This ArcGIS model inserts a file name into a feature class attribute table. The tool allows an user to identify features by a field that reference the name of the original file. It is useful when an user have to merge multiple feature classes and needs to identify which layer the features come from.

Includes settings for a CPI (Crop Production Index) Generation toolbox and instructions on how to alter it to be usable for your county.This documentation assumes the user has a basic understanding of ArcGIS, its tools, and its data structure, Model Builder. Arcade and Python Scripting used here will be covered in the documentation.

This three-dimensional (3-D) building massing model of New York City is one of the many digital tools provided by the Office of Technology and Innovation. There are three file formats available to download via the buttons below. For more information on all the tools available, view the OTI's Digital Tools web page.

This permit conversion tool converts ePermit .xls files to quarter-quarter or lat/long locations. Also included is a public lands survey geodatabase necessary to run the POU tool. This Model Builder toolset is available for ArcGIS 10.1-5. The March 2018 update provided here tests for field types and processes the fields accordingly.

The Gravity model toolbox, a programmed ArcGIS tool to map and prioritize the potential corridors of urban green space.

The purpose of the Tool is to use CPI, CER (Crop Equivalency Rating) or NCCPI (National Commodity Crop Productivity Index) to assess tax values accurately and fairly, regarding tillable acres of land. The steps in this document will focus solely on CPI values, but can be modified to use CER or NCCPI data. This project was created by counties for counties to use as a no cost solution for assessing tax values to tillable acres.This documentation assumes the user has a basic understanding of ArcGIS, its tools, and its data structure, Model Builder, and basic Arcade and Python Scripting.

Construction Tools Detection

## Overview

Construction Tools Detection is a dataset for object detection tasks - it contains Equipments annotations for 751 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us online at http://goto.arcgisonline.com/earthobs2/REMSS_SeaSurfaceTempSea Surface Temperature is a key climate and weather measurement used for weather prediction, ocean forecasts, tropical cyclone forecasts, and in coastal applications such as fisheries, pollution monitoring and tourism. El Niño and La Niña are two examples of climate events which are forecast through the use of sea surface temperature maps. The Naval Oceanographic Office sea surface temperature dataset is calculated from satellite-based microwave and infrared imagery. These data are optimally interpolated to provide a daily, global map of the midday (12:00 pm) sea surface temperature. Learn more about the source data. Phenomenon Mapped: Sea Surface TemperatureUnits: Degrees CelsiusTime Interval: DailyTime Extent: 2008/04/01 12:00:00 UTC to presentCell Size: 11 kmSource Type: ContinuousPixel Type: Floating PointData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global OceansSource: Naval Oceanographic OfficeUpdate Cycle: SporadicArcGIS Server URL: http://earthobs2.arcgis.com/arcgisTime: This is a time-enabled layer. It shows the average sea surface temperature during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the average of all days in the time extent. Minimum temporal resolution is one day; maximum is one month.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder. Units are in degrees Celsius, and there is a processing template to convert pixels to Fahrenheit. See this Esri blog post for more information on how to use this layer in your analysis. Do not use this layer for analysis while the Cartographic Renderer processing template is applied.This layer is part of the Living Atlas of the World that provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.

Marine life have certain thresholds for temperature that they can live in. For instance, deep-water corals have been recorded in temperatures of -1⁰C. Seafloor temperatures generally decrease with increasing depth. Phenomenon Mapped: Seafloor temperatureUnits: Degrees CelsiusCell Size: 30 arc seconds, approximately 1 kmSource Type: DiscretePixel Type: Signed integerSpatial Reference: GCS_WGS_1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global oceansSource: Marine Conservation Institute (MCI)Citation: Boyer TP, Levitus S, Garcia HE, Locamini RA, Stephens C, et al. (2005) Objective analyses of annual, seasonal, and monthly temperature and salinity for the World Ocean on a 0.25° grid. International Journal of Climatology 25: 931–945.Publication Date: 2005ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Marine Conservation Institute used this dataset as an input to a predictive habitat model documented in the publication Global Habitat Suitability for Framework-Forming Cold-Water Corals.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – Celsius to Fahrenheit, Unit Conversion – Celsius to Kelvin, and Cartographic Renderer - see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

Software reverse engineering and reengineering are becoming common in the field of games and website development. Simulation and modeling play an important role in understanding the flow of the overall system. Business process modeling notation (BPMN) is used to show the overall architecture of the business process. Simulated business process re-engineering is essential for implementing change or creating new processes. The simulation model explains whether a change will be successful or not prior to adopting any new business processes or other changes. Some available tools help convert the BPMN to a simulating BPMN model but converting the discrete event simulation model build in commercial off the shelf simulation packages like Simul8 to the BPMN to help generate business process simulation to BPMN is also a key challenge. This framework is introduced to convert the simulation model to BPMN using the reverse engineering concept to understand how the converting tools convert the BPMN model to the simulation model. After understanding this process, the concept of reengineering will be used to build a BPMN from the simulation model. The framework is divided into three main parts model translation, model mapping, and model formation. For model building, two simulation tools Simul8 and BPSimulator are used. It is then tested on two case studies bank and product manufacturing. The output shows the BPMN model is generated from the simulation model within less time on a single click saving time and resources for developing BPMN model first and then making simulation model for testing purpose.

Headquarters building model.

Ocean depth plays an important role in the distribution and abundance of living organisms and has important implications for shipping and offshore development projects such as wind power and oil extraction.Phenomenon Mapped: Seafloor depth, bathymetryUnits: Meters below sea levelCell Size: 30 arc seconds, approximately 1 kmSource Type: DiscretePixel Type: Signed integerSpatial Reference: GCS_WGS_1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global oceansSource: Marine Conservation Institute (MCI)Citation: Becker JJ, Sandwell DT, Smith WHF, Braud J, Binder B, et al. (2009) Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. Marine Geodesy 32: 355–371.Publication Date: 2009ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Marine Conservation Institute used this dataset as an input to a predictive habitat model documented in the publication Global Habitat Suitability for Framework-Forming Cold-Water Corals.The source data is available from the Scripps Institution of Oceanography Satellite Geodesy Webpage.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – meters to feet, Cartographic Renderer, Aspect, Slope, and Hillshade - see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

Building Thermal Model Calibration Tools Market Outlook

According to our latest research, the global building thermal model calibration tools market size reached USD 528.4 million in 2024, with a robust compound annual growth rate (CAGR) of 9.2% projected through the forecast period. By 2033, the market is expected to attain a value of USD 1,172.6 million. This sustained growth is primarily driven by the increasing emphasis on energy efficiency and stringent building performance regulations worldwide, which are compelling stakeholders to adopt advanced calibration solutions for accurate thermal modeling and energy optimization.

One of the primary growth drivers for the building thermal model calibration tools market is the rising global focus on sustainable construction and energy conservation. As governments and regulatory bodies across the globe enact stricter codes for building energy performance, the demand for precise calibration tools has soared. These tools enable building owners and energy consultants to align simulated thermal performance with real-world data, thus ensuring compliance and unlocking cost savings. The integration of advanced analytics and artificial intelligence into calibration software has further enhanced the accuracy and usability of these tools, making them indispensable for both new construction and retrofitting projects. The proliferation of green building certifications, such as LEED and BREEAM, has also fueled market adoption, as accurate thermal modeling is a prerequisite for certification and ongoing performance monitoring.

Another significant factor propelling market expansion is the rapid digitalization of the construction and facility management sectors. The adoption of Building Information Modeling (BIM) and the Internet of Things (IoT) has created a data-rich environment, enabling more granular and dynamic calibration of thermal models. This digital transformation has not only improved the precision of calibration but also reduced the time and labor costs associated with manual methods. As a result, both large enterprises and small to medium-sized facilities are increasingly investing in calibration tools to optimize energy usage, reduce operational expenses, and enhance occupant comfort. The shift towards cloud-based deployment modes has further democratized access to sophisticated calibration solutions, making them viable for a broader spectrum of end-users.

In addition, the market is benefiting from the growing awareness of climate change and the urgent need to reduce greenhouse gas emissions from the built environment. Governments, corporations, and institutional stakeholders are prioritizing investments in energy management technologies, including thermal model calibration tools, as part of their sustainability strategies. This trend is particularly pronounced in regions with aggressive carbon reduction targets and significant investments in smart city infrastructure. The convergence of regulatory pressure, technological innovation, and environmental responsibility is creating a fertile landscape for the continued growth of the building thermal model calibration tools market.

From a regional perspective, North America and Europe currently dominate the market, accounting for over 65% of global revenue in 2024, driven by mature construction industries, advanced regulatory frameworks, and widespread adoption of energy efficiency technologies. However, the Asia Pacific region is emerging as a high-growth market, buoyed by rapid urbanization, increasing construction activity, and government initiatives promoting sustainable building practices. The Middle East & Africa and Latin America are also witnessing steady growth, supported by infrastructure modernization and rising energy costs. Each region presents unique opportunities and challenges, shaped by local regulations, climate conditions, and market maturity.

Component Analysis

The component segment of the building thermal model

Building Thermal Model Calibration Tools Market Outlook

According to our latest research, the global market size for Building Thermal Model Calibration Tools reached USD 752 million in 2024, reflecting a robust demand for energy efficiency and advanced building performance analytics. The market is projected to expand at a CAGR of 10.2% from 2025 to 2033, reaching a forecasted value of USD 1.96 billion by 2033. This growth trajectory is primarily driven by the increasing adoption of digital twin technologies, stringent regulatory standards on building energy consumption, and a rising emphasis on sustainability across the construction sector.

A significant growth factor propelling the Building Thermal Model Calibration Tools Market is the global push toward energy-efficient building operations. As energy costs continue to rise and governments introduce stricter energy codes, building owners and facility managers are increasingly turning to sophisticated calibration tools to optimize HVAC systems, reduce operational costs, and meet regulatory compliance. These tools enable precise alignment of simulation models with actual building performance data, ensuring accurate forecasting and effective energy management. The integration of Internet of Things (IoT) sensors and smart meters further enhances the capabilities of these calibration tools, allowing real-time data acquisition and dynamic model adjustments, which are critical for modern, high-performance buildings.

Another key driver is the rapid advancement in software technologies and automated calibration algorithms. Innovations in artificial intelligence (AI) and machine learning (ML) have made automated calibration more accessible and reliable, reducing the time and expertise required for manual model adjustments. This technological evolution is particularly beneficial for large-scale commercial and industrial facilities, where complex systems and diverse usage patterns necessitate continuous calibration. As a result, software vendors are increasingly focusing on user-friendly interfaces, interoperability with various building management systems, and cloud-based deployment models, all of which contribute to the accelerated adoption of calibration tools across different end-user segments.

The growing awareness of sustainability and green building certifications such as LEED and BREEAM is also fueling market expansion. Building owners and energy consultants are leveraging calibration tools not only to optimize energy consumption but also to validate and document building performance for certification purposes. This trend is especially pronounced in regions where governments and municipalities offer incentives for energy-efficient buildings. Additionally, the proliferation of smart cities and urban infrastructure projects in emerging economies is creating new opportunities for the deployment of advanced building thermal model calibration tools, further boosting market growth on a global scale.

From a regional perspective, North America currently leads the Building Thermal Model Calibration Tools Market, accounting for approximately 36% of the global revenue in 2024, followed by Europe and Asia Pacific. The United States, in particular, benefits from a mature construction sector, well-established energy efficiency regulations, and a high penetration of digital technologies. Meanwhile, Asia Pacific is expected to exhibit the fastest growth rate over the forecast period, driven by rapid urbanization, government initiatives for smart infrastructure, and increasing investments in commercial real estate. Europe remains a significant market due to its stringent building energy codes and widespread adoption of green building standards.

Component Analysis

The Building Thermal Model Calibration Tools Market is segmented by component into software and services. The software segment dominates the market, accounting for a substantial share of the total revenue in 2024. This dominance is attributed to the continuous evolution of calibration platforms, which now offer advanced analytics, seamless integration with building management systems, and enhanced user experience. Leading software solutions provide robust simulation environments, enabling users to calibrate models with high accuracy, automate repetitive tasks, and generate actionable insights for energy optimization. The increasing complexity of modern building systems and the need for precise control over thermal performance are driving demand for feature-rich, s

Retirement Notice: This item is in mature support as of April 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.Sea Surface Temperature is a key climate and weather measurement used for weather prediction, ocean forecasts, tropical cyclone forecasts, and in coastal applications such as fisheries, pollution monitoring and tourism. El Niño and La Niña are two examples of climate events which are forecast through the use of sea surface temperature maps. The Naval Oceanographic Office sea surface temperature dataset is calculated from satellite-based microwave and infrared imagery. These data are optimally interpolated to provide a daily, global map of the midday (12:00 pm) sea surface temperature. Learn more about the source data. Phenomenon Mapped: Sea Surface TemperatureUnits: Degrees CelsiusTime Interval: Daily Time Extent: 2008/04/01 12:00:00 UTC to presentCell Size: 11 kmSource Type: ContinuousPixel Type: Floating PointData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global OceansSource: Naval Oceanographic OfficeArcGIS Server URL: https://earthobs2.arcgis.com/arcgis Time: This is a time-enabled layer. It shows the average sea surface temperature during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the average of all days in the time extent. Minimum temporal resolution is one day; maximum is one month. What can you do with this layer? Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop. Analysis: This layer can be used as an input to geoprocessing tools and model builder. Units are in degrees Celsius, and there is a processing template to convert pixels to Fahrenheit. Do not use this layer for analysis while the Cartographic Renderer processing template is applied.

Model Card Generation Tools Market Outlook

According to our latest research, the global Model Card Generation Tools market size reached USD 498 million in 2024, with a robust compound annual growth rate (CAGR) of 24.6% projected through the forecast period. This exceptional growth is fueled by the increasing adoption of artificial intelligence (AI) and machine learning (ML) across diverse industries, as organizations prioritize transparency, compliance, and explainability in their AI models. By 2033, the market is expected to attain a value of USD 4.43 billion, reflecting the surging demand for model documentation and governance solutions that address regulatory and ethical requirements in AI-driven environments.

One of the primary growth factors propelling the Model Card Generation Tools market is the intensifying focus on responsible AI practices among enterprises. As AI and ML models are increasingly deployed in high-stakes applications such as finance, healthcare, and government, the need for transparency and explainability has become paramount. Regulatory bodies and industry standards now require organizations to provide detailed documentation of their models, including information about data provenance, performance metrics, and potential biases. Model Card Generation Tools facilitate this process by automating the creation of standardized, comprehensive model cards, enabling organizations to meet compliance requirements efficiently while fostering trust among stakeholders and end-users.

Another significant driver is the rising complexity of AI models and the growing diversity of use cases across industries. Modern machine learning models, particularly those based on deep learning, often operate as "black boxes," making it challenging for organizations to understand and communicate their decision-making processes. Model card generation solutions address this challenge by offering user-friendly interfaces and customizable templates that allow data scientists and ML engineers to document model assumptions, limitations, and intended use cases. This not only streamlines internal governance but also enhances collaboration between technical and non-technical teams, accelerating the adoption of AI technologies in regulated sectors such as banking, insurance, and healthcare.

The rapid expansion of cloud-based AI development platforms and the proliferation of open-source ML frameworks have also contributed to the growth of the Model Card Generation Tools market. Cloud-based deployment enables scalable, on-demand access to model documentation tools, allowing organizations of all sizes to integrate model card generation into their ML pipelines with minimal infrastructure investment. Additionally, the integration of model card tools with popular ML platforms and version control systems supports continuous model monitoring and lifecycle management, further strengthening the market's value proposition. This trend is particularly pronounced in industries with stringent data privacy and governance requirements, where automated documentation and auditability are essential for regulatory compliance.

Regionally, North America remains the largest market for Model Card Generation Tools, accounting for over 38% of global revenue in 2024. The region's dominance is attributed to the early adoption of AI technologies, a mature regulatory landscape, and the presence of leading technology vendors. Europe follows closely, driven by the implementation of the General Data Protection Regulation (GDPR) and other AI governance frameworks. Meanwhile, the Asia Pacific region is witnessing the fastest growth, with a projected CAGR of 28.2% through 2033, as enterprises across China, India, and Southeast Asia ramp up their investments in AI-driven digital transformation and compliance solutions.

Component Analysis

The Component segment of the Model Card Generation Tools market is primarily divided into Software and Services. Software solutions dominate this segment, accounting for approximately 69% of the market share in 2024. These software offerings encompass standalone model card generation applications, integrated plugins for popular ML platforms, and APIs that automate the creation of model cards directly from model metadata. Organizations are increasingly seeking robust, scalable, and user-friendly so

The global Dental Anatomy Models market is poised for robust expansion, estimated to reach approximately $150 million in 2025, with a projected Compound Annual Growth Rate (CAGR) of around 8% over the forecast period of 2025-2033. This significant growth is primarily fueled by the increasing demand for enhanced dental education and training across academic institutions and dental schools worldwide. The rising complexity of dental procedures necessitates advanced visualization tools, making dental anatomy models indispensable for both theoretical learning and practical skill development. Furthermore, the expanding healthcare infrastructure, particularly in emerging economies, and a growing awareness of oral health are contributing to the sustained demand. The market is also benefiting from technological advancements in model creation, leading to more realistic and detailed representations of oral structures, which further aids in better patient understanding and treatment planning. The market landscape for Dental Anatomy Models is characterized by a diverse range of applications, with Scientific Research and Hospitals emerging as key segments alongside Education. In scientific research, these models are crucial for understanding disease progression and testing new treatment methodologies. Hospitals utilize them for patient consultations, enabling clearer explanations of proposed dental interventions and improving patient compliance. While the market benefits from these drivers, certain restraints such as the high cost of sophisticated 3D-printed models and the limited availability of specialized training programs in some regions could pose challenges. However, the continuous innovation in materials and manufacturing techniques, along with the growing emphasis on standardized dental education globally, are expected to overcome these limitations, paving the way for sustained market dominance and value creation. The competitive landscape features prominent players like GPI Anatomicals and 3B Scientific, who are continually innovating to capture market share. This in-depth report delves into the global Dental Anatomy Models market, offering a meticulously detailed analysis of its current landscape and future trajectory. Spanning a Study Period of 2019-2033, with a Base Year of 2025 and an Estimated Year also set at 2025, this report provides a robust forecast for the Forecast Period: 2025-2033, building upon the Historical Period: 2019-2024. The market, valued in the millions, is poised for significant expansion, driven by technological advancements, increasing demand for advanced dental education, and the growing emphasis on patient understanding of oral health. This comprehensive report will serve as an indispensable resource for stakeholders seeking to understand market dynamics, identify growth opportunities, and navigate the competitive landscape.

Flow diagrams are a common tool used to help build and interpret models of dynamical systems, often in biological contexts such as consumer-resource models and similar compartmental models. Typically, their usage is intuitive and informal. Here, we present a formalized version of flow diagrams as a kind of weighted directed graph which follow a strict grammar, which translate into a system of ordinary differential equations (ODEs) by a single unambiguous rule, and which have an equivalent representation as a relational database. (We abbreviate this schema of “ODEs and formalized flow diagrams” as OFFL.) Drawing a diagram within this strict grammar encourages a mental discipline on the part of the modeler in which all dynamical processes of a system are thought of as interactions between dynamical species that draw parcels from one or more source species and deposit them into target species according to a set of transformation rules. From these rules, the net rate of change for each species can be derived. The modeling schema can therefore be understood as both an epistemic and practical heuristic for modeling, serving both as an organizational framework for the model building process and as a mechanism for deriving ODEs. All steps of the schema beyond the initial scientific (intuitive, creative) abstraction of natural observations into model variables are algorithmic and easily carried out by a computer, thus enabling the future development of a dedicated software implementation. Such tools would empower the modeler to consider significantly more complex models than practical limitations might have otherwise proscribed, since the modeling framework itself manages that complexity on the modeler’s behalf. In this report, we describe the chief motivations for OFFL, carefully outline its implementation, and utilize a range of classic examples from ecology and epidemiology to showcase its features.

Portions of the world's interior, such as central Asia are extremely secluded from the ocean and are more than 2,000 km from the nearest coast. Distance to coast can be used in asset management and modeling project costs. Phenomenon Mapped: Distance to coastUnits: KilometersCell Size: 655.9259912 metersSource Type: DiscretePixel Type: Signed integerSpatial Reference: World Equidistant CylindricalMosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: 2015ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Distance to Coast layer was calculated by Esri using the Euclidean Distance Tool in ArcMap and the Esri Country Boundaries layer.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – kilometers to miles, Unit Conversion - kilometers to nautical miles, Cartographic Renderer, and Classified Renderer.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

Software reverse engineering and reengineering are becoming common in the field of games and website development. Simulation and modeling play an important role in understanding the flow of the overall system. Business process modeling notation (BPMN) is used to show the overall architecture of the business process. Simulated business process re-engineering is essential for implementing change or creating new processes. The simulation model explains whether a change will be successful or not prior to adopting any new business processes or other changes. Some available tools help convert the BPMN to a simulating BPMN model but converting the discrete event simulation model build in commercial off the shelf simulation packages like Simul8 to the BPMN to help generate business process simulation to BPMN is also a key challenge. This framework is introduced to convert the simulation model to BPMN using the reverse engineering concept to understand how the converting tools convert the BPMN model to the simulation model. After understanding this process, the concept of reengineering will be used to build a BPMN from the simulation model. The framework is divided into three main parts model translation, model mapping, and model formation. For model building, two simulation tools Simul8 and BPSimulator are used. It is then tested on two case studies bank and product manufacturing. The output shows the BPMN model is generated from the simulation model within less time on a single click saving time and resources for developing BPMN model first and then making simulation model for testing purpose.