Abstract: A non-transitory processor-readable medium includes code to cause a processor to receive aerial data having a plurality of points arranged in a pattern. An indication associated with each point is provided as an input to a machine learning model to classify each point into a category from a plurality of categories. For each point, a set of points (1) adjacent to that point and (2) having a common category is identified to define a shape from a plurality of shapes. A polyline boundary of each shape is defined by analyzing with respect to a criterion, a position of each point associated with a border of that shape relative to at least one other point. A layer for each category including each shape associated with that category is defined and a computer-aided design file is generated using the polyline boundary of each shape and the layer for each category.

Abstract: A non-transitory processor-readable medium includes code to cause a processor to receive aerial data having a plurality of points arranged in a pattern. An indication associated with each point is provided as an input to a machine learning model to classify each point into a category from a plurality of categories. For each point, a set of points (1) adjacent to that point and (2) having a common category is identified to define a shape from a plurality of shapes. A polyline boundary of each shape is defined by analyzing with respect to a criterion, a position of each point associated with a border of that shape relative to at least one other point. A layer for each category including each shape associated with that category is defined and a computer-aided design file is generated using the polyline boundary of each shape and the layer for each category.

Abstract: A non-transitory processor-readable medium includes code to cause a processor to receive aerial data having a plurality of points arranged in a pattern. An indication associated with each point is provided as an input to a machine learning model to classify each point into a category from a plurality of categories. For each point, a set of points (1) adjacent to that point and (2) having a common category is identified to define a shape from a plurality of shapes. A polyline boundary of each shape is defined by analyzing with respect to a criterion, a position of each point associated with a border of that shape relative to at least one other point. A layer for each category including each shape associated with that category is defined and a computer-aided design file is generated using the polyline boundary of each shape and the layer for each category.

Abstract: A non-transitory processor-readable medium includes code to cause a processor to receive aerial data having a plurality of points arranged in a pattern. An indication associated with each point is provided as an input to a machine learning model to classify each point into a category from a plurality of categories. For each point, a set of points (1) adjacent to that point and (2) having a common category is identified to define a shape from a plurality of shapes. A polyline boundary of each shape is defined by analyzing with respect to a criterion, a position of each point associated with a border of that shape relative to at least one other point. A layer for each category including each shape associated with that category is defined and a computer-aided design file is generated using the polyline boundary of each shape and the layer for each category.