Esri (Canada)

About: Esri (Canada) is a company organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Geographic information system & Smart city. The organization has 20 authors who have published 19 publications receiving 140 citations. The organization is also known as: Environmental Systems Research Institute.

Individual Tree Crown Delineation Using Multispectral LiDAR Data.

Abstract: In this study, multispectral Light Detection and Ranging (LiDAR) data were utilized to improve delineation of individual tree crowns (ITC) as an important step in individual tree analysis. A framework to integrate spectral and height information for ITC delineation was proposed, and the multi-scale algorithm for treetop detection developed in one of our previous studies was improved. In addition, an advanced region-based segmentation method that used detected treetops as seeds was proposed for segmentation of individual crowns based on their spectral, contextual, and height information. The proposed methods were validated with data acquired using Teledyne Optech's Titan LiDAR sensor. The sensor was operated at three wavelengths (1550 nm, 1064 nm, and 532 nm) within a study area located in the city of Toronto, ON, Canada. The proposed method achieved 80% accuracy, compared with manual delineation of crowns, considering both matched and partially matched crowns, which was 12% higher than that obtained by the earlier marker-controlled watershed (MCW) segmentation technique. Furthermore, the results showed that the integration of spectral and height information improved ITC delineation using either the proposed framework or MCW segmentation, compared with using either spectral or height information individually.

A crowd sensing system identifying geotopics and community interests from user-generated content

Abstract: This paper presents a crowd sensing system (CSS) that captures geospatial social media topics and allows the review of results. Using Web-resources derived from social media platforms, the CSS uses...

Semi-Automated Building Footprint Extraction From Orthophotos

Abstract: Here we describe and apply a semi-automated, object-based method for extracting vector-building footprint polygons from aerial photographs (orthophotos) within urban settings. The approach integrates the use of high resolution orthophotos and image segmentation software and is compared with methods using Light Detection and Ranging (LiDAR) as the source data input. LiDAR data gives the best results with less processing, but is not widely used by municipalities due to the expense. Results from semi-automated image segmentation of the orthophotos showed a high accuracy between extracted building segments and reference building footprints for two study sites, comparable to those achieved using LiDAR data. We recommend image acquisition during summer months with a resolution of 10 cm by 10 cm. When data acquisition budgets are limited, combining ancillary GIS on roads with a semi-automated and object-based segmentation approach is a best practice strategy for land cover feature extraction and change quantific...

A decision-level fusion approach to tree species classification from multi-source remotely sensed data

Abstract: In this study, an object-oriented, decision-level fusion method is proposed for tree species classification based on spectral, textural, and structural features derived from multi-spectral and panchromatic imagery and Light Detection And Ranging (LiDAR) data. Murphy's average method based on the Dempster Shafer theory (DST) was used to calculate the combined mass function for decision making purposes. For individual feature groups, the mass functions were calculated using the support vector machine (SVM) classification method. The species examined included Norway maple, honey locust, Austrian pine, blue spruce, and white spruce. In addition to these species, a two- or three-species compound class was included in the decision process based on the normalized entropy in the presence of conflict that was itself determined according to whether individual groups of features were consistent. The developed method provided a mechanism to identify tree crowns, which could not be classified to one single species with a high confidence due to the conflict among feature groups. Data used in this study were obtained for the Keele Campus of York University, Toronto, Ontario. Among the 223 test crowns, 204 crowns were assigned to one single species, and the overall classification accuracy was 0.89. A decision could not be made for 19 crowns with confidence, and as a result, a two- or three-species compound class was assigned. The classification accuracy was higher than that obtained using SVM classification based on individual and combined spectral, structural, and textural features.