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Globally, urban areas are rapidly expanding and high-quality remote sensing products are essential to help guide such development towards efficient and sustainable pathways. Here, we present an algorithm to address a common problem in digital aerial photogrammetry (DAP)-based image point clouds: vertical mis-registration. The algorithm uses the ground as inferred from airborne laser scanning (ALS) data as a reference surface and re-aligns individual point clouds to this surface. We demonstrate the effectiveness of the proposed method for the city of Kuopio, in central Finland. Here, we use the standard deviation of the vertical coordinate values as a measure of the mis-registration. We show that such standard deviation decreased substantially (more than 1.0 m) for a large proportion (23.2%) of the study area. Moreover, it was shown that the method performed better in urban and suburban areas, compared to vegetated areas (parks, forested areas, and so on). Hence, we demonstrate that the proposed algorithm is a simple and effective method to improve the quality and usability of DAP-based point clouds in urban areas. [ABSTRACT FROM AUTHOR]

• Mulitemporal urban land cover classification is challenging, but topical. • Image point cloud (IPC) data helps better differentiate between land cover classes. • IPC data can be used in conjunction with Bayesian techniques to yield consistent, multitemporal land cover maps (accuracy observed = 92%, kappa = 0.89) Multitemporal land cover classification over urban areas is challenging, especially when using heterogeneous data sources with variable quality attributes. A prominent challenge is that classes with similar spectral signatures (such as trees and grass) tend to be confused with one another. In this paper, we evaluate the efficacy of image point cloud (IPC) data combined with suitable Bayesian analysis based time-series rectification techniques to improve the classification accuracy in a multitemporal context. The proposed method uses hidden Markov models (HMMs) to rectify land covers that are initially classified by a random forest (RF) algorithm. This land cover classification method is tested using time series of remote sensing data from a heterogeneous and rapidly changing urban landscape (Kuopio city, Finland) observed from 2006 to 2014. The data consisted of aerial images (5 years), Landsat data (all 9 years) and airborne laser scanning data (1 year). The results of the study demonstrate that the addition of three-dimensional image point cloud data derived from aerial stereo images as predictor variables improved overall classification accuracy, around three percentage points. Additionally, HMM-based post processing reduces significantly the number of spurious year-to-year changes. Using a set of 240 validation points, we estimated that this step improved overall classification accuracy by around 3.0 percentage points, and up to 6 to 10 percentage points for some classes. The overall accuracy of the final product was 91% (kappa = 0.88). Our analysis shows that around 1.9% of the area around Kuopio city, representing a total area of approximately 0.61 km2, experienced changes in land cover over the nine years considered. [ABSTRACT FROM AUTHOR]