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Integrating GEOBIA, Machine Learning, and Volunteered Geographic Information to Map Vegetation over Rooftops

Department of Geography, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N1N4, Canada
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(12), 462;
Received: 19 September 2018 / Revised: 13 November 2018 / Accepted: 26 November 2018 / Published: 29 November 2018
(This article belongs to the Special Issue GEOBIA in a Changing World)
PDF [3507 KB, uploaded 3 December 2018]


The objective of this study is to evaluate operational methods for creating a particular type of urban vegetation map—one focused on vegetation over rooftops (VOR), specifically trees that extend over urban residential buildings. A key constraint was the use of passive remote sensing data only. To achieve this, we (1) conduct a review of the urban remote sensing vegetation classification literature, and we then (2) discuss methods to derive a detailed map of VOR for a study area in Calgary, Alberta, Canada from a late season, high-resolution airborne orthomosaic based on an integration of Geographic Object-Based Image Analysis (GEOBIA), pre-classification filtering of image-objects using Volunteered Geographic Information (VGI), and a machine learning classifier. Pre-classification filtering lowered the computational burden of classification by reducing the number of input objects by 14%. Accuracy assessment results show that, despite the presence of senescing vegetation with low vegetation index values and deep shadows, classification using a small number of image-object spectral attributes as classification features (n = 9) had similar overall accuracy (88.5%) to a much more complex classification (91.8%) comprising a comprehensive set of spectral, texture, and spatial attributes as classification features (n = 86). This research provides an example of the very specific questions answerable about precise urban locations using a combination of high-resolution passive imagery and freely available VGI data. It highlights the benefits of pre-classification filtering and the judicious selection of features from image-object attributes to reduce processing load without sacrificing classification accuracy. View Full-Text
Keywords: GEOBIA; vegetation over rooftops; machine learning GEOBIA; vegetation over rooftops; machine learning

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Griffith, D.C.; Hay, G.J. Integrating GEOBIA, Machine Learning, and Volunteered Geographic Information to Map Vegetation over Rooftops. ISPRS Int. J. Geo-Inf. 2018, 7, 462.

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