This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for Virginia Beach, VA. As such, it represents a "top down" mapping perspective. This layer is a high-resolution land-cover dataset for Virginia Beach, Virginia. Nine land-cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads; (7) other paved surfaces; (8) forested wetlands; and (9) non-forested wetlands. The primary sources used to derive this land cover layer were 2012 LiDAR and 2013 3-band orthoimagery. Ancillary data sources included GIS data provided by Virginia Beach (e.g., county boundary, building footprints, water, parking lots, roads, utility poles, bridges, airfields) or developed by the UVM Spatial Analysis Laboratory (bare soils, utility lines, modified National Wetland Inventory wetlands polygons). This land cover dataset is considered current as of 2012. Object-based image analysis (OBIA) was used to extract land-cover information using the best available remotely-sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment, a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were used to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to thorough manual quality control. More than 50,000 corrections were made to the classification

This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for Virginia Beach, VA. As such, it represents a "top down" mapping perspective. This layer is a high-resolution land-cover dataset for Virginia Beach, Virginia. Nine land-cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads; (7) other paved surfaces; (8) forested wetlands; and (9) non-forested wetlands. The primary sources used to derive this land cover layer were 2012 LiDAR and 2013 3-band orthoimagery. Ancillary data sources included GIS data provided by Virginia Beach (e.g., county boundary, building footprints, water, parking lots, roads, utility poles, bridges, airfields) or developed by the UVM Spatial Analysis Laboratory (bare soils, utility lines, modified National Wetland Inventory wetlands polygons). This land cover dataset is considered current as of 2012. Object-based image analysis (OBIA) was used to extract land-cover information using the best available remotely-sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment, a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were used to ensure that the end product is both accurate and cartographically pleasing. No accuracy assessment was conducted, but the dataset was subjected to thorough manual quality control. More than 50,000 corrections were made to the classification