To achieve true data interoperability is to eliminate format and data model barriers, allowing you to seamlessly access, convert, and model any data, independent of format. The ArcGIS Data Interoperability extension is based on the powerful data transformation capabilities of the Feature Manipulation Engine (FME), giving you the data you want, when and where you want it.In this course, you will learn how to leverage the ArcGIS Data Interoperability extension within ArcCatalog and ArcMap, enabling you to directly read, translate, and transform spatial data according to your independent needs. In addition to components that allow you to work openly with a multitude of formats, the extension also provides a complex data model solution with a level of control that would otherwise require custom software.After completing this course, you will be able to:Recognize when you need to use the Data Interoperability tool to view or edit your data.Choose and apply the correct method of reading data with the Data Interoperability tool in ArcCatalog and ArcMap.Choose the correct Data Interoperability tool and be able to use it to convert your data between formats.Edit a data model, or schema, using the Spatial ETL tool.Perform any desired transformations on your data's attributes and geometry using the Spatial ETL tool.Verify your data transformations before, after, and during a translation by inspecting your data.Apply best practices when creating a workflow using the Data Interoperability extension.

ArcMap'ten ArcGIS Pro'ya geçişle birlikte eski Personal Geodatabase (.mdb) verilerinizi daha yeni ve verimli olan File Geodatabase (.gdb) formatına ArcGIS Data Interoperability aracılığıyla topluca nasıl dönüştürebileceğinizi göreceksiniz.Alıştırmayı yapmak için gerekli tahmini süre: 30 DakikaYazılım gereksinimi: ArcGIS Data Interoperability

Temperature survey at 2 meters. This web map service (WMS) was published using ArcServer v. 10.1 and is compliant with OGC (Open Geospatial Consortium) version 1.30 specifications. This service provides dynamic, spatially referenced geographic information using data collected for the National Geothermal Data System (http://www.geothermaldata.org/). In addition to the WMS capabilities, this service was designed to be interoperable with both WFS (Web Feature Services) as well as KML (Keyhole Markup Language). The WFS capabilities allow the client to query, make additions and/or modifications to an existing dataset. WFS can be utilized through the interoperability extension in ArcCatalog. For more information on using the ArcGIS data interoperability extension visit http://www.esri.com/software/arcgis/extensions/datainteroperability /common-questions.html. A KML service allows the client to view an image of the data in three dimensions, using free software available for download on the internet such as ArcGIS Explorer or Google Earth. For more information on OGC specifications, visit http://www.opengeospatial.org/standards.

A combination of stormwater system data throughout Stark County, Ohio. The data is combined using an ETL via the data interoperability extension for ArcGIS Pro. Each weekend, the ETL is automatically ran via Python/Windows Task Scheduler to update the data with any changes from the past week from each of the source datasets. The source data is stored in ArcGIS SDE databases that Stark County GIS (SCGIS) provides for departments, cities, villages, and townships within the county. SCGIS currently maintains SDE databases for Canton, Alliance, Louisville, North Canton, Beach City, Easton Canton, Minerva, Meyers Lake, Stark County Engineer (SCE), and each of the townships. In addition to those datasets (which are updated weekly), this layer also includes data from the cities of Massillon and Canal Fulton, which are not stored in databases maintained by SCGIS. Data for those two cities is updated separately as new iterations become available.As this layer encompasses the entire county, source feature classes are consolidated into 4 layers to improve performance on ArcGIS Online. Discharge points are the point at which water exits part of the stormwater system, such as the outlet of a pipe or ditch. It includes outfalls defined under NPDES Phase II. Structures includes both inlets (catch basins, yard drains, etc.) and manholes. Pipes includes storm sewers, as well as culverts (pipes in which both ends are daylit). Finally, the ditches layer includes roadside ditches, as well as off-road ditches in some areas/instances.

The ArcGIS Data Interoperability extension enables you to work with data stored in a significant number of formats that are native and non-native to ArcGIS. From a simple translation between two formats to complex transformations on data content and structure, this extension provides the solution to overcome interoperability barriers.After completing this course, you will be able to:Use existing translation parameters to control data translations.Translate multiple datasets at once.Use parameters to change the coordinate system of the data.

The geospatial data fusion market is experiencing robust growth, driven by increasing demand for location-based intelligence across various sectors. The convergence of diverse data sources, including satellite imagery, LiDAR, sensor networks, and social media, is fueling innovation in applications such as precision agriculture, urban planning, environmental monitoring, and disaster response. This market's expansion is propelled by advancements in data processing capabilities, cloud computing infrastructure, and the development of sophisticated algorithms for data integration and analysis. While the market size and CAGR were not explicitly provided, a reasonable estimation, considering the industry trends and the presence of key players like Esri, indicates a significant market value, potentially exceeding $5 billion in 2025, with a Compound Annual Growth Rate (CAGR) of around 15% projected through 2033. This growth is further supported by the rising adoption of IoT devices and the increasing availability of high-resolution geospatial data. However, challenges remain. Data interoperability issues, the need for skilled professionals to manage complex datasets, and concerns regarding data security and privacy could potentially restrain market growth. Nevertheless, ongoing technological advancements and the increasing recognition of the value of geospatial insights across various industries are likely to overcome these hurdles, ensuring continued market expansion in the forecast period. The segmentation of the market is likely diverse, encompassing software, services, and hardware solutions tailored to specific industry needs, with further regional variations in adoption rates driven by factors such as infrastructure development and government regulations. The competitive landscape involves established players alongside emerging technology companies, all vying for market share through innovative solutions and strategic partnerships.

The region is the top tier of local government in New Zealand. There are 16 regions of New Zealand (Part 1 of Schedule 2 of the Local Government Act 2002). Eleven are governed by an elected regional council, while five are governed by territorial authorities (the second tier of local government) who also perform the functions of a regional council and thus are known as unitary authorities. These unitary authorities are Auckland Council, Nelson City Council, Gisborne, Tasman, and Marlborough District Councils. The Chatham Islands Council also perform some of the functions of a regional council, but is not strictly a unitary authority. Unitary authorities act as regional councils for the purposes of a wide range of Acts and regulations. Regional council areas are based on water catchment areas. Regional councils are responsible for the administration of many environmental and public transport matters.Regional Councils were established in 1989 after the abolition of the 22 local government regions. The local government act 2002, requires the boundaries of regions to confirm as far as possible to one or more water catchments. When determining regional boundaries, the local Government commission gave consideration to regional communities of interest when selecting water catchments to included in a region. It also considered factors such as natural resource management, land use planning and environmental matters. Some regional boundaries are conterminous with territorial authority boundaries but there are many exceptions. An example is Taupo District, which is split between four regions, although most of its area falls within the Waikato Region. Where territorial local authorities straddle regional council boundaries, the affected area have been statistically defined in complete area units. Generally regional councils contain complete territorial authorities. The unitary authority of the Auckland Council was formed in 2010, under the Local Government (Tamaki Makarau Reorganisation) Act 2009, replacing the Auckland Regional Council and seven territorial authorities.The seaward boundary of any costal regional council is the twelve mile New Zealand territorial limit. Regional councils are defined at meshblock and area unit level.Regional Councils included in the 2013 digital pattern are:Regional Council CodeRegional Council Name01Northland Region02Auckland Region03Waikato Region04Bay of Plenty Region05Gisborne Region06Hawke's Bay Region07Taranaki Region08Manawatu-Wanganui Region09Wellington Region12West Coast Region13Canterbury Region14Otago Region15Southland Region16Tasman Region17Nelson Region18Marlborough Region99Area Outside RegionAs at 1stJuly 2007, Digital Boundary data became freely available.Deriving of Output FilesThe original vertices delineating the meshblock boundary pattern were digitised in 1991 from 1:5,000 scale urban maps and 1:50,000 scale rural maps. The magnitude of error of the original digital points would have been in the range of +/- 10 metres in urban areas and +/- 25 metres in rural areas. Where meshblock boundaries coincide with cadastral boundaries the magnitude of error will be within the range of 1–5 metres in urban areas and 5 - 20 metres in rural areas. This being the estimated magnitude of error of Landonline.The creation of high definition and generalised meshblock boundaries for the 2013 digital pattern and the dissolving of these meshblocks into other geographies/boundaries were completed within Statistics New Zealand using ESRI's ArcGIS desktop suite and the Data Interoperability extension with the following process: 1. Import data and all attribute fields into an ESRI File Geodatabase from LINZ as a shapefile2. Run geometry checks and repairs.3. Run Topology Checks on all data (Must Not Have Gaps, Must Not Overlap), detailed below.4. Generalise the meshblock layers to a 1m tolerance to create generalised dataset. 5. Clip the high definition and generalised meshblock layers to the coastline using land water codes.6. Dissolve all four meshblock datasets (clipped and unclipped, for both generalised and high definition versions) to higher geographies to create the following output data layers: Area Unit, Territorial Authorities, Regional Council, Urban Areas, Community Boards, Territorial Authority Subdivisions, Wards Constituencies and Maori Constituencies for the four datasets. 7. Complete a frequency analysis to determine that each code only has a single record.8. Re-run topology checks for overlaps and gaps.9. Export all created datasets into MapInfo and Shapefile format using the Data Interoperability extension to create 3 output formats for each file. 10. Quality Assurance and rechecking of delivery files.The High Definition version is similar to how the layer exists in Landonline with a couple of changes to fix topology errors identified in topology checking. The following quality checks and steps were applied to the meshblock pattern:Translation of ESRI Shapefiles to ESRI geodatabase datasetThe meshblock dataset was imported into the ESRI File Geodatabase format, required to run the ESRI topology checks. Topology rules were set for each of the layers. Topology ChecksA tolerance of 0.1 cm was applied to the data, which meant that the topology engine validating the data saw any vertex closer than this distance as the same location. A default topology rule of “Must Be Larger than Cluster Tolerance” is applied to all data – this would highlight where any features with a width less than 0.1cm exist. No errors were found for this rule.Three additional topology rules were applied specifically within each of the layers in the ESRI geodatabase – namely “Must Not Overlap”, “Must Not Have Gaps” and “"Area Boundary Must Be Covered By Boundary Of (Meshblock)”. These check that a layer forms a continuous coverage over a surface, that any given point on that surface is only assigned to a single category, and that the dissolved boundaries are identical to the parent meshblock boundaries.Topology Checks Results: There were no errors in either the gap or overlap checks.GeneralisingTo create the generalised Meshblock layer the “Simplify Polygon” geoprocessing tool was used in ArcGIS, with the following parameters:Simplification Algorithm: POINT_REMOVEMaximum Allowable Offset: 1 metreMinimum Area: 1 square metreHandling Topological Errors: RESOLVE_ERRORSClipping of Layers to CoastlineThe processed feature class was then clipped to the coastline. The coastline was defined as features within the supplied Land2013 with codes and descriptions as follows:11- Island – Included12- Mainland – Included21- Inland Water – Included22- Inlet – Excluded23- Oceanic –Excluded33- Other – Included.Features were clipped using the Data Interoperability extension, attribute filter tool. The attribute filter was used on both the generalised and high definition meshblock datasets creating four meshblock layers. Each meshblock dataset also contained all higher geographies and land-water data as attributes. Note: Meshblock 0017001 which is classified as island, was excluded from the clipped meshblock layers, as most of this meshblock is oceanic. Dissolve meshblocks to higher geographiesStatistics New Zealand then dissolved the ESRI meshblock feature classes to the higher geographies, for both the full and clipped dataset, generalised and high definition datasets. To dissolve the higher geographies, a model was built using the dissolver, aggregator and sorter tools, with each output set to include geography code and names within the Data Interoperability extension. Export to MapInfo Format and ShapfilesThe data was exported to MapInfo and Shapefile format using ESRI's Data Interoperability extension Translation tool. Quality Assurance and rechecking of delivery filesThe feature counts of all files were checked to ensure all layers had the correct number of features. This included checking that all multipart features had translated correctly in the new file.

A combination of stormwater structure data throughout Stark County, Ohio. Structures includes both inlets (catch basins, yard drains, etc.) and manholes. The data is combined using an ETL via the data interoperability extension for ArcGIS Pro. Each weekend, the ETL is automatically ran via Python/Windows Task Scheduler to update the data with any changes from the past week from each of the source datasets. The source data is stored in ArcGIS SDE databases that Stark County GIS (SCGIS) provides for departments, cities, villages, and townships within the county. SCGIS currently maintains SDE databases for Canton, Alliance, Louisville, North Canton, Beach City, Easton Canton, Minerva, Meyers Lake, Stark County Engineer (SCE), and each of the townships. In addition to those datasets (which are updated weekly), this layer also includes data from the cities of Massillon and Canal Fulton, which are not stored in databases maintained by SCGIS. Data for those two cities is updated separately as new iterations become available.

This web map service (WMS) was published using ArcServer v. 10.1 and is compliant with OGC (Open Geospatial Consortium) version 1.30 specifications. This service provides dynamic, spatially referenced geographic information using data collected for the National Geothermal Data System (http://www.geothermaldata.org/). In addition to the WMS capabilities, this service was designed to be interoperable with both WFS (Web Feature Services) as well as KML (Keyhole Markup Language). The WFS capabilities allow the client to query, make additions and/or modifications to an existing dataset. WFS can be utilized through the interoperability extension in ArcCatalog. For more information on using the ArcGIS data interoperability extension visit http://www.esri.com/software/arcgis/extensions/datainteroperability /common-questions.html. A KML service allows the client to view an image of the data in three dimensions, using free software available for download on the internet such as ArcGIS Explorer or Google Earth. For more information on OGC specifications, visit http://www.opengeospatial.org/standards.

description: OBIS-USA provides aggregated, interoperable biogeographic data collected primarily from U.S. waters and oceanic regions--the Arctic, the Atlantic and Pacific oceans, the Caribbean Sea, Gulf of Mexico and the Great Lakes. It provides access to datasets from state and federal agencies as well as educational and research institutions. OBIS-USA handles both specimen-based data and survey results. Survey data come from recovered archives and current research activities. The datasets document where and when species were observed or collected, bringing together marine biogeographic data that are spatially, taxonomically, and temporally comprehensive. The public OBIS-USA site (http://www.usgs.gov/obis-usa) provides actual data contents as well as summary data about what is contained in each dataset to assist users in evaluating suitability for use. Current functionality allows the user to locate, view, and aggregate the datasets and FGDC compliant metadata as well as to view and search the taxonomic, geographic, and temporal extent. To promote data interoperability, the data are available in accordance with the marine-focused implementation of the Darwin Core data standard. In addition to basic download functions (tab-delimited), OBIS-USA offers web services for query flexibility and a wide range of output formats, such as kml, NetCDF, MATLAB, json, and graph or map output, to enable diverse types of scientific and geospatial data use and analysis platforms and products. OBIS-USA's two web services (ERDDAP and GeoServer) enable integration of OBIS-USA biogeographic data with other data types, such as seafloor geology, physical oceanography, water chemistry, and climate data. The NOAA Environmental Research Division Data Access Program(ERRDDAP) enables users to query scientific data by flexible parameters and obtain output in many formats. Access can be found at http://www1.usgs.gov/erddap/tabledap/AllMBG.html . OBIS-USA uses the tabledap component of ERDDAP to access Darwin-Core-type tabular spatial data; tabledap is a superset of the OPeNDAP DAP constraint protocol. OBIS-USA offers an ESRI REST Service with access to Darwin-Core-type point data at http://gis1.usgs.gov/arcgis/rest/services/OBISUSA/OBIS_USA_All_Marine_Biogeographic_Records/MapServer/ and an OGC compliant Web Mapping Service (wms) http://gis1.usgs.gov/arcgis/services/OBISUSA/OBIS_USA_All_Marine_Biogeographic_Records/MapServer/WMSServer?request=GetCapabilities&service=WMS. OBIS-USA and collaborators are further deploying the Darwin Core standard to capture richer information, such as absence and abundance, observations on effort, individual tracking, and more advanced biogeography capabilities. Data are accepted into OBIS-USA from the data originator or holder, minimizing the burden on the participant. OBIS-USA works with data providers to understand the best process to transfer the data, review the data prior to their release, gather comprehensive metadata, and then allow public access to this information. Becoming part of the OBIS-USA network is intended to have tangible benefits for participants, for example, freeing the participant from responding to requests for data and alleviating security concerns since users do not directly access the participant's computers.; abstract: OBIS-USA provides aggregated, interoperable biogeographic data collected primarily from U.S. waters and oceanic regions--the Arctic, the Atlantic and Pacific oceans, the Caribbean Sea, Gulf of Mexico and the Great Lakes. It provides access to datasets from state and federal agencies as well as educational and research institutions. OBIS-USA handles both specimen-based data and survey results. Survey data come from recovered archives and current research activities. The datasets document where and when species were observed or collected, bringing together marine biogeographic data that are spatially, taxonomically, and temporally comprehensive. The public OBIS-USA site (http://www.usgs.gov/obis-usa) provides actual data contents as well as summary data about what is contained in each dataset to assist users in evaluating suitability for use. Current functionality allows the user to locate, view, and aggregate the datasets and FGDC compliant metadata as well as to view and search the taxonomic, geographic, and temporal extent. To promote data interoperability, the data are available in accordance with the marine-focused implementation of the Darwin Core data standard. In addition to basic download functions (tab-delimited), OBIS-USA offers web services for query flexibility and a wide range of output formats, such as kml, NetCDF, MATLAB, json, and graph or map output, to enable diverse types of scientific and geospatial data use and analysis platforms and products. OBIS-USA's two web services (ERDDAP and GeoServer) enable integration of OBIS-USA biogeographic data with other data types, such as seafloor geology, physical oceanography, water chemistry, and climate data. The NOAA Environmental Research Division Data Access Program(ERRDDAP) enables users to query scientific data by flexible parameters and obtain output in many formats. Access can be found at http://www1.usgs.gov/erddap/tabledap/AllMBG.html . OBIS-USA uses the tabledap component of ERDDAP to access Darwin-Core-type tabular spatial data; tabledap is a superset of the OPeNDAP DAP constraint protocol. OBIS-USA offers an ESRI REST Service with access to Darwin-Core-type point data at http://gis1.usgs.gov/arcgis/rest/services/OBISUSA/OBIS_USA_All_Marine_Biogeographic_Records/MapServer/ and an OGC compliant Web Mapping Service (wms) http://gis1.usgs.gov/arcgis/services/OBISUSA/OBIS_USA_All_Marine_Biogeographic_Records/MapServer/WMSServer?request=GetCapabilities&service=WMS. OBIS-USA and collaborators are further deploying the Darwin Core standard to capture richer information, such as absence and abundance, observations on effort, individual tracking, and more advanced biogeography capabilities. Data are accepted into OBIS-USA from the data originator or holder, minimizing the burden on the participant. OBIS-USA works with data providers to understand the best process to transfer the data, review the data prior to their release, gather comprehensive metadata, and then allow public access to this information. Becoming part of the OBIS-USA network is intended to have tangible benefits for participants, for example, freeing the participant from responding to requests for data and alleviating security concerns since users do not directly access the participant's computers.

The Building Information Modeling (BIM) software market, valued at $8.72 billion in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 13.90% from 2025 to 2033. This expansion is fueled by several key factors. Increasing adoption of digital technologies within the architecture, engineering, and construction (AEC) industries is a primary driver. BIM software offers significant advantages in improving project planning, collaboration, and cost management, leading to increased efficiency and reduced errors. The rising complexity of construction projects globally, coupled with stringent regulatory requirements for building safety and sustainability, further necessitates the use of advanced BIM solutions. Growth is also being driven by the increasing availability of cloud-based BIM platforms, which enhance accessibility and collaboration among stakeholders. The market is segmented by solution type (software and services), application (commercial, residential, industrial, and others), and end-user (contractors, architects, facilities managers, and others). North America currently holds a significant market share, driven by early adoption and robust technological infrastructure; however, Asia Pacific is projected to witness substantial growth due to rapid urbanization and infrastructure development. The competitive landscape is marked by both established players like Autodesk, Bentley Systems, and Nemetschek, and emerging innovative companies. These companies are continuously investing in research and development to enhance functionalities, integrate new technologies like artificial intelligence and machine learning, and develop user-friendly interfaces to cater to a wider user base. While the market faces some restraints such as the high initial investment costs of BIM software and the need for skilled professionals, the long-term benefits and increasing awareness of its advantages are expected to outweigh these challenges. The market's future trajectory is positive, with continued growth driven by technological advancements, industry adoption, and the overarching need for efficient and sustainable construction practices. The projected market size in 2033 will significantly surpass the 2025 value, reflecting the considerable growth potential of the BIM software market. 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.

A combination of stormwater discharge point data throughout Stark County, Ohio. Discharge points are the point at which water exits part of the stormwater system, such as the outlet of a pipe or ditch. It includes outfalls defined under NPDES Phase II. The data is combined using an ETL via the data interoperability extension for ArcGIS Pro. Each weekend, the ETL is automatically ran via Python/Windows Task Scheduler to update the data with any changes from the past week from each of the source datasets. The source data is stored in ArcGIS SDE databases that Stark County GIS (SCGIS) provides for departments, cities, villages, and townships within the county. SCGIS currently maintains SDE databases for Canton, Alliance, Louisville, North Canton, Beach City, Easton Canton, Minerva, Meyers Lake, Stark County Engineer (SCE), and each of the townships. In addition to those datasets (which are updated weekly), this layer also includes data from the cities of Massillon and Canal Fulton, which are not stored in databases maintained by SCGIS. Data for those two cities is updated separately as new iterations become available.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Buildings feature layer for a section in UJI campus derived from a True Orthoimage of the area with an intention of checking the accuracy and completeness of the OpenStreetMap data for the area. The True Orthoimage generated from processing some Drone imagery data of the area.

The Government Open Data Management (ODM) Platform market is experiencing robust growth, driven by increasing government initiatives promoting transparency, accountability, and citizen engagement. The market's expansion is fueled by the rising need for efficient data management, analysis, and dissemination across various sectors like healthcare, transportation, and public safety. Cloud-based solutions are gaining significant traction due to their scalability, cost-effectiveness, and accessibility. The integration of advanced analytics and AI capabilities within ODM platforms is further enhancing their value proposition, enabling governments to derive actionable insights from open data for better policy-making and improved public services. While North America currently holds a significant market share due to early adoption and established technology infrastructure, regions like Asia Pacific are witnessing rapid growth driven by increasing digitalization and government investments in open data initiatives. The market is segmented by application (IT & Cybersecurity, Aerospace & Defense, Healthcare & Pharmaceuticals, Energy & Utilities, Logistics & Transportation, and Others) and by type (Cloud-Based and On-Premises). Competition is relatively high, with established players like Socrata and CKAN facing competition from emerging solution providers offering innovative functionalities. However, data security and privacy concerns, along with the complexities of data integration and standardization, pose challenges to market growth. The forecast period (2025-2033) anticipates sustained growth, particularly in developing economies where the potential for utilizing open data for societal improvement is immense. Factors like increasing cybersecurity threats and the need for robust data governance frameworks will shape the market landscape. The increasing adoption of open data standards and interoperability solutions will be crucial for driving wider adoption and maximizing the benefits of government open data initiatives. Specific application segments, such as healthcare and transportation, are poised for significant growth due to the potential for improved public health outcomes and optimized transportation management through data-driven insights. Strategic partnerships between government agencies and technology providers will be critical in accelerating market penetration and ensuring the successful implementation of ODM platforms. A conservative estimation of CAGR (considering a global market size around $2 billion in 2025) suggests an impressive expansion over the forecast period.

description: This dataset contains well log observations compiled by the New Mexico Bureau of Geology and Mineral Resources and published as a Web feature service for the National Geothermal Data System. Fields in the Well Log Observation Content Model will become XML elements in interchange documents for WMS simple features provided by a node in the USGIN network. Each observation includes a Log URI, API Number, Latitude and Longitude spatially referenced to EPSG: 4326 (for interoperability). The data contained in the submitted dataset is available as an Excel Spreadsheet, ESRI service, WMS and WFS service. Link to Well Log Observation Content Model: http://stategeothermaldata.org/data_delivery/content_models/well_log_observation.; abstract: This dataset contains well log observations compiled by the New Mexico Bureau of Geology and Mineral Resources and published as a Web feature service for the National Geothermal Data System. Fields in the Well Log Observation Content Model will become XML elements in interchange documents for WMS simple features provided by a node in the USGIN network. Each observation includes a Log URI, API Number, Latitude and Longitude spatially referenced to EPSG: 4326 (for interoperability). The data contained in the submitted dataset is available as an Excel Spreadsheet, ESRI service, WMS and WFS service. Link to Well Log Observation Content Model: http://stategeothermaldata.org/data_delivery/content_models/well_log_observation.

The Spain Geospatial Imagery Analytics Market is experiencing robust growth, projected to reach a significant size within the forecast period (2025-2033). Driven by increasing adoption of advanced technologies like AI and machine learning in various sectors, the market is witnessing a Compound Annual Growth Rate (CAGR) of 23%. This growth is fueled by the increasing need for precise and timely geospatial data across diverse verticals. Key sectors such as agriculture, insurance, defense and security, and environmental monitoring are adopting geospatial imagery analytics for improved efficiency and informed decision-making. The market is segmented by type (imagery and video analytics), deployment mode (on-premise and cloud), organization size (SMEs and large enterprises), and vertical. The cloud deployment model is gaining traction due to its scalability and cost-effectiveness. Large enterprises are leading the adoption, but SMEs are increasingly recognizing the value proposition of geospatial analytics, further driving market expansion. While data limitations prevent precise Spain-specific figures, the overall market trends and CAGR strongly suggest substantial growth potential in the Spanish market, mirroring global trends. The competitive landscape features both established tech giants like Google, Microsoft, and Oracle, and specialized geospatial analytics companies such as Hexagon AB, ESRI, and Trimble. The presence of these players ensures a dynamic market with continuous innovation and competitive pricing. The market's future trajectory hinges on continued technological advancements, government support for digital transformation initiatives within various sectors, and the increasing availability of high-resolution satellite and aerial imagery. Challenges include data privacy concerns, the need for skilled professionals, and ensuring data interoperability. Despite these challenges, the Spain Geospatial Imagery Analytics Market is poised for considerable expansion throughout the forecast period, providing significant opportunities for both established players and emerging market entrants. Recent developments include: July 2023: Databricks is the provider of a big data tool named Databricks Lakehouse Platform, which merges data science, data engineering, machine learning, and analytics within a single platform. To provide even more valuable insights to data scientists, spatial analytics is often added to the mix to put large amounts of data in the proper context. A new partnership with Esri brings advanced spatial analytics capabilities in Esri’s ArcGIS software to the Databricks Lakehouse Platform, allowing users to perform spatial analytics at scale., June 2023: Meta Platforms, the parent organization of Facebook and Instagram, announced its plans to grant researchers access to components of its new "human-like" artificial intelligence (AI) model. This advanced model, named I-JEPA, boasts superior accuracy in analyzing and completing unfinished images compared to existing models. Unlike other generative AI models primarily focusing on neighboring pixels, I-JEPA utilizes background knowledge about the world to fill in the missing portions of images.. Key drivers for this market are: Increasing Adoption of Location-based Services, Increasing Demand for Safe and Secure Mining Operations. Potential restraints include: Increasing Adoption of Location-based Services, Increasing Demand for Safe and Secure Mining Operations. Notable trends are: Cloud Segment is Expected to Hold a Significant Share of the Market.

This dataset contains well log observations compiled by the Kentucky Geological Survey and published as a Web feature service for the National Geothermal Data System. Fields in the Well Log Observation Content Model will become XML elements in interchange documents for WMS simple features provided by a node in the USGIN network. Each observation includes a Log URI, API Number, Latitude and Longitude spatially referenced to EPSG: 4326 (for interoperability). The data contained in the submitted dataset is available as an Excel Spreadsheet, ESRI service, WMS and WFS service. Link to Well Log Observation Content Model: http://stategeothermaldata.org/data_delivery/content_models/well_log_observation.

description: This dataset contains wellheader features data compiled by the Wyoming Geological Survey, and published as a Web feature service for the National Geothermal Data System. Fields in the WellHeader Feature Content Model will become XML elements in interchange documents for WMS simple features provided by a node in the USGIN network. Each feature includes a label, drilled depth (if available), location uncertainty and source. The dataset is spatially referenced to EPSG: 4326 (for interoperability). The data contained in the submitted dataset is available as an Excel Spreadsheet, ESRI service, WMS and WFS service. Link to Well header feature Content Model: http://www.stategeothermaldata.org/data_delivery/content_models/well_header.; abstract: This dataset contains wellheader features data compiled by the Wyoming Geological Survey, and published as a Web feature service for the National Geothermal Data System. Fields in the WellHeader Feature Content Model will become XML elements in interchange documents for WMS simple features provided by a node in the USGIN network. Each feature includes a label, drilled depth (if available), location uncertainty and source. The dataset is spatially referenced to EPSG: 4326 (for interoperability). The data contained in the submitted dataset is available as an Excel Spreadsheet, ESRI service, WMS and WFS service. Link to Well header feature Content Model: http://www.stategeothermaldata.org/data_delivery/content_models/well_header.

description: These data contains aqueous well chemistry information compiled by the Geological Survey of Alabama, and published as a Web feature service for the National Geothermal Data System. Fields in the Aqueous Chemistry Content Model will become XML elements in interchange documents for WMS simple features provided by a node in the USGIN network. Each chemistry analysis includes a fluid temperature, source/citation, latitude and longitude referenced to EPSG:4326 (for interoperability). The data contained in the submitted dataset(s) are available as Excel Spreadsheets, ESRI service, WMS and WFS services.For more information see links provided.; abstract: These data contains aqueous well chemistry information compiled by the Geological Survey of Alabama, and published as a Web feature service for the National Geothermal Data System. Fields in the Aqueous Chemistry Content Model will become XML elements in interchange documents for WMS simple features provided by a node in the USGIN network. Each chemistry analysis includes a fluid temperature, source/citation, latitude and longitude referenced to EPSG:4326 (for interoperability). The data contained in the submitted dataset(s) are available as Excel Spreadsheets, ESRI service, WMS and WFS services.For more information see links provided.