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This includes LiDAR data, which was collected at varying time periods in New Jersey, road centerlines, and the National Hydrography Dataset (NHD). Initial feature extraction was carried out in the coordinate system of the LiDAR data (most often UTM). LiDAR point clouds were normalized and classified to separate out buildings and trees. Raster surface models and intensity images were generated from the LiDAR point cloud data. Imagery data were mosaicked, and vector datasets were examined for completeness, accuracy, and topology.This project employed a semi-automated approach in which features were automatically extracted then manually reviewed and edited. The automated feature extraction workflow used an OBIA framework to automatically extract aforementioned impervious classes from the source datasets using a rule-based expert system. The expert system made use of segmentation, classification, and morphology algorithms to group pixels into objects, classify the objects based on the properties of the source datasets, and then refined the objects to enhance aesthetics and improve visual realism. The approach to mapping involved both automated feature extraction techniques and manual editing of impervious surfaces. A \u201cbottom-up\u201d approach to mapping was carried out in which impervious surface features that are under tree canopy but visible in the leaf-off imagery was mapped. The key advantage of the object-based approach is that the use of the spectral, textural, geometric, and contextual properties in the imagery reduce confusion between impervious land cover features and those that are similar in tone, such sand on the banks of streams.", "geometryType": "esriGeometryPolygon", "sourceSpatialReference": { "wkid": 102100, "latestWkid": 3857, "xyTolerance": 0.001, "zTolerance": 0.001, "mTolerance": 0.001, "falseX": -20037700, "falseY": -30241100, "xyUnits": 10000, "falseZ": -100000, "zUnits": 10000, "falseM": -100000, "mUnits": 10000 }, "copyrightText": "Applied Geographics, Inc. (AppGeo) along with The University of Vermont Spatial Analysis Lab (UVM SAL) has developed an Object-Based Image Analysis (OBIA) system that mapped impervious surfaces from a combination of remotely-sensed and vector-GIS datasets. The OBIA system was coupled with a detailed manual-corrections process and a thorough accuracy assessment to generate a statewide impervious surface layer for the State of New Jersey.", "parentLayer": null, "subLayers": [], "minScale": 0, "maxScale": 0, "drawingInfo": { "renderer": { "type": "uniqueValue", "field1": "ClassName", "uniqueValueInfos": [ { "symbol": { "type": "esriSFS", "style": "esriSFSSolid", "color": [ 245, 122, 122, 255 ], "outline": { "type": "esriSLS", "style": "esriSLSSolid", "color": [ 110, 110, 110, 255 ], "width": 0.4 } }, "value": "Building", "label": "Building" }, { "symbol": { "type": "esriSFS", "style": "esriSFSSolid", "color": [ 178, 178, 178, 255 ], "outline": { "type": "esriSLS", "style": "esriSLSSolid", "color": [ 110, 110, 110, 255 ], "width": 0.4 } }, "value": "Road", "label": "Road" }, { "symbol": { "type": "esriSFS", "style": "esriSFSSolid", "color": [ 255, 235, 190, 255 ], "outline": { "type": "esriSLS", "style": "esriSLSSolid", "color": [ 110, 110, 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