These data provide an accurate high-resolution shoreline compiled from imagery of Portland, ME . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source...

Tile Download Link 2006 3" pixel resolution orthoimagery of the city of Portland ME. Provided by the Maine Office of GIS and the Maine GeoLibrary Board. Portland 2006 3" orthoimagery - As the prime contractor, Bradstreet Consultants, Inc. used the aerial photography flown in one session on April 26th, 2006, by Richard Crouse & Associates, Inc. of Frederick, MD who acquired approximately 1335 photos @ 1"=600' with airborne GPS using a Wild RC-30 (#13261) aerial camera. Bradstreet Consultants, Inc. painted and repainted ground targets for photo survey control points (~200) to support full analytical aerotriangulation. The aerotriangulation solution was used to set up each stereopair of photos for orthorectification and DTM compilation. The ortho imagery was created by scanning (14 microns (um) per pixel or 1815 dpi) the original 1"=600' scale film and employing a digital terrain model (DTM) from updating some town's original DTM and some town's from scratch using the 1"=600' scale negatives or diapositives using both analytical and softcopy (digital) stereoplotter in the Kork KDMS format.

This tree map viewer shows tree types, size, condition and etc.

Tile Download Link 2005 6" pixel resolution orthoimagery of South Portland from May 6, 2005. Provided by the Maine Office of GIS and the Maine GeoLibrary Board. As the prime contractor, Bradstreet Consultants, Inc. used the aerial photography flown in one session on May 6, 2005, by Richard Crouse & Associates, Inc. of Frederick, MD who acquired approximately 1335 photos @ 1"=600' with airborne GPS using a Wild RC-30 (#13261) aerial camera. Bradstreet Consultants, Inc. painted and repainted ground targets for photo survey control points (~200) to support full analytical aerotriangulation. The aerotriangulation solution was used to set up each stereopair of photos for orthorectification and DTM compilation. The ortho imagery was created by scanning (14 microns (um) per pixel or 1815 dpi) the original 1"=600' scale film and employing a digital terrain model (DTM) from updating some town's original DTM and some town's from scratch using the 1"=600' scale negatives or diapositives using both analytical and softcopy (digital) stereoplotter in the Kork KDMS format.

These data were automated to provide an accurate high-resolution historical shoreline of Portland City and Harbor, Maine suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

Maine Statewide Orthoimagery Project - During the spring of 2020 new 4-band (R, G, B, and NIR) aerial imagery was acquired covering the entire project area using Leica ADS digital camera systems. All imagery was collected during the 2022 spring flying season during leaf-off conditions for deciduous vegetation in the State of Maine. The sun angle shall be 25-degrees or greater, and streams should be within their normal banks, unless otherwise negotiated. During flight planning and acquisition, a significant effort is made to limit clouds, snow (please note: small amounts of snow such as piles in parking lots, extreme shaded areas, within dense evergreens or unpopulated northern facing slopes may be acceptable), fog, haze, smoke, or other ground obscuring conditions in the imagery. In no case will the maximum cloud cover exceed 5% per image. Within the immediate areas of power plants, factories, or controlled agricultural burns some steam or smoke and/or shadows may be visible on imagery. Woolpert produced new 8-bit, 4-band stacked color digital orthoimagery files in GeoTIFF format with TFW “world file” at a 45cm (18-inch), 30cm (12-inch), 15cm (6-inch) and 7.5cm (3-inch).The Maine GeoLibrary Board has developed a statewide, 5-year, rotating orthoimagery acquisition program for Maine to facilitate state, regional and local government GIS base mapping in an efficient and cost-effective program. The State of Maine will use digital orthoimagery for the development of various base map products in a computerized GIS that will support the needs of the state and multiple stakeholders through applications, such as, multi-jurisdictional homeland security mapping applications, state and county emergency management applications, regional and local planning, state and local public safety applications, economic development and other GIS business objectives.

A layer showing GPCOG's 26 member communities: Bridgton, Cape Elizabeth, Casco, Chebeague Island, Cumberland, Cumberland County, Durham, Falmouth, Freeport, Frye Island, Gorham, Gray, Harrison, Long Island, Naples, New Gloucester, North Yarmouth, Portland, Pownal, Raymond, Scarborough, Sebago, South Portland, Standish, Westbrook, Windham, Yarmouth.

The Communities at Sea maps use Vessel Trip Report location point data as input to create density polygons representing visitation frequency ("fisherdays"). The data show total labor including crew time and the time spent in transit to and from fishing locations. They do not show other variables such as vessel value or number of pounds landed. The results can be interpreted as maps of "community presence." This layer shows data for the gillnet fishing gear group for Portland, ME from 2011-2015.

The Communities at Sea maps use Vessel Trip Report location point data as input to create density polygons representing visitation frequency ("fisherdays"). The data show total labor including crew time and the time spent in transit to and from fishing locations. They do not show other variables such as vessel value or number of pounds landed. The results can be interpreted as maps of "community presence." This layer shows data for the pots and traps fishing gear group for Portland, ME from 2011-2015.

description: Abstract: Monthly and annual average solar resource potential for the lower 48 states of the United States of America. Purpose: Provide information on the solar resource potential for the for the lower 48 states of the United States of America. Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximatley 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. Other Citation Details: George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME. ### License Info This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data. Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data. THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA. The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.; abstract: Abstract: Monthly and annual average solar resource potential for the lower 48 states of the United States of America. Purpose: Provide information on the solar resource potential for the for the lower 48 states of the United States of America. Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximatley 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. Other Citation Details: George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME. ### License Info This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data. Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data. THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA. The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

A map used in the Transportation Hazard Reporter app to report transportation hazards and dangerous traffic behaviors that could lead to fatalities or serious injuries.

Greater Portland Landmarks undertook a reconnaissance-level survey of 1032 properties in Portland and South Portland, Maine. The goal of the survey was to document historic neighborhoods that were located in low-lying areas prone to flooding due to storms and rising sea levels and to create historic statements for the two neighborhoods. In addition to recording architectural features the survey looked at vulnerabilities, including height of utilities, openings at grade, poor drainage, etc. This map overlays the highest mean tide plus five levels of storm surge over the surveyed properties in order to help property owners and municipal planners understand which historic buildings are threatened by sea level rise or intense storms.

The Communities at Sea maps use Vessel Trip Report location point data as input to create density polygons representing visitation frequency ("fisherdays"). The data show total labor including crew time and the time spent in transit to and from fishing locations. They do not show other variables such as vessel value or number of pounds landed. The results can be interpreted as maps of "community presence." This layer shows data for the small bottom trawl fishing gear group for Portland, ME from 2011-2015.

This layer shows the boundaries of the areas in Maine which are under the jurisdiction of each DEP office, based on the 1:24,000 towns layer provided by Maine Office of GIS. It can be used to quickly identify which DEP office would have jurisdiction over sites in Maine. CMRO - Central Maine Office in Augusta, EMRO - Eastern Maine Office in Bangor, NMRO - Northern Maine Office in Presque Isle, Southern Maine Office in Portland.

Executive summary of Portland Commuter Rail Study: Portland, ME to Brunswick and Auburn, ME (2005), HNTB and the Department of Transportation. Prepared for 2018 Strategic Planning process. Includes link to report.

Line feature class which represents coastal engineering structures along coastlines of communities in York and Cumberland Counties, Maine, from Kittery to South Portland.

Microsoft recently released a free set of deep learning generated building footprints covering the United States of America. As part of that project Microsoft shared 8 million digitized building footprints with height information used for training the Deep Learning Algorithm. This map layer includes all buildings with height information for the original training set that can be used in scene viewer and ArcGIS pro to create simple 3D representations of buildings. Learn more about the Microsoft Project at the Announcement Blog or the raw data is available at Github.Click see Microsoft Building Layers in ArcGIS Online.Digitized building footprint by State and City

Alabama Greater Phoenix City, Mobile, and Montgomery

Arizona Tucson

Arkansas Little Rock with 5 buildings just across the river from Memphis

California Bakersfield, Fresno, Modesto, Santa Barbara, Sacramento, Stockton, Calaveras County, San Fran & bay area south to San Jose and north to Cloverdale

Colorado Interior of Denver

Connecticut Enfield and Windsor Locks

Delaware Dover

Florida Tampa, Clearwater, St. Petersburg, Orlando, Daytona Beach, Jacksonville and Gainesville

Georgia Columbus, Atlanta, and Augusta

Illinois East St. Louis, downtown area, Springfield, Champaign and Urbana

Indiana Indianapolis downtown and Jeffersonville downtown

Iowa Des Moines

Kansas Topeka

Kentucky Louisville downtown, Covington and Newport

Louisiana Shreveport, Baton Rouge and center of New Orleans

Maine Augusta and Portland

Maryland Baltimore

Massachusetts Boston, South Attleboro, commercial area in Seekonk, and Springfield

Michigan Downtown Detroit

Minnesota Downtown Minneapolis

Mississippi Biloxi and Gulfport

Missouri Downtown St. Louis, Jefferson City and Springfield

Nebraska Lincoln

Nevada Carson City, Reno and Los Vegas

New Hampshire Concord

New Jersey Camden and downtown Jersey City

New Mexico Albuquerque and Santa Fe

New York Syracuse and Manhattan

North Carolina Greensboro, Durham, and Raleigh

North Dakota Bismarck

Ohio Downtown Cleveland, downtown Cincinnati, and downtown Columbus

Oklahoma Downtown Tulsa and downtown Oklahoma City

Oregon Portland

Pennsylvania Downtown Pittsburgh, Harrisburg, and Philadelphia

Rhode Island The greater Providence area

South Carolina Greensville, downtown Augsta, greater Columbia area and greater Charleston area

South Dakota greater Pierre area

Tennessee Memphis and Nashville

Texas Lubbock, Longview, part of Fort Worth, Austin, downtown Houston, and Corpus Christi

Utah Salt Lake City downtown

Virginia Richmond

Washington Greater Seattle area to Tacoma to the south and Marysville to the north

Wisconsin Green Bay, downtown Milwaukee and Madison

Wyoming Cheyenne