In this course, you will learn about some common types of data used for GIS mapping and analysis, and practice adding data to a file geodatabase to support a planned project.Goals Create a file geodatabase. Add data to a file geodatabase. Create an empty geodatabase feature class.

You have been assigned a new project, which you have researched, and you have identified the data that you need.The next step is to gather, organize, and potentially create the data that you need for your project analysis.In this course, you will learn how to gather and organize data using ArcGIS Pro. You will also create a file geodatabase where you will store the data that you import and create.After completing this course, you will be able to perform the following tasks:Create a geodatabase in ArcGIS Pro.Create feature classes in ArcGIS Pro by exporting and importing data.Create a new, empty feature class in ArcGIS Pro.

Water Meter points within Fuquay-Varina. Most meter devices are owned and maintained by the Town, which provides water utility services. However, on some commercial sites, for example, the meter box and meter yoke are actually privately owned and maintained while the meter device is owned and maintained by the Town. This water meter dataset is constantly under development and improvement as there is increasing demand to integrate GIS meter information with other solutions. Please note that some meter points are not field-validated and some are not associated with a valid METERID for water service, and may essentially be duplicated legacy locations from old GIS data. Please note that ALL public utility data layers can be downloaded in a single .mpkx (ArcGIS Pro map package file), updated every Friday evening. This .mpkx file can be opened directly with ArcGIS Pro version 3+. Alternatively, you can extract the file geodatabase within it by renaming the file ending .mpkx to .zip and treating it like a zip archive file, for use in any version of ArcGIS Pro or ArcMap software. You can also use QGIS, a powerful, free, and open-source GIS software.The Town of Fuquay-Varina creates, maintains, and serves out a variety of utility information to the public, including its Potable Water System, Sanitary Sewer System, and Stormwater Collection System features. This is the same utility data displayed in our public web map. This utility data includes some features designated as 'private' that are not owned or maintained by the Town, but may be helpful for modeling and other informational purposes. Please pay particular attention to the terms of use and disclaimer associated with these data. Some data includes the use of Subtypes and Domains that may not translate well to Shapefile or GeoJSON downloads available through our Open Data site. Please beware the dangers of cartographic misrepresentation if you are unfamiliar with filtering and symbolizing data based on attributes. Water System Layers:Water LinesWater ValvesWater ManholesFire HydrantsFire Department ConnectionsWater MetersRPZ (Backflow Preventers)Water TankWater Booster StationsHarnett County Water District AreaSewer System Layers:Gravity Sewer LinesForced Sewer LinesSewer ManholesSewer ValvesSewer CleanoutsSewer Pump StationsWastewater Treatment PlantsStormwater System Layers:Stormwater Lines (Pipes)Stormwater Points (Inlets/Outlets/Manholes)Stormwater Control Measure Points (SCM's, such as Wet Ponds / Retention Basins)

The hypsometric integral (HI) is one of the most commonly used measures that geomorphologists use to describe the shape of the Earth’s surface. A hypsometric integral is usually calculated by plotting the cumulative height and the cumulative area under that height for individual watersheds and then taking the area under that curve to get the hypsometric integral. In a GIS hypsometric integral is calculated by slicing watersheds into elevation bands and plotting the cumulative area for each band. Due to the iterative nature that is required for calculating hypsometric integral it tends to be one of the harder to calculate watershed variables, and thus the need for an automated tool. Although there are instructions online for how to calculate HI in ArcGIS this tool automates the processes and doesn’t require users to do their own plotting or export results to spreadsheets.

This toolbox contains two models. Hypsometric Integral (for shapefiles only) is the main model that most users will want to run. Hypsometric Integral (submodel) is a model that is nested within the Hypsometric Integral (for shapefiles only) model and doesn’t need to be run by itself. The tool computes the hypsometric integral for a given watershed. A new shapefile will be created representing the same watershed the user inputs, but includes a new field, "HI," representing hypsometric integral percentages.

In some instances the Hypsometric Integral (for shapefiles) will show up with a red X and won’t be useable. The workaround for this is to open the Hypsometric Integral (for shapefiles) tool in edit mode (ModelBuilder) delete the Hypsometric Integral (submodel) and drag in your version of the Hypsometric Integral (submodel). Re-connect the following parameters: input DEM, Input Watershed, TempWorkspace, and then connect the output (HI Values for all Watersheds) to the Append tool. Click save.

ArcGIS Pro provides the tools to integrate, visualize, analyze, and share your data. This course introduces you to the powerful capabilities of ArcGIS Pro and how it can be used in your work.Kontakt os for adgang

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu.We captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024.Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each area.The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857.For using these data:- The Adobe Suite gives you great software to open .Tif files.- You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.- Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk.- You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files.- The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file.This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

Imagery as a subscription service, provided by Nearmap USProactive Survey Capture Program. Nearmap is continuously capturing aerial imagery throughout the United States, 365 days a year. All imagery is stored on the cloud and ready to stream within days of capture. Current and Historical Imagery. We fly large urban areas in the US up to 3 times per year, frequently capturing seasonal changes for leaf-on/ leaf-off information. In addition to our newest survey captures, you'll also have instant access to our complete catalog of historical imagery dating back to 2014.Wide-Scale Coverage. We cover 71% of the US population, including the top 430 urban areas with over 330,000 square miles captured annually. Consistently High-Resolution. Our imagery is always 2.2-3" GSD or better, meaning you can see clearly and plan accurately, before even setting foot on site. Integration with ArcGIS. Integrate Nearmap imagery into the suite of ArcGIS platforms: ArcGIS Online, ArcGIS Enterprise, ArcGIS Pro, ArcGIS Collector, ArcGIS Survey 123, etc. Available through ArcGIS Marketplace as well as our API offerings.Integration with Autodesk. Integration with ArcMap.Make sure to install Nearmap’s free add-in for ArcMap to get instant integrated access to current and historical high-res imagery from Nearmap.

The Topography Toolbox has been updated and expanded for ArcGIS Pro. Tools calculate:McCune and Keon (2002) Heat Load IndexSlope Position ClassificationTopographic Convergence/Wetness IndexTopographic Position IndexMultiscale Topographic Position IndexHeight Above Nearest DrainageHeight Above RiverVector Ruggedness MeasureLocalized Vector Ruggedness MeasureWind Exposure/Shelter IndexHypsometric Integral

The dataset has combined the Parcels and Computer-Assisted Mass Appraisal (CAMA) data for 2023 into a single dataset. This dataset is designed to make it easier for stakeholders and the GIS community to use and access the information as a geospatial dataset. Included in this dataset are geometries for all 169 municipalities and attribution from the CAMA data for all but one municipality. Pursuant to Section 7-100l of the Connecticut General Statutes, each municipality is required to transmit a digital parcel file and an accompanying assessor’s database file (known as a CAMA report), to its respective regional council of governments (COG) by May 1 annually. These data were gathered from the CT municipalities by the COGs and then submitted to CT OPM. This dataset was created on 12/08/2023 from data collected in 2022-2023. Data was processed using Python scripts and ArcGIS Pro, ensuring standardization and integration of the data.CAMA Notes:The CAMA underwent several steps to standardize and consolidate the information. Python scripts were used to concatenate fields and create a unique identifier for each entry. The resulting dataset contains 1,353,595 entries and information on property assessments and other relevant attributes.CAMA was provided by the towns.Canaan parcels are viewable, but no additional information is available since no CAMA data was submitted.Spatial Data Notes:Data processing involved merging the parcels from different municipalities using ArcGIS Pro and Python. The resulting dataset contains 1,247,506 parcels.No alteration has been made to the spatial geometry of the data.Fields that are associated with CAMA data were provided by towns.The data fields that have information from the CAMA were sourced from the towns’ CAMA data.If no field for the parcels was provided for linking back to the CAMA by the town a new field within the original data was selected if it had a match rate above 50%, that joined back to the CAMA.Linking fields were renamed to "Link".All linking fields had a census town code added to the beginning of the value to create a unique identifier per town.Any field that was not town name, Location, Editor, Edit Date, or a field associated back to the CAMA, was not used in the creation of this Dataset.Only the fields related to town name, location, editor, edit date, and link fields associated with the towns’ CAMA were included in the creation of this dataset. Any other field provided in the original data was deleted or not used.Field names for town (Muni, Municipality) were renamed to "Town Name".

Coordinate system Update:Notably, this dataset will be provided in NAD 83 Connecticut State Plane (2011) (EPSG 2234) projection, instead of WGS 1984 Web Mercator Auxiliary Sphere (EPSG 3857) which is the coordinate system of the 2023 dataset and will remain in Connecticut State Plane moving forward.Ownership Suppression and Data Access:The updated dataset now includes parcel data for all towns across the state, with some towns featuring fully suppressed ownership information. In these instances, the owner’s name will be replaced with the label "Current Owner," the co-owner’s name will be listed as "Current Co-Owner," and the mailing address will appear as the property address itself. For towns with suppressed ownership data, users should be aware that there was no "Suppression" field in the submission to verify specific details. This measure was implemented this year to help verify compliance with Suppression.New Data Fields:The new dataset introduces the "Land Acres" field, which will display the total acreage for each parcel. This additional field allows for more detailed analysis and better supports planning, zoning, and property valuation tasks. An important new addition is the FIPS code field, which provides the Federal Information Processing Standards (FIPS) code for each parcel’s corresponding block. This allows users to easily identify which block the parcel is in.Updated Service URL:The new parcel service URL includes all the updates mentioned above, such as the improved coordinate system, new data fields, and additional geospatial information. Users are strongly encouraged to transition to the new service as soon as possible to ensure that their workflows remain uninterrupted. The URL for this service will remain persistent moving forward. Once you have transitioned to the new service, the URL will remain constant, ensuring long term stability.For a limited time, the old service will continue to be available, but it will eventually be retired. Users should plan to switch to the new service well before this cutoff to avoid any disruptions in data access.The dataset has combined the Parcels and Computer-Assisted Mass Appraisal (CAMA) data for 2024 into a single dataset. This dataset is designed to make it easier for stakeholders and the GIS community to use and access the information as a geospatial dataset. Included in this dataset are geometries for all 169 municipalities and attribution from the CAMA data for all but one municipality. Pursuant to Section 7-100l of the Connecticut General Statutes, each municipality is required to transmit a digital parcel file and an accompanying assessor’s database file (known as a CAMA report), to its respective regional council of governments (COG) by May 1 annually. These data were gathered from the CT municipalities by the COGs and then submitted to CT OPM. This dataset was created on 10/31/2024 from data collected in 2023-2024. Data was processed using Python scripts and ArcGIS Pro, ensuring standardization and integration of the data.<p style='margin-top:0px; margin-bottom:1.5rem; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helv

The Israel geospatial analytics market is projected to grow from USD 1.69 million in 2025 to USD 2.69 million by 2033, at a CAGR of 5.93% during the forecast period. The growth of this market is attributed to increasing adoption of geospatial analytics in various end-user verticals, such as agriculture, utility and communication, defense and intelligence, government, mining and natural resources, automotive and transportation, healthcare, real estate and construction, and other end-user verticals. Geospatial analytics helps in better decision-making, improves operational efficiency, and enhances customer engagement. Key drivers of the Israel geospatial analytics market include increasing adoption of cloud-based geospatial platforms, rising demand for real-time insights, and growing investments in smart city development. However, factors such as high cost of implementation and skilled labor shortage may hinder the market growth. Major companies operating in the Israel geospatial analytics market include SAS Institute Inc., General Electrical Company, Esri Inc. (Environmental Systems Research Institute), Harris Corporation, Microsoft Corporation, Autodesk Inc., Oracle Corporation, Trimble Inc., Bentley Systems Inc., and Google Inc. The Israel geospatial analytics market is estimated to grow from $170 million in 2023 to $320 million by 2029, at a CAGR of 9.5%. The market growth is majorly driven by the increasing adoption of geospatial technologies in various end-user verticals, such as agriculture, utility and communication, defense and intelligence, government, mining and natural resources, automotive and transportation, healthcare, real estate and construction. Recent developments include: June 2023: Autodesk and Esri's partnership accelerates innovations in AEC. Autodesk's InfoWater Pro and Esri's ArcGIS Pro were integrated to make this possible, and there are many more examples of how their partnership with Esri enables BIM and GIS data to flow between respective solutions seamlessly. The result is that project stakeholders can now visualize, understand, and analyze infrastructure within its real-world context., February 2023: Mercedes-Benz and Google announced a long-term strategic partnership to accelerate auto innovation and create the industry's next-generation digital luxury car experience. With this partnership, Mercedes-Benz will be the first automaker to build its branded navigation experience based on new in-car data and navigation capabilities from the Google Maps Platform. This will give the luxury automaker access to Google's leading geospatial offering, including detailed information about places, real-time and predictive traffic information, automatic rerouting, and more.. Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: High Costs and Operational Concerns, Concerns related to Geoprivacy and Confidential Data. Notable trends are: Surface Analysis is Expected to Hold Significant Share of the Market.

The Israeli geospatial analytics market, valued at $1.69 billion in 2025, is projected to experience robust growth, driven by increasing government investments in infrastructure development, the burgeoning adoption of precision agriculture techniques, and a rising demand for advanced location-based services across various sectors. The market's Compound Annual Growth Rate (CAGR) of 5.93% from 2025 to 2033 signifies a consistent expansion. Key growth drivers include the increasing availability of high-resolution satellite imagery, advancements in data analytics technologies, and the growing need for efficient resource management. The market is segmented by type (surface analysis, network analysis, geovisualization) and end-user vertical (agriculture, utilities & communication, defense & intelligence, government, mining & natural resources, automotive & transportation, healthcare, real estate & construction). The strong presence of established players like SAS Institute, Esri, and Google, alongside innovative startups, fosters competition and innovation within the Israeli market. Government initiatives promoting technological advancement further contribute to the market's expansion. While data limitations currently restrict precise market segmentation analysis for Israel, the overall growth trajectory reflects a significant investment opportunity within this specialized tech sector. The robust demand for geospatial data analysis in sectors like precision agriculture, infrastructure planning, and national security underscores the market's long-term potential. The forecast period of 2025-2033 anticipates a steady rise in market value, largely fueled by the continued integration of geospatial analytics into diverse business strategies and governmental policy. The adoption of cloud-based solutions and advancements in artificial intelligence (AI) and machine learning (ML) are likely to significantly impact the market's future growth. The competitive landscape is expected to become more dynamic with mergers, acquisitions, and strategic partnerships potentially reshaping the market structure. While challenges such as data security concerns and the need for skilled professionals may exist, the overall market outlook remains positive, driven by strong technological advancements and rising demand across diverse sectors in Israel. Recent developments include: June 2023: Autodesk and Esri's partnership accelerates innovations in AEC. Autodesk's InfoWater Pro and Esri's ArcGIS Pro were integrated to make this possible, and there are many more examples of how their partnership with Esri enables BIM and GIS data to flow between respective solutions seamlessly. The result is that project stakeholders can now visualize, understand, and analyze infrastructure within its real-world context., February 2023: Mercedes-Benz and Google announced a long-term strategic partnership to accelerate auto innovation and create the industry's next-generation digital luxury car experience. With this partnership, Mercedes-Benz will be the first automaker to build its branded navigation experience based on new in-car data and navigation capabilities from the Google Maps Platform. This will give the luxury automaker access to Google's leading geospatial offering, including detailed information about places, real-time and predictive traffic information, automatic rerouting, and more.. Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: High Costs and Operational Concerns, Concerns related to Geoprivacy and Confidential Data. Notable trends are: Surface Analysis is Expected to Hold Significant Share of the Market.

The distribution map of Festuca dolichophylla relies on diverse data sources. Geographical coordinates (latitude and longitude) and country initials (countryCode) were extracted from Tropicos, the Gbif repository (up to May 2019), and the iDigBio database (up to July 2021). Additionally, data from other sources, including BMAP Peru (2023), Eduardo-Palomino (2022), Ccora et al. (2019), Arana et al. (2013), Castro (2019), Flores (2017), Gonzales (2017), and Martínez y Pérez (1999), were integrated. The Gbif data points are associated with gbifID numbers for reference. Please note that this compilation provides essential information for understanding the distribution of F. dolichophylla across various regions.

Software

Organized data by geographic coordinates was uploaded to ArcGIS Pro v. 3.2.0 for map production. Geospatial visualization and mapping were carried out using ArcGIS Pro, allowing us to create the distribution map of F. dolichophylla.

Methods

Funding

The Middle East Geospatial Analytics market, valued at $1.16 billion in 2025, is projected to experience robust growth, driven by significant investments in infrastructure development, smart city initiatives, and the burgeoning need for precise location intelligence across various sectors. A Compound Annual Growth Rate (CAGR) of 8.15% from 2025 to 2033 indicates a substantial expansion, with the market expected to surpass $2 billion by 2033. Key drivers include increasing adoption of advanced technologies like AI and machine learning within geospatial analytics, coupled with growing government initiatives promoting digital transformation and data-driven decision-making. The demand for accurate land management, resource optimization, and efficient urban planning is further fueling market expansion. Segmentation reveals strong growth in surface analysis and network analysis within the ‘By Type’ category, while the ‘By End-user Vertical’ segment is witnessing significant contributions from the Agriculture, Utility & Communication, and Defense & Intelligence sectors. The presence of established players like Esri, Bentley Systems, and Autodesk, alongside emerging specialized firms, ensures a competitive and dynamic market landscape. However, challenges like data security concerns, high implementation costs, and the need for skilled professionals could potentially restrain market growth. The Middle East's unique geopolitical landscape and rapid urbanization present both opportunities and challenges. Government initiatives focused on national infrastructure projects and sustainable development are creating substantial demand for geospatial analytics solutions. The region's focus on diversification beyond oil and gas is further stimulating adoption across sectors like agriculture, tourism, and transportation. However, regulatory hurdles and data privacy concerns, especially within the defense and intelligence sectors, need careful consideration. The high cost of sophisticated geospatial analytics technology and the need for specialized expertise might limit penetration in certain segments. Nevertheless, the long-term outlook remains optimistic, driven by the region's commitment to technological advancement and the increasing recognition of the value of data-driven insights for improved decision-making. Recent developments include: June 2023: Autodesk and Esri's partnership accelerated innovations in AEC. Autodesk's InfoWater Pro and Esri's ArcGIS Pro were integrated to make this possible, and there are many more examples of how their partnership with Esri enables BIM and GIS data to flow between respective solutions seamlessly. The result is that project stakeholders can now visualize, understand, and analyze infrastructure within its real-world context., February 2023: Mercedes-Benz and Google announced a long-term strategic partnership to accelerate auto innovation and create the industry's next-generation digital luxury car experience. With this partnership, Mercedes-Benz will be the first automaker to build its branded navigation experience based on new in-car data and navigation capabilities from the Google Maps Platform. This will give the luxury automaker access to Google's leading geospatial offering, including detailed information about places, real-time and predictive traffic information, automatic rerouting, and more.. Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Notable trends are: Surface Analysis is Expected to Hold Significant Share of the Market.

This deep learning model is used for extracting windows and doors in textured building data displayed in 3D views. Manually digitizing windows/doors from 3D building data can be a slow process. This model automates the extraction of these objects from a 3D view and can help in speeding up 3D editing and analysis workflows. Using this model, existing building data can be enhanced with additional information on location, size and orientation of windows and doors. The extracted windows and doors can be further used to perform 3D visibility analysis using existing 3D geoprocessing tools in ArcGIS.This model can be useful in many industries and workflows. National Government and state-level law enforcement could use this model in security analysis scenarios. Local governments could use windows and door locations to help with tax assessments with CAMA (computer aided mass appraisal) plus impact-studies for urban planning. Public safety users might be interested in regards to physical or visual access to restricted areas, or the ability to build evacuation plans. The commercial sector, with everyone from real-estate agents to advertisers to office/interior designers, would be able to benefit from knowing where windows and doors are located. Even utilities, especially mobile phone providers, could take advantage of knowing window sizes and positions. To be clear, this model doesn't solve these problems, but it does allow users to extract and collate some of the data they will need to do it.Using the modelThis model is generic and is expected to work well with a variety of building styles and shapes. To use this model, you need to install supported deep learning frameworks packages first. See Install deep learning frameworks for ArcGIS for more information. The model can be used with the Interactive Object Detection tool.A blog on the ArcGIS Pro tool that leverages this model is published on Esri Blogs. We've also published steps on how to retrain this model further using your data.InputThe model is expected to work with any textured building data displayed in 3D views. Example data sources include textured multipatches, 3D object scene layers, and integrated mesh layers. OutputFeature class with polygons representing the detected windows and doors in the input imagery. Model architectureThe model uses the FasterRCNN model architecture implemented using ArcGIS API for Python.Training dataThis model was trained using images from the Open Images Dataset.Sample resultsBelow, are sample results of the windows detected with this model in ArcGIS Pro using the Interactive Object Detection tool, which outputs the detected objects as a symbolized point feature class with size and orientation attributes.

The City maintains a digital inventory of the stormwater sewer system drainage area (MS4 area) to aid in implementation of the Integrated Stormwater Management Program control measures. In 2023, the storm sewer drainage basin area was updated and included in the 2023 Integrated Stormwater Management Program Document. The storm sewer drainage area was updated using ArcGIS Pro to model drainage based on the City’s stormwater infrastructure GIS data and a Digital Elevation Model (DEM) derived from Light Detection and Ranging (LiDAR) captured in March 2022. City outfalls that discharge stormwater to surface waterbodies either directly or indirectly were identified as points for analysis. Stormwater Underground Injection Control infrastructure was also analyzed to distinguish between infiltration-only basins and basins draining to the storm sewer system. For more information, please see the 2023 Integrated Stormwater Management Program Document https://www.bendoregon.gov/government/departments/utilities/stormwater

The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 10% from 2025 to 2033, reaching approximately $39 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of cloud-based GIS solutions offers enhanced accessibility, scalability, and cost-effectiveness, particularly appealing to smaller organizations. Secondly, the burgeoning need for precise spatial data analysis in various applications, including urban planning, geological exploration, and water resource management, significantly contributes to market growth. Thirdly, advancements in technologies such as AI and machine learning are integrating into GIS tools, leading to more sophisticated analytical capabilities and improved decision-making. Finally, the increasing availability of high-resolution satellite imagery and other geospatial data further fuels market expansion. However, market growth is not without challenges. High initial investment costs associated with implementing and maintaining sophisticated GIS systems can pose a barrier to entry for smaller businesses. Furthermore, the complexity of GIS software and the need for specialized skills to operate and interpret data effectively can limit widespread adoption. Despite these restraints, the market’s overall trajectory remains positive, with the cloud-based segment projected to maintain a dominant market share due to its inherent advantages. Growth will be geographically diverse, with North America and Europe continuing to be significant markets, while Asia-Pacific is expected to experience the fastest growth due to rapid urbanization and infrastructure development. The continued development of user-friendly interfaces and increased integration with other business intelligence tools will further accelerate market expansion in the coming years.

The global satellite remote sensing software market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise figures for market size and CAGR aren't provided, considering the technological advancements and applications in agriculture (precision farming, crop monitoring), water conservancy (flood management, irrigation optimization), forest management (deforestation monitoring, resource assessment), and the public sector (urban planning, disaster response), a conservative estimate places the 2025 market size at approximately $2 billion. This figure reflects the substantial investments in satellite imagery acquisition and analysis capabilities worldwide. The market is further fueled by the rising adoption of cloud-based solutions, enhancing accessibility and scalability of software platforms. Trends such as the integration of AI and machine learning for automated image processing, the proliferation of high-resolution satellite imagery, and the increasing availability of open-source software are accelerating market expansion. However, factors such as the high cost of specialized software licenses and the need for skilled professionals to operate the sophisticated systems act as restraints. The market is segmented by application (agriculture, water conservancy, forest management, public sector, others) and software type (open-source, non-open-source). The North American and European markets currently hold significant shares, but the Asia-Pacific region is witnessing rapid growth due to increasing infrastructure development and government initiatives promoting geospatial technologies. This dynamic market landscape presents lucrative opportunities for both established players and emerging companies in the years to come. The forecast period (2025-2033) anticipates continued growth, with a projected CAGR of approximately 12%, driven by the aforementioned technological advancements and broadening applications across various industry verticals. The competitive landscape is comprised of both major players like ESRI, Trimble, and PCI Geomatica, offering comprehensive suites of software, and smaller, specialized companies focusing on niche applications or open-source solutions. The market is characterized by both proprietary and open-source software options. Open-source solutions like QGIS and GRASS GIS offer cost-effective alternatives, particularly for research and smaller organizations, while commercial solutions provide advanced functionalities and support. The increasing availability of cloud-based solutions is blurring the lines between these segments, with hybrid models emerging that combine the benefits of both. Future growth will be significantly influenced by collaborations between software providers and satellite imagery providers, fostering a more integrated ecosystem and streamlining the data acquisition and processing workflow. The market will continue to benefit from advancements in satellite technology, producing higher-resolution, more frequent, and more affordable imagery.

VT E911 Composite geocoder - uses ESITE, RDSNAME, and RDSRANGE. REFRESHED WEEKLY. VCGI, in collaboration with the VT E911 Board, has created a suite of geocoding services that can be used to batch geocode addresses using ArcGIS Desktop 10.x. This service can also be integrated into ESRI ArcGIS web-based mapping applications.Input Address Requirements Must use valid E911 addresses (street style addressing...no P.O. box addresses!) and E911 town names. Limitations Don't attempt to geocode more than 50000 records or so. You must have an Internet connection to use the services. A DSL, cable, or other high bandwidth connection is the best option. Addresses other than E911 addresses are not supported. ArcGIS Pro - How To:Startup ArcGIS ProUnder the "Insert" ribbon select Connections --> New ArcGIS Server. Server URL = https://maps.vcgi.vermont.gov/arcgis/servicesBrowse to the ./EGC_services folder and select GEOCODE_COMPOSITE (or GEOCODE_ESITE).Add the table you want to geocode to project, then right-click and select "Geocode Table". Choose the “Go to Tool” option at the bottom of the dialogue box.Make selections and run geocoder.ArcGIS Desktop (ArcMap) - How To: Startup ArcMap 10+ Add a table containing VT addresses to geocode. ?Click the "Add Data" button.Navigate to your table, choose to add your tableRight-click on the table in the table of contentsSelect "Geocode Addresses...".Select "Add" in the dialog box.Browse to the "GIS Servers" icon in your catalog, then double click "Add ArcGIS Server".Select "Use GIS Services", then Next.ServerURL = https://maps.vcgi.vermont.gov/arcgis/services then click finish.Browse to "arcgis on maps.vcgi.org (user)". Browse to .\EGC_services folder.Select GECODE_ESITE (or GEOCODE_COMPOSITE). Click OK.Select whatever options you want in the geocode dialog box, including output, then click ok.The output will be automatically added to your ArcMap session.

1. Abstract Landslides are damaging and deadly, and they occur in every U.S. state. However, our current ability to understand landslide hazards at the national scale is limited, in part because spatial data on landslide occurrence across the U.S. varies greatly in quality, accessibility, and extent. Landslide inventories are typically collected and maintained by different agencies and institutions, usually within specific jurisdictional boundaries, and often with varied objectives and information attributes or even in disparate formats. The purpose of this data release is to provide an openly accessible, centralized map of existing information about landslide occurrence across the entire U.S. This data release is an update of previous versions 1 (Jones and others, 2019) and 2 (Belair and others, 2022). Changes relative to version 2 are summarized in us_ls_v3_changes.txt. It provides an integrated database of the landslides from these inventories (refer to US_Landslide_v3_gpkg) with a selection of uniform attributes, including links to the original digital inventory files (whenever available) (“Inv_URL”). The data release includes digital inventories created by both USGS and non-USGS authors. The original inventory is denoted by an abbreviation in the “Inventory” attribute. The full citation for each abbreviation can be found in us_ls_v3_references.csv. The date of the landslide event is included as a minimum and maximum (“Date_Min” and “Date_Max”) to accommodate events that happen within a range of dates. The date value is inherently difficult to interpret or discern due to the nature of landsliding, where some landslides move for long periods of time or move intermittently, and some areas can exhibit multiple landslide events. To preserve the constituent inventories as much as possible, we include all entries even if they are not considered landslides, such as “gullies” or “avalanche chutes.” We include a landslide type attribute when that information is available (“LS_Type”). The landslide classification system used in the original inventories is not always known or stated in the metadata, but many mapping entities use the schema from Cruden and Varnes (1996) or the updated schema from Hungr and others (2014). Given the wide range of landslide information sources in this data compilation, we provide an attribute to assess the relative confidence in the characterization of the location and extent of each landslide (entry) (“Confidence”). The confidence level reflects the resolution and quality of input data, as well as the method used for identification and mapping. This confidence does not reflect a formal accuracy assessment of field attributes. Relative to the previous data releases (version 1 and 2), this update (v3) includes more inventories, updated confidence rules, a new landslide type attribute, a new unique identifier (“USGS_ID”), new machine-readable date fields, and an ancillary database containing all fields from the original inventories (refer to US_Landslide_v3_ancillary). Please contact gs-haz_landslides_inventory@usgs.gov for more information on how to contribute additional inventories to this community effort. When possible, please cite the constituent inventories as well as this data release. This data release includes: (1) a landslide point file and polygon file in multiple forms (US_Landslide_v3_gpkg, US_Landslide_v3_shp, US_Landslide_v3_csv), (2) an ancillary database with original fields (US_Landslide_v3_ancillary), (3) a spreadsheet that summarizes the confidence rules, their justification, and any extra analyses (us_ls_v3_analyses.csv), (4) a summary file of the changes made between version 2 and version 3 (us_ls_v3_changes.txt), (5) a file containing the references of the constituent inventories (us_ls_v3_references.csv), (6) and a readme (README.txt). Disclaimer: Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. 2. Data fields Field Names Definitions USGS_ID Unique USGS identifier for each landslide entry. Date_Min Minimum possible date of landslide occurrence. If date is known to the day, Date_Min will have a value while Date_Max is empty. Time zone is assumed to be local, except for Inventories ‘USGS Earthquake-Triggered Ground Failure’ and ‘USGS Seismogenic Mass Movements’ which are in UTC. Date_Max Maximum possible date of landslide occurrence. If date is known to the day, Date_Max will be empty while Date_Min has a value. Time zone is assumed to be local, except for Inventories ‘USGS Earthquake-Triggered Ground Failure’ and ‘USGS Seismogenic Mass Movements’ which are in UTC. Fatalities Number of fatalities caused by landslide event. Confidence Confidence in landslide (entry) extent, nature, and location. LS_Type Landslide (entry) type. Classification schema of original inventories is often not specified. Inventory Name of original source inventory. Inv_URL URL or link to original source inventory. Info_Source Information source or sub-layer from original source inventory. Notes Unformatted notes field, includes additional information. Lat_N Latitude of point or polygon centroid in WGS 1984 Lon_W Longitude of point or polygon centroid in WGS 1984 3. Confidence attributes Confidence Definitions 1 Possible landslide (feature) in the area 2 Probable landslide (feature) in the area 3 Likely landslide (feature) at or near this location 5 Moderate confidence in extent or nature of landslide (feature) at this location 8 High confidence in extent or nature of landslide (feature) 4. References Belair, G.M., Jones, E.S., Slaughter, S.L., and Mirus, B.B., 2022, Landslide Inventories across the United States version 2: U.S. Geological Survey data release, https://doi.org/10.5066/P9FZUX6N. Cruden, D.M. and Varnes, D.J., 1996, Landslide Types and Processes, in Turner, K.A. and Schuster R. L., eds., Landslides Investigation and Mitigation: Transportation Research Board, U.S. National Research Council Special Report 247, U.S. National Academy of Sciences, Chapter 3, p. 36-75. ESRI, 2023, ArcGIS Pro (Version 3.1.3), Redlands, CA: Environmental Systems Research Institute, Retrieved from https://www.esri.com/en-us/arcgis/products/arcgis-pro/resources. Hungr, O., Leroueil, S., and Picarelli, L., 2014, The Varnes classification of landslide types, an update, Landslides, 11(2), p. 167-194, https://doi.org/10.1007/s10346-013-0436-y. Jones, E.S., Mirus, B.B, Schmitt, R.G., Baum, R.L., Burns, W.J., Crawford, M., Godt, J.W., Kirschbaum, D.B., Lancaster, J.T., Lindsey, K.O., McCoy, K.E., Slaughter, S., and Stanley, T.A., 2019, Landslide Inventories across the United States: U.S. Geological Survey data release, https://doi.org/10.5066/P9E2A37P. Python Software Foundation, 2023, Python Language Reference, version 3.9, Retrieved from http://www.python.org. QGIS.org, 2022, QGIS Geographic Information System (Version 3.28.4-Firenze), QGIS Association, Retrieved from http://www.qgis.org.