9,381 results returned
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Title: USA (Populated Places, 2003)
Contributors:- Point data
- 2008
Summary: This data set includes cities in the United States, Puerto Rico and the U.S. Virgin Islands. These cities were collected from the 1970 National Atlas of the United States. Where applicable, U.S. Census Bureau codes for named populated places were associated with each name to allow additional information to be attached. The Geographic Names Information System (GNIS) was also used as a source for additional information. This is a revised version of the December, 2003, data set.
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Title: USA (Water Bodies, 2005)
Contributors:- Polygon data
- 2005
- MIT authentication required
Summary: U.S. Water Bodies represents the major lakes, reservoirs, large rivers, lagoons, and estuaries in the United States.
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Title: Lidar-Derived Tiled DEM for Buffalo County, WI 2016
Contributors:- Raster data
- 2023
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents a Lidar-derived tiled Digital Elevation Model (DEM) for Buffalo County, Wisconsin in 2023. A DEM represents the bare-Earth surface, removing all natural and built features. This dataset contains individual files available for download consisting of smaller tiled geographic areas over the extent of an entire county.
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Title: Lidar Tile Index Buffalo County, WI 2023
Contributors:- Polygon data
- 2023
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents the tile index used for Lidar-derived datasets in Buffalo County, Wisconsin in 2023. The tile index is a shapefile that shows how tiled Lidar datasets are labeled and where they are located geographically.
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Title: Lidar-Derived Breaklines for Buffalo County, WI 2023
Contributors:- Line data
- 2023
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents Lidar-derived breaklines in Buffalo County, Wisconsin in 2023. Hydro breaklines maintain the definition of water-related features in an elevation model. They are used to capture linear discontinuities in the surface, lake shorelines, single-line drains for small rivers, and double-line drains for large rivers.
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Title: Lidar-Derived Classified LAS for Buffalo County, WI 2023
Contributors:- Point data
- 2023
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents Lidar-derived classified LAS points for Buffalo County, Wisconsin in 2023. Point classification uses semi-automated techniques on the point cloud to assign the feature type associated with each point. Lidar points can be classified into a number of categories including bare earth or ground, top of canopy, and water. The different classes are defined using numeric integer codes in the LAS files.
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Title: Lidar-Derived Intensity Images Buffalo County, WI 2023
Contributors:- Raster data
- 2023
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents Lidar-derived tiled intensity images for Buffalo County, Wisconsin in 2023. Lidar intensity is recorded as the return strength of a laser beam. It is a bi-product, provided as an integer number between 1-256. This number varies with the composition of the surface object reflecting the laser beam. A low number indicates low reflectivity while a high number indicates high reflectivity. The intensity of the laser beam return can also be affected by the angle of arrival (scan angle), range, surface composition, roughness, and moisture content.
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Title: Lidar-Derived Tiled DEM for Manitowoc County, WI 2023
Contributors:- Raster data
- 2023
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents a Lidar-derived tiled Digital Elevation Model (DEM) for Manitowoc County, Wisconsin in 2023. A DEM represents the bare-Earth surface, removing all natural and built features. This dataset contains individual files available for download consisting of smaller tiled geographic areas over the extent of an entire county.
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Title: Lidar-Derived Intensity Images for Manitowoc County, WI 2023
Contributors:- Raster data
- 2023
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents Lidar-derived tiled intensity images for Manitowoc County, Wisconsin in 2023. Lidar intensity is recorded as the return strength of a laser beam. It is a bi-product, provided as an integer number between 1-256. This number varies with the composition of the surface object reflecting the laser beam. A low number indicates low reflectivity while a high number indicates high reflectivity. The intensity of the laser beam return can also be affected by the angle of arrival (scan angle), range, surface composition, roughness, and moisture content.
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Title: Lidar Tile Index for Manitowoc County, WI 2023
Contributors:- Polygon data
- 2023
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents the tile index used for Lidar-derived datasets in Manitowoc County, Wisconsin in 2023. The tile index is a shapefile that shows how tiled Lidar datasets are labeled and where they are located geographically.
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Title: Lidar-Derived Breaklines for Manitowoc County, WI 2023
Contributors:- Line data
- 2023
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents Lidar-derived breaklines for Manitowoc County, Wisconsin in 2023. Hydro breaklines maintain the definition of water-related features in an elevation model. They are used to capture linear discontinuities in the surface, lake shorelines, single-line drains for small rivers, and double-line drains for large rivers.
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Title: Lidar-Derived Classified LAS for Manitowoc County, WI 2023
Contributors:- Point data
- 2023
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents Lidar-derived classified LAS points for Manitowoc County, Wisconsin in 2023. Point classification uses semi-automated techniques on the point cloud to assign the feature type associated with each point. Lidar points can be classified into a number of categories including bare earth or ground, top of canopy, and water. The different classes are defined using numeric integer codes in the LAS files.
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Title: LiDAR-Derived Tiled Digital Elevation Model (DEM) for Bayfield County, WI 2022
Contributors:- Raster data
- 2022
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents a LiDAR-derived tiled Digital Elevation Model (DEM) for Bayfield County, Wisconsin in 2022. A DEM represents the bare-Earth surface, removing all natural and built features.
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Title: LiDAR-Derived Intensity Images for Bayfield County, WI 2022
Contributors:- Raster data
- 2022
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents LiDAR-derived intensity images for Bayfield County, Wisconsin in 2022. LiDAR intensity is recorded as the return strength of a laser beam. It is a bi-product, provided as an integer number between 1-256. This number varies with the composition of the surface object reflecting the laser beam. A low number indicates low reflectivity while a high number indicates high reflectivity. The intensity of the laser beam return can also be affected by the angle of arrival (scan angle), range, surface composition, roughness, and moisture content.
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Title: LiDAR-Derived Breaklines for Bayfield County, WI 2022
Contributors:- Line data
- 2022
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents LiDAR-derived breaklines in Bayfield County, Wisconsin in 2022. Hydro breaklines maintain the definition of water-related features in an elevation model. They are used to capture linear discontinuities in the surface, lake shorelines, single-line drains for small rivers, and double-line drains for large rivers.
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Title: LiDAR-Derived Classified LAS for Bayfield County, WI 2022
Contributors:- Point data
- 2022
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents LiDAR-derived classified LAS points for Bayfield County, Wisconsin in 2022. Point classification uses semi-automated techniques on the point cloud to assign the feature type associated with each point. LiDAR points can be classified into a number of categories including bare earth or ground, top of canopy, and water. The different classes are defined using numeric integer codes in the LAS files.
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Title: LiDAR Tile Index for Bayfield County, WI 2022
Contributors:- Polygon data
- 2022
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents the LiDAR tile index for Bayfield County, Wisconsin in 2022. The tile index shows how tiled lidar datasets are labeled and where they are located geographically.
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Title: LiDAR-Derived Classified LAS for Burnett County, WI 2022
Contributors:- Point data
- 2022
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents LiDAR-derived classified LAS points for Burnett County, Wisconsin in 2022. Point classification uses semi-automated techniques on the point cloud to assign the feature type associated with each point. LiDAR points can be classified into a number of categories including bare earth or ground, top of canopy, and water. The different classes are defined using numeric integer codes in the LAS files.
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Title: LiDAR-Derived Tiled Digital Elevation Model (DEM) for Burnett County, WI 2022
Contributors:- Raster data
- 2022
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents a LiDAR-derived tiled Digital Elevation Model (DEM) for Burnett County, Wisconsin in 2022. A DEM represents the bare-Earth surface, removing all natural and built features.
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Title: LiDAR-Derived Intensity Images for Burnett County, WI 2022
Contributors:- Raster data
- 2022
- Not owned by MIT (Owned by University of Wisconsin-Madison)
Summary: This data represents LiDAR-derived intensity images for for Burnett County, Wisconsin in 2015. LiDAR intensity is recorded as the return strength of a laser beam. It is a bi-product, provided as an integer number between 1-256. This number varies with the composition of the surface object reflecting the laser beam. A low number indicates low reflectivity while a high number indicates high reflectivity. The intensity of the laser beam return can also be affected by the angle of arrival (scan angle), range, surface composition, roughness, and moisture content.