precision of 1 km × 1 km. The locations of primary schools are determined through Gaode Maps, Tianditu, and Baidu Maps, and their coordinates are corrected and incorporated into a spatial database. Based on primary school coordinates and cost raster data in the study area, the Cost Distance tool in ArcGIS is used to calculate the shortest time cost from each raster point to primary schools. The population-weighted schooling time is also calculated based on the cost raster fitted to the OSM road network. The comprehensive cost is calculated by integrating three indicators: population-weighted schooling time, the number of teachers per student, and educational expenditure per student. The entropy weight method is used to determine the weight of each indicator, and the comprehensive cost values are sorted and classified before being linked to the county-level administrative units in ArcGIS.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This is a data output from the GeoCrimeData project (http://geocrimedata.blogspot.com/). It contains Open Street Map data with derived measures of road integration (which can be used as a proxy for traffic volume). The data were derived from Open Street Map downloaded provided on the ShareGeo repository (e.g. for England: http://www.sharegeo.ac.uk/handle/10672/28) For more information about how the data was created, see: https://docs.google.com/document/d/16eNQKKxlLlh8H2Gayz86F68ZsTXUF72kJ1qiW2VUu7A/edit For other GeoCrimeData written material, see: https://docs.google.com/document/d/1gJ9B4BZNvL3w2DPfyv9vu-7P7_tnVf3F3H3rvygr1cc. Map data (c) OpenStreetMap contributors, CC-BY-SA This dataset was derived from OpenStreetMap. Access and use constraints are based on conditions set out in the OpenStreetMap Licence Agreement which can be found at http://wiki.openstreetmap.org/wiki/OpenStreetMap_License. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-11-10 and migrated to Edinburgh DataShare on 2017-02-21.
Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
License information was derived automatically
This data comes from the FINESS file and is integrated by the contributors to the OpenStreetMap project. They are under ODbL license which imposes identical sharing and the mandatory attribution mention must be “© OpenStreetMap contributors under ODbL license” in accordance with http://osm.org/copyright
The dataset includes both the resuscitation services of public hospitals and private institutions (non-profit or profit-making).
Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
License information was derived automatically
List of 78 Beach Rescue Posts (PS) of the Hérault department for the 2021 summer season. The data has been placed in the OpenStreetMap free collaborative database using the emergency=lifeguard tag. However, some fields not relevant to OSM have not been integrated. You can replay an extraction query from OSM at any time with this link: http://overpass-turbo.eu/s/1apL The list of fields is as follows: — ID — name — address — municipality — grouping = the SDIS34 grouping in which the emergency station is located — type = LITTORAL for seaside stations, LAC for lakeside stations — supervision = entity responsible for supervising the post in 2021 The data shall be provided in the following formats: — GeoPackage (because it’s much better than the Shape!), containing a layer of dots with the position of each post — CSV containing a dots layer with two fields for XY coordinates, as well as an associated CSVT file that specifies the type of each field All geographical coordinates are projected Lambert93 (EPSG:2154).
Departmental Houses of Solidarity To best meet the expectations of the Meurthe-et-Mosellans, the departmental council reorganizes its medico-social centers (CMS) and creates departmental houses of solidarities (MDS). The aim is to offer every inhabitant, whatever their age, their situation, their place of residence, a benevolent, close welcome. For the department, the objective is to guarantee equal access to the public service and to provide a quality service that makes it possible to find the solutions best suited to the needs expressed by everyone. Present throughout Meurthe-et-Moselle, the Maisons départementales des solidarités (MDS) are organised into sites and local reception points, particularly in rural areas. The MDS are composed of teams of professionals in the social and medico-social field. The tasks of the MDS shall be to: informing, accompanying and protecting individuals and families; health actions for families, children and pregnant women, to carry out prevention and integration measures to help combat exclusion. While guaranteeing the confidentiality of information for the respect of individuals, the MDS want to enable everyone to improve their living conditions and exercise their citizenship. The data was generated within the administrative limits of OpenStreetMap
Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
License information was derived automatically
This data comes from the FINESS file and is integrated by the contributors to the OpenStreetMap project. They are under ODbL license which imposes identical sharing and the mandatory attribution mention must be “© OpenStreetMap contributors under ODbL license” in accordance with http://osm.org/copyright
The dataset includes both the emergency services of public and private hospitals participating in the public service. There is no distinction between the reception of general, adult and/or paediatric emergencies.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
VGI projects such as OpenStreetMap (OSM) have grown in the past few years, resulting in more accurate, complete and up-to-date datasets. As a result, governments are now interested in VGI as a reliable source of information. Integrating geographic information requires solving semantic heterogeneity in a way that allows the identification of features in different datasets representing the same real phenomena. We present a method for identifying sets of homologous features in authoritative and VGI datasets. First, a domain ontology is used as a common knowledge among datasets. Second, direct mapping from the datasets to the domain ontology is done, resulting in new datasets using the same vocabulary and therefore, interoperable.
http://rightsstatements.org/vocab/InC/1.0/http://rightsstatements.org/vocab/InC/1.0/
The OSM Readiness Tool (V1.0) was developed in response to a request by industry partners, SBEnrc Project 2.2 Offsite Fabrication. The problem identified was: 'What will enable us to determine if Off-Ssite Manufacture (OSM) is suitable for a building project?' International studies indicate that benefits are accrued if OSM is considered at early stages and incorporated into the design. Successful OSM adoption can be increased though planning and engagement with all OSM stakeholders throughout the entire building process. Having OSM capability and capacity information available will assist the project team with decision-making concering adopting an OSM procurement model. Timing of the decision to adopt an OSM procurement model effects project outcomes: Early adoption of the OSM procurement model, with input from a wide variety of OSM stakeholders, is considered the low risk option. A high level of stakeholder engagement provides information concerning the availability of essential OSM capability and capacity. Expertise, from a wide range of stakeholders, supports integration of OSM into the total project at the Arrange Team stage. Adopting OSM at the Tendering stage could mean additional delays for delivery and possible problems with integrating OSM specifications into the Shop Drawings. Adopting an OSM procurement strategy after the Detail Design is completed can benefit a building project, but the benefits afforded by early AEC knowledge sharing relationships will be limited, thus constraining the effectiveness of an OSM procurement model indicating a medium level of risk. Adopting OSM at the Construction stage of a project is always an option. But, the lack of early project team planning and engagement will certainly mean delays and difficulty with on-site installation. Thus, late adoption of an OSM procurement model is considered a high risk delivery strategy.