Identifying parking regions in aerial image
Method identifying vehicle parking regions from aerial images, comprising: i. retrieving a first aerial image 500' from a database; ii. extracting features from the first image 502-512; iii. detecting the presence of a car park in the first image based on the features; iv. classifying the type...
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creator | Sai Supriya Vadla Saranya Kanagaraj Harishankar Chinnathambi Sudhir Behani Marius Schneider Benedikt Bergander |
description | Method identifying vehicle parking regions from aerial images, comprising: i. retrieving a first aerial image 500' from a database; ii. extracting features from the first image 502-512; iii. detecting the presence of a car park in the first image based on the features; iv. classifying the type of parking region based on the features; v. if no vehicle parking is detected, repeating steps i-iv with a second aerial image obtained at a different time from the first aerial image; and vi. storing the classified parking region in a database comprising parking regions on Earth. The features may comprise: rows of vehicles 502; bay markings; consistent gaps between rows of vehicles or bay markings 504; roads adjacent to the row(s) of vehicles or bays 512; surrounding area characteristics 510; ground surface 506; and/or vehicle size and shape 508. Aerial image may be satellite images. Feature detection may comprise deep learning. The type of parking may be: off-street parking; on-street parking; a parking on a building; light vehicle parking; and/or a heavy vehicle parking. The different time may be selected may be a different: day; time in a day; and day with different weather conditions. |
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The features may comprise: rows of vehicles 502; bay markings; consistent gaps between rows of vehicles or bay markings 504; roads adjacent to the row(s) of vehicles or bays 512; surrounding area characteristics 510; ground surface 506; and/or vehicle size and shape 508. Aerial image may be satellite images. Feature detection may comprise deep learning. The type of parking may be: off-street parking; on-street parking; a parking on a building; light vehicle parking; and/or a heavy vehicle parking. 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The features may comprise: rows of vehicles 502; bay markings; consistent gaps between rows of vehicles or bay markings 504; roads adjacent to the row(s) of vehicles or bays 512; surrounding area characteristics 510; ground surface 506; and/or vehicle size and shape 508. Aerial image may be satellite images. Feature detection may comprise deep learning. The type of parking may be: off-street parking; on-street parking; a parking on a building; light vehicle parking; and/or a heavy vehicle parking. 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The features may comprise: rows of vehicles 502; bay markings; consistent gaps between rows of vehicles or bay markings 504; roads adjacent to the row(s) of vehicles or bays 512; surrounding area characteristics 510; ground surface 506; and/or vehicle size and shape 508. Aerial image may be satellite images. Feature detection may comprise deep learning. The type of parking may be: off-street parking; on-street parking; a parking on a building; light vehicle parking; and/or a heavy vehicle parking. The different time may be selected may be a different: day; time in a day; and day with different weather conditions.</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTING COUNTING PHYSICS |
title | Identifying parking regions in aerial image |
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