This dataset is from the City of Boston's Street Address Management (SAM) system, containing Boston addresses. Updated nightly and shared publicly.

Segmentation models perform a pixel-wise classification by classifying the pixels into different classes. The classified pixels correspond to different objects or regions in the image. These models have a wide variety of use cases across multiple domains. When used with satellite and aerial imagery, these models can help to identify features such as building footprints, roads, water bodies, crop fields, etc.Generally, every segmentation model needs to be trained from scratch using a dataset labeled with the objects of interest. This can be an arduous and time-consuming task. Meta's Segment Anything Model (SAM) is aimed at creating a foundational model that can be used to segment (as the name suggests) anything using zero-shot learning and generalize across domains without additional training. SAM is trained on the Segment Anything 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks. This makes the model highly robust in identifying object boundaries and differentiating between various objects across domains, even though it might have never seen them before. Use this model to extract masks of various objects in any image.Using the modelFollow 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. Fine-tuning the modelThis model can be fine-tuned using SamLoRA architecture in ArcGIS. Follow the guide and refer to this sample notebook to fine-tune this model.Input8-bit, 3-band imagery.OutputFeature class containing masks of various objects in the image.Applicable geographiesThe model is expected to work globally.Model architectureThis model is based on the open-source Segment Anything Model (SAM) by Meta.Training dataThis model has been trained on the Segment Anything 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks.Sample resultsHere are a few results from the model.

A web service of the Address Point file of buildings and properties in New York State. See metadata for additional information. Additional metadata, including field descriptions, can be found at the NYS GIS Clearinghouse: https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=921.SAM Address Points Data Dictionary: https://gis.ny.gov/gisdata/supportfiles/Address-Points-Data-Dictionary.pdfIf the purpose of accessing the address points service is for geocoding, NYS ITS has a publicly available geocoding service which includes the address points along with other layers. For more information about the geocoding service, please visit: https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1278.For more information about the SAM Program, please visit:https://gis.ny.gov/streets.Publication Date: See Update FrequencyCurrent as of Date: 2 business days prior to Publication DateUpdate frequency: Second and forth Fridays of each monthThis map service is available to the public.The State of New York, acting through the New York State Office of Information Technology Services, makes no representations or warranties, express or implied, with respect to the use of or reliance on the Data provided. The User accepts the Data provided “as is” with no guarantees that it is error free, complete, accurate, current or fit for any particular purpose and assumes all risks associated with its use. The State disclaims any responsibility or legal liability to Users for damages of any kind, relating to the providing of the Data or the use of it. Users should be aware that temporal changes may have occurred since this Data was created.

A Feature web service of the Address Point file of buildings and properties in New York State. Please note that, due to the large size, the NYS Address Point statewide layer cannot be downloaded in shapefile format. A map service of the Street and Address Maintenance (SAM) Program Address Point file is available here: https://gisservices.its.ny.gov/arcgis/rest/services.SAM Address Points Data Dictionary: https://gis.ny.gov/system/files/documents/2024/02/address-points-data-dictionary.pdf. If the purpose of accessing the address points service is for geocoding, NYS ITS has a publicly available geocoding service which includes the address points along with other layers. For more information about the geocoding service, please visit: https://gis.ny.gov/address-geocoder. For more information about the SAM Program, please visit: https://gis.ny.gov/streets-addresses.Please contact NYS ITS Geospatial Services at nysgis@its.ny.gov if you have any questions. Publication Date: See Update Frequency. Current as of Date: 2 business days prior to Publication Date. Update frequency: Second and fourth Friday of each month. Spatial Reference of Source Data: NAD_1983_UTM_Zone_18N. Spatial Reference of Map Service: WGS 1984 Web Mercator Auxiliary.This feature service is available to the public.

City of Boston street segments data from the Street Address Management (SAM) system. Updated nightly.

Summary statistics and autocorrelation coefficient for the number of cases of severe acute malnutrition as assessed in 153 Jamaican communities.

Link to the Sam Transit website that provides public transit for Sioux Falls, South Dakota.

ObjectivesSevere acute malnutrition (SAM) is an important risk factor for illness and death globally, contributing to more than half of deaths in children worldwide. We hypothesized that SAM is positively correlated to poverty, low educational attainment, major crime and higher mean soil concentrations of lead, cadmium and arsenic.MethodsWe reviewed admission records of infants admitted with a diagnosis of SAM over 14 years (2000–2013) in Jamaica. Poverty index, educational attainment, major crime and environmental heavy metal exposure were represented in a Geographic Information System (GIS). Cases of SAM were grouped by community and the number of cases per community/year correlated to socioeconomic variables and geochemistry data for the relevant year.Results375 cases of SAM were mapped across 204 urban and rural communities in Jamaica. The mean age at admission was 9 months (range 1–45 months) and 57% were male. SAM had a positive correlation with major crime (r = 0.53; P < 0.001), but not with educational attainment or the poverty index. For every one unit increase in the number of crimes reported, the rate of occurrence of SAM cases increased by 1.01% [Incidence rate ratio (IRR) = 1.01 (95% CI = 1.006–1.014); P P

An Exit point layer (excluding NYC) suitable for use in a GIS. For more information about the SAM Program, please visit: https://gis.ny.gov/streets-addresses. This map service is available to the public. Spatial Reference of Source Data: NAD_1983_UTM_Zone_18N. Spatial Reference of Map Service: WGS 1984 Web Mercator Auxiliary.

Original Data: These files contain rasterized topographic lidar elevations generated from data collected using a Teledyne ALTM Galaxy PRIME sensor. Native lidar data is not generally in a format accessible to most Geographic Information Systems (GIS). Specialized in-house and commercial software packages are used to process the native lidar data into 3-dimensional positions that can be imported into GIS software for visualization and further analysis. Horizontal positions are referenced to the North American Datum of 1983 Universal Transverse Mercator Zone 16 North (NAD83 UTM Zone 16N). Vertical positions are referenced to the NAD83 (2011) ellipsoid and provided in meters. The National Geodetic Survey's (NGS) GEOID18 model is used to transform the vertical positions from ellipsoid to orthometric heights referenced to the North American Vertical Datum of 1988 (NAVD88). The 3-D position data are sub-divided into a series of LAS files, which are tiled into 1-km by 1-km boxes defined by the Military Grid Reference System. The data were provided to the NOAA Office for Coastal Management (OCM) by the USACE Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) to make the data publicly available for bulk and custom downloads from the NOAA Digital Data Access Viewer (DAV).

This project explores the feasibility of integrating solar-powered infrastructure into bike pathways as a sustainable energy and transportation solution for California. Using advanced tools like ArcGIS (for analysis), PVWatts, SAM, and JEDI, this study evaluates the economic, environmental, and technical implications through a conceptual case study based in Riverside. Insights drawn from global case studies and stakeholder feedback highlight challenges such as financial constraints, regulatory complexities, and technical design considerations, while also identifying opportunities for renewable energy generation, greenhouse gas emission reductions, and enhanced urban mobility. The conceptual case study serves as a framework for assessing potential benefits and informing actionable strategies. Recommendations address barriers and align implementation with California’s climate action and sustainability goals, offering a roadmap for integrating renewable energy with active transportation sy..., The data collection and processing methods for this project utilized a combination of publicly available tools and resources to ensure accuracy and usability. Key geospatial, energy modeling, and economic analysis data were gathered using reliable tools such as ArcGIS, SAM, JEDI, and PVWatts, with outputs systematically processed into accessible formats. This approach enabled comprehensive analysis of bike path integration, energy performance, and economic impacts.

Data Collection:

BikePaths_Riverside.qgz: Geospatial data detailing bike paths in Riverside was gathered from publicly available sources and initially analyzed using ArcGIS Pro. To ensure open access and reusability, the data has been converted to a .qgz project file compatible with QGIS (version 3.42), a free and open-source GIS platform.

SAM_Input_Variable_Values.csv: Input parameters were collected based on standard system specifications, financial assumptions, and default or adjusted inputs available in the System Ad..., , # Data for: Solar bike path feasibility study in California

https://doi.org/10.5061/dryad.4tmpg4fn1

Description of the data and file structure

The data was collected to evaluate the feasibility, technical requirements, and potential impacts of integrating solar-powered infrastructure into bike pathways. The study utilized geospatial data from ArcGIS for spatial analysis and site evaluation, combined with energy modeling tools such as PVWatts and SAM to estimate energy production, greenhouse gas reductions, and financial metrics. The JEDI model was employed to assess economic and job creation impacts. These efforts were guided by a conceptual case study in Riverside, California, to simulate real-world scenarios and inform actionable strategies for renewable energy integration. Feedback from stakeholders further shaped the analysis, addressing technical, economic, and regulatory challenges while aligning with California's sustainability goa...,

This is an ArcGIS Online web service updated by the Colorado Parks and Wildlife GIS Unit for distributing Colorado state parks and wildlife species GIS data for public distribution.

This file was updated on December 9, 2024.

Comment here regarding the Sam Houston National Forest’s Initial Wilderness Inventory.

Summary statistics and Spearman rank correlation coefficient for the associations between socioeconomic and geochemical variables and number of cases of severe acute malnutrition as assessed in up to 204 Jamaican communities.

NYPlace uses Municipality Centroid Points to return a match within a NYS Cities, Towns, Villages, Indian Reservations, Unincorporated Places, and Neighborhoods.Locator NameSource DataDescriptionNYPlaceMunicipality Centroid PointsThis locator contains points placed at the centroid of NYS Cities, Towns, Villages, Indian Reservations, Unincorporated Places, and Neighborhoods.

This deep learning model is used to detect and segment trees in high 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 DeepForest and has been trained on data from the National Ecological Observatory Network (NEON). The model also uses Segment Anything Model (SAM) by Meta.Using the modelFollow 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.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS tools.Input8 bit, 3-band high-resolution (10-25 cm) imagery.OutputFeature class containing separate masks for each tree.Applicable geographiesThe model is expected to work well in the United States.Model architectureThis model is based upon the DeepForest python package which uses the RetinaNet model architecture implemented in torchvision and open-source Segment Anything Model (SAM) by Meta.Accuracy metricsThis model has an precision score of 0.66 and recall of 0.79.Training dataThis model has been trained on NEON Tree Benchmark dataset, provided by the Weecology Lab at the University of Florida. The model also uses Segment Anything Model (SAM) by Meta that is trained on 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks.Sample resultsHere are a few results from the model.CitationsWeinstein, B.G.; Marconi, S.; Bohlman, S.; Zare, A.; White, E. Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks. Remote Sens. 2019, 11, 1309Geographic Generalization in Airborne RGB Deep Learning Tree Detection Ben Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Ethan P White bioRxiv 790071; doi: https://doi.org/10.1101/790071

MassGIS is working very closely with the State 911 Department in the state’s Executive Office of Public Safety and Security on the Next Generation 911 Emergency Call System. MassGIS developed and is maintaining the map and address information that are at the heart of this new system. Statewide deployment of this new 9-1-1 call routing system was completed in 2018.Address sources include the Voter Registration List from the Secretary of the Commonwealth, site addresses from municipal departments (primarily assessors), and customer address lists from utilities. Addresses from utilities were “anonymized” to protect customer privacy. The MAD was also validated for completeness using the Emergency Service List (a list of telephone land line addresses) from Verizon.The MAD contains both tabular and spatial data, with addresses being mapped as point features. At present, the MAD contains 3.2 million address records and 2.2 million address points. As the database is very dynamic with changes being made daily, the data available for download will be refreshed weekly.A Statewide Addressing Standard for Municipalities is another useful asset that has been created as part of this ongoing project. It is a best practices guide for the creation and storage of addresses for Massachusetts Municipalities.Points features with each point having an address to the building/floor/unit level, when that information is available. Where more than one address is located at a single location multiple points are included (i.e. "stacked points"). The points for the most part represent building centroids. Other points are located as assessor parcel centroids.Points will display at scales 1:75,000 and closer.MassGIS' service does not contain points for Boston; they may be accessed at https://data.boston.gov/dataset/live-street-address-management-sam-addresses/resource/873a7659-68b6-4ac0-98b7-6d8af762b6f1.More details about the MAD and Master Address Points.Feature service also available.

Autocorrelation coefficient (lag 1) for the poverty and crime indices and Pearson correlation coefficient for correlation of these variables with the number of cases of severe acute malnutrition as assessed in up to 153 Jamaican communities.

Shows the management zones used by Bay of Plenty Regional Council to manage and monitor water use and water quality in the region. Full Architecture for this project can be found here.Created as part of BOPRC Biosecurity GIS development, commenced in April 2020.Scott Sambell from Ethos Environmental is contracted by the Biosecurity Team to create integrated system on boprc.maps.arcgis.com for recording, analysing and reporting pest weed observations and actions. Sam Stephens and Juliet O'Connell are the BOPRC contacts.Contacts:Scott Sambell: scott@ethosgis.comSam Stephens: Sam.Stephens@boprc.govt.nzJuliet O'Connell: Juliet.O'Connell@boprc.govt.nz

The global utility coordination services market is experiencing robust growth, driven by increasing urbanization, expanding infrastructure projects, and stringent regulations aimed at minimizing disruptions during utility work. The market's size in 2025 is estimated at $15 billion, reflecting a compound annual growth rate (CAGR) of approximately 7% from 2019 to 2024. This growth is fueled by several key factors. Firstly, the rising complexity of underground and above-ground utility networks necessitates sophisticated coordination to prevent costly damages and service interruptions. Secondly, governments worldwide are increasingly mandating utility coordination to ensure public safety and efficient project delivery. This regulatory push is particularly evident in North America and Europe, regions that currently hold significant market share. Finally, technological advancements, such as GIS mapping and digital collaboration platforms, are improving efficiency and reducing the risk of errors in utility coordination. The Water and Wastewater, and Electric Power application segments are expected to lead the market growth, followed by Oil and Gas and Transportation sectors due to their extensive utility networks. The market is segmented by application (Water and Wastewater, Electric Power, Transportation, Oil and Gas, Telecommunications, Others) and type (Underground Utility, Above Ground Utility). While North America and Europe currently dominate the market, Asia-Pacific is poised for significant growth, driven by rapid infrastructure development in countries like China and India. However, factors like high initial investment costs for technology adoption and a potential shortage of skilled professionals could restrain market growth to some extent. The competitive landscape is characterized by a mix of large multinational firms and specialized regional players. Companies are focusing on strategic partnerships, technological innovations, and expansion into new geographic markets to maintain a competitive edge. The forecast period (2025-2033) anticipates continued market expansion, with the CAGR potentially increasing slightly as technology adoption accelerates and infrastructure development continues globally. This positive outlook makes the utility coordination services market an attractive sector for investment and expansion.