The location of proposed water lines in public water systems in the Commonwealth of Kentucky. A proposed water line depicts a linear feature for which a water project is proposed. Proposed water lines are mapped for water line extension, upgrade, and rehab/replacement projects.

Data set is a delination of groundwater basins in NM. Updated September 2005 with the declaration of all the existing declared basins by extension.

The location of proposed wastewater lines in public wastewater systems in the Commonwealth of Kentucky. A proposed wastewater line depicts a linear feature for which a wastewater project is proposed. Proposed wastewater lines are mapped for wastewater line extension, upgrade, and rehab/replacement projects.

A catalogue service that conforms to the HTTP protocol binding of the OpenGIS Catalogue Service ISO Metadata Application Profile specification (version 2.0.2)

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.

GIS Bay Extension Package Market Outlook

According to our latest research, the Global GIS Bay Extension Package market size was valued at $1.25 billion in 2024 and is projected to reach $3.78 billion by 2033, expanding at a CAGR of 13.2% during 2024–2033. The primary growth driver for this market is the increasing integration of advanced geospatial analytics and real-time data processing capabilities within urban planning, utilities management, and environmental monitoring applications. As cities and enterprises worldwide continue to embrace digital transformation, the demand for sophisticated GIS solutions—particularly those that can seamlessly extend the functionality of existing GIS bays—is surging. These packages enable organizations to optimize spatial data management, enhance decision-making processes, and ensure operational efficiency across diverse sectors.

Regional Outlook

North America currently holds the largest share of the GIS Bay Extension Package market, accounting for approximately 38% of global revenue in 2024. This dominance is attributed to the region’s mature technological infrastructure, widespread adoption of GIS solutions across government and private sectors, and robust investments in smart city initiatives. The United States, in particular, is a frontrunner due to its proactive regulatory frameworks, strong focus on infrastructure modernization, and substantial funding for urban planning and disaster management projects. The presence of leading GIS technology providers and an ecosystem conducive to innovation further solidifies North America’s leadership in the market. Additionally, partnerships between public agencies and private enterprises are accelerating the deployment of advanced GIS bay extension packages, enabling more efficient data integration and spatial analysis.

In contrast, the Asia Pacific region is witnessing the fastest growth, with a projected CAGR of 16.4% from 2024 to 2033. This rapid expansion is fueled by significant investments in digital infrastructure, urbanization, and government-led smart city programs across countries such as China, India, Japan, and South Korea. The increasing need for effective urban planning, environmental monitoring, and transportation management solutions is driving the adoption of GIS bay extension packages. Furthermore, the region’s burgeoning population and the consequent demand for efficient utilities and resource management are compelling public and private sector organizations to invest in scalable and interoperable GIS solutions. Strategic collaborations between local governments and global technology vendors are also fostering innovation and accelerating market penetration in Asia Pacific.

Emerging economies in Latin America and the Middle East & Africa are gradually increasing their adoption of GIS bay extension packages, albeit at a slower pace due to budget constraints, limited technical expertise, and infrastructural challenges. However, localized demand for disaster management, agriculture optimization, and utilities management is generating new growth opportunities. Policy reforms aimed at digital transformation and the deployment of smart infrastructure are beginning to take shape, particularly in Brazil, the UAE, and South Africa. Nonetheless, these regions face challenges related to data standardization, integration with legacy systems, and the need for skilled personnel. Addressing these issues through capacity building and international partnerships will be crucial for unlocking the full potential of GIS bay extension packages in these markets.

Report Scope

This map consists of short-term (~50 years) shoreline change rates for the lower 48 states of the United States and Hawaii. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.1, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change for the lower 48 states were calculated using an end-point rate method based on shorelines from 1970 or 1978 and 2000 to provide an approximately 30-yr short-term rate. Short-term rates of shoreline change for Hawaii were calculated using a weighted linear regression rate based on all available shoreline data between 1950 and 2008. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

This deep learning model is used to detect trees in low-resolution drone or aerial imagery. Tree detection can be used for applications such as vegetation management, forestry, urban planning, etc. High resolution aerial and drone imagery can be used for tree detection due to its high spatio-temporal coverage.

This deep learning model is based on MaskRCNN and has been trained on data from the DM Dataset preprocessed and collected by the IST Team.

There is no need of high-resolution imagery you can perform all your analysis on low resolution imagery by detecting the trees with the accuracy of 75% and finetune the model to increase your performance and train on your own data.

Licensing requirements ArcGIS Desktop – ArcGIS Image Analyst and ArcGIS 3D Analyst extensions for ArcGIS Pro ArcGIS Enterprise – ArcGIS Image Server with raster analytics configured ArcGIS Online – ArcGIS Image for ArcGIS Online

Using the model Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.

Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.

Input 3-band low-resolution (70 cm) satellite imagery.

Output Feature class containing detected trees

Applicable geographies The model is expected to work well in the U.A.E.

Model architecture This model is based upon the MaskRCNN python package and uses the Resnet-152 model architecture implemented in pytorch.

Training data This model has been trained on the Satellite Imagery created and Labelled by the team and validated on the different locations with more diverse locations.

Accuracy metrics This model has an average precision score of 0.45.

Sample results Here are a few results from the model.

Supermarkets are one of the most popular and convenient ways in which Americans gain access to healthy food, such as fresh meat and fish, or fresh fruits and vegetables. There are various ways in which people gain access to supermarkets. People in the suburbs drive to supermarkets and load up the car with many bags of food. People in cities depend much more on walking to the local store, or taking a bus or train.This map came about after asking a simple question: how many Americans live within a reasonable walk or drive to a supermarket?In this case, "reasonable" was defined as a 10 minute drive, or a 1 mile walk. The ArcGIS Network Analyst extension performed the calculations on NAVTEQ streets, and the ArcGIS Spatial Analyst extension created a heat map of the walkable access and drivable access to supermarkets.The green dots represent populations in poverty who live within one mile of a supermarket. The red dots represent populations in poverty who live beyond a one mile walk to a supermarket, but may live within a 10 minute drive...assuming they have access to a car. The grey dots represent the total population in a given area.This is an excellent map to use as backdrop to show how people are improving access to healthy food in their community. Open this map in ArcGIS Explorer to add your favorite farmers' market, CSA, or transit line -- then share that map via Facebook, Twitter or email.This map shows data for the entire U.S. The supermarkets included in the analysis have annual sales of $1 million or more. Populations in poverty are represented by taking the block group poverty rate (e.g. 10%) from the Census and symbolizing each block in that block group based on that percentage. Demographic data from U.S. Census 2010 and Esri Business location from infoUSAData sources: see this map package.

This dataset consists of short-term (~33 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using an end point rate-of-change method based on available shoreline data between 1979 and 2012. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. Transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into tree and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Trees is useful in applications such as high-quality 3D basemap creation, urban planning, forestry workflows, and planning climate change response.Trees could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Tree in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputThe model is trained with classified LiDAR that follows the LINZ base specification. The input data should be similar to this specification.Note: The model is dependent on additional attributes such as Intensity, Number of Returns, etc, similar to the LINZ base specification. This model is trained to work on classified and unclassified point clouds that are in a projected coordinate system, in which the units of X, Y and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Wellington CityTesting dataset - Tawa CityValidation/Evaluation dataset - Christchurch City Dataset City Training Wellington Testing Tawa Validating ChristchurchModel architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.991200 0.975404 0.983239 High Vegetation 0.933569 0.975559 0.954102Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 80%, Test: 20%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-121.69 m to 26.84 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-15 to +15 Maximum points per block8192 Block Size20 Meters Class structure[0, 5]Sample resultsModel to classify a dataset with 5pts/m density Christchurch city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

The data provided here denotes the authors’ revised service areas for a subset of 377 Public Housing Authorities (PHAs) for which HUD previously estimated service areas. Using HUD administrative data on the location of Housing Choice Voucher holders, HUD’s estimated service areas were revised to better capture voucher activity. Specifically, the authors developed two different tests and correction procedures. The first assesses if the estimated service area omits a sizable share of voucher holder locations (so is “too small”), and if so, adjusts to include census designated places or counties containing at least 5 percent of a PHA’s voucher holders. The second test checks whether the estimated service boundary includes areas the PHA does not appear to serve and that are clearly served by another PHA (so is “too large”), in this case adjusting by removing those areas. 148 of the 377 PHA estimated service areas were found to be too small, too large, or both, and so have revised service areas that differ from HUD’s estimated service areas. The detailed methodology is provided below. Additionally, a spreadsheet is supplied that identifies geographies that were added to and dropped from HUD’s estimated services to create the revised service areas for affected PHAs.

This is an experimental dataset that is designed to aid researchers in studying the HCV program. The methodology and the service areas themselves have not been validated by HUD’s Office of Public and Indian Housing (PIH) or the Public Housing Agencies. For additional discussion of the approach, see Tauber et al. (2024); please contact the authors with any questions or comments.

Reference:Tauber, Kristen, Ingrid Gould Ellen, and Katherine O’Regan. 2024. “Whom Do We Serve? Refining Public Housing Agency Service Areas.” Cityscape 26(1) (2024): 395-400.

eXtension Foundation, the University of New Hampshire, and Virginia Tech have developed a mapping and data exploration tool to assist Cooperative Extension staff and administrators in making strategic planning and programming decisions. The tool, called the National Extension Web-mapping Tool (or NEWT), is the key in efforts to make spatial data available within cooperative extension system. NEWT requires no GIS experience to use. NEWT provides access for CES staff and administrators to relevant spatial data at a variety of scales (national, state, county) in useful formats (maps, tables, graphs), all without the need for any experience or technical skills in Geographic Information System (GIS) software. By providing consistent access to relevant spatial data throughout the country in a format useful to CES staff and administrators, NEWT represents a significant advancement for the use of spatial technology in CES. Users of the site will be able to discover the data layers which are of most interest to them by making simple, guided choices about topics related to their work. Once the relevant data layers have been chosen, a mapping interface will allow the exploration of spatial relationships and the creation and export of maps. Extension areas to filter searches include 4-H Youth & Family, Agriculture, Business, Community, Food & Health, and Natural Resources. Users will also be able to explore data by viewing data tables and graphs. This Beta release is open for public use and feedback. Resources in this dataset:Resource Title: Website Pointer to NEWT National Extension Web-mapping Tool Beta. File Name: Web Page, url: https://www.mapasyst.org/newt/ The site leads the user through the process of selecting the data in which they would be most interested, then provides a variety of ways for the user to explore the data (maps, graphs, tables).

Surface geology, 1:500,000 scale, downloadable GIS data, June 2010, version 3. Downloadable GIS data includes: One ESRI ArcGIS 9.3 geodatabase, consisting of a set of 8 feature classes; Metadata for each feature class, in HTML format (for ease of reading outside of GIS software); One ArcGIS map document (ending in the .mxd extension), containing specifications for data presentation in ArcMap; One ArcGIS layer file for each feature class (ending in the .lyr extension), containing specifications for data presentation in the free ArcGIS Explorer (as well as ArcMap); README file.

The Seabed Landform Classification Toolset is a GIS toolbox designed to classify seabed landforms on continental and island shelf settings. The user is guided through a series of classification steps within an ArcGIS toolbox to classify prominent seabed features termed ‘seabed landforms’, which characterise the morphology of the seabed surface. Seabed landforms include reefs/banks, peaks, plains, scarps, channels and depressions. Plain areas can additionally be classified into high and low features at localised and broad scales to capture features within plain surfaces. Common variables for seabed classification are utilised, including slope, bathymetric position index and ruggedness, and a series of procedures are applied to identify reef outcrops and minimise noise. The classification approach applies a whole-seascape classification which is aimed to offer a flexible and user-friendly approach to extract key seabed features from high-resolution shelf bathymetry data.

This toolset was developed using ESRI ArcGIS Desktop 10.8 and requires an Advanced licence with Spatial Analyst and 3D Analyst and extensions. It utilises scripts within the Benthic Terrain Modeler toolset (Walbridge et al. 2018) and Geomorphometry and Gradients Metrics Toolbox (Evans et al., 2014).

Please read the User Guide and supporting documentation for information on how to run the toolset. A web explainer is available at: https://arcg.is/1Tqmv50

The Seabed Landform Classification Toolset is also available for download on GitHub (https://github.com/LinklaterM/Seabed-Landforms-Classification-Toolset/).

The toolset was developed by the Coastal and Marine Team, NSW Department of Climate Change, Energy, the Environment and Water (formerly NSW Department of Planning and Environment), funded by NSW Climate Change Fund through the Coastal Management Funding Package and the Marine Estate Management Authority.

Please cite this toolset as: Linklater, M, Morris, B.D. and Hanslow, D.J. (2023) Classification of seabed landforms on continental and island shelves. Frontiers of Marine Science, 10, https://doi.org/10.3389/fmars.2023.1258556.

Other toolsets utilised by the Seabed Landform Classification Toolset include: Benthic Terrain Modeler: Walbridge, S., Slocum, N., Pobuda, M., and Wright, D. J. (2018). Unified geomorphological analysis workflows with Benthic Terrain Modeler. Geosciences 8, 94. Geomorphometry and Gradients Metrics Toolbox: Evans, J., Oakleaf, J., and Cushman, S. (2014). An ArcGIS Toolbox for Surface Gradient and Geomorphometric Modeling, Version 2.0-0. https://github.com/jeffreyevans/GradientMetrics.

This web map shows the cumulative data of executed lot extension transactions from 2018 onwards including the date of execution, lot number, location, user, premium and date of registration. It is a subset of data made available by Lands Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.

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

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Valles Calders, upper part of the Jemez River basin by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate orthogonal transects at a user-defined separation along the coast, and calculate rates of change (linear regression, endpoint rate, average of rates, average of endpoints, jackknife).