We analysed the spatial distribution of nitrogen dioxide over Calgary (Canada) in summer 2010 and winter 2011 and in summer 2015 and winter 2016, and estimated land use regressions for 2015–16 (2010–11 models were estimated previously). As nitrogen dioxide exhibited spatial clustering, we evaluated the following spatial specifications against a linear model: spatially autoregressive (lag), spatially autoregressive (error), and geographically weighted regression. The spatially autoregressive (lag) specification performed best, achieving goodness-of-fit aligned with or greater than values reported in the literature. We compared the 2015–16 spatially autoregressive models with the 2010–11 models and reparametrized them on the 2010–11 and the 2015–16 data. Finally, we identified a single set of predictors to best fit the data. Nitrogen dioxide concentration decreased over the 5 years, retaining consistent spatial and seasonal patterns, with higher concentrations over traffic corridors and industrial areas, and greater variation in summer than winter. The multi-temporal analysis suggested that spatial land use regressions were robust over the time interval, despite moderate land use change. Multi-temporal spatial land use regressions yielded consistent predictors for each season over time, which can aid estimation of air pollution at fine spatial resolution over an extended time period.

As part of the Heat Energy Assessment Technologies (HEAT) project, we describe a novel geographic object-based mosaicing algorithm referred to as Object-Based Mosaicing (OBM) that joins thermal airborne flight lines around urban roof objects rather than bisecting them with arbitrary mosaic join lines. An OBM mosaic is compared with a “traditional” mosaic product (created in ENVI 4.8) consisting of 44 TABI-1800 flight lines of the City of Calgary, Alberta, Canada (825 km2). Compared with the traditional mosaic, OBM results in the following: 1) visually improved roof shapes within the scene; 2) reduced processing time (up to 50 %); 3) more accurate hot-spot detection; and 4) a better data set for more accurate home energy models-as the thermal imagery for each roof are from a single acquisition time. Conversely, without applying OBM to the full scene, 14 209 homes are bisected within the traditional mosaic product.

Centerline detection and line width estimation are important for many computer vision applications, e.g., road network extraction from high resolution remotely sensed imagery. Radon transform-based linear feature detection has many advantages over other approaches: for example, its robustness in noisy images. However, it usually fails to detect the centerline of a thick line due to the peak selection problem. In this paper, several key issues that affect the centerline detection using the radon transform are investigated. A mean filter is proposed to locate the true peak in the radon image and a profile analysis technique is used to further refine the line parameters. The theta-boundary problem of the radon transform is also discussed and the erroneous line parameters are corrected. Intensive experiments have shown that the proposed methodology is effective in finding the centerline and estimating the line width of thick lines.