Urban land use information is essential for a variety of urban-related applications such as urban planning and regional administration. The extraction of urban land use from very fine spatial resolution (VFSR) remotely sensed imagery has, therefore, drawn much attention in the remote sensing community. Nevertheless, classifying urban land use from VFSR images remains a challenging task, due to the extreme difficulties in differentiating complex spatial patterns to derive high-level semantic labels. Deep convolutional neural networks (CNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction. However, blurred object boundaries and geometric distortion, as well as huge computational redundancy, severely restrict the potential application of CNN for the classification of urban land use. In this paper, a novel object-based convolutional neural network (OCNN) is proposed for urban land use classification using VFSR images. Rather than pixel-wise convolutional processes, the OCNN relies on segmented objects as its functional units, and CNN networks are used to analyse and label objects such as to partition within-object and between-object variation. Two CNN networks with different model structures and window sizes are developed to predict linearly shaped objects (e.g. Highway, Canal) and general (other non-linearly shaped) objects. Then a rule-based decision fusion is performed to integrate the class-specific classification results. The effectiveness of the proposed OCNN method was tested on aerial photography of two large urban scenes in Southampton and Manchester in Great Britain. The OCNN combined with large and small window sizes achieved excellent classification accuracy and computational efficiency, consistently outperforming its sub-modules, as well as other benchmark comparators, including the pixel-wise CNN, contextual-based MRF and object-based OBIA-SVM methods. The proposed method provides the first object-based CNN framework to effectively and efficiently address the complicated problem of urban land use classification from VFSR images.

Land cover (LC) and land use (LU) have commonly been classified separately from remotely sensed imagery, without considering the intrinsically hierarchical and nested relationships between them. In this paper, for the first time, a highly novel joint deep learning framework is proposed and demonstrated for LC and LU classification. The proposed Joint Deep Learning (JDL) model incorporates a multilayer perceptron (MLP) and convolutional neural network (CNN), and is implemented via a Markov process involving iterative updating. In the JDL, LU classification conducted by the CNN is made conditional upon the LC probabilities predicted by the MLP. In turn, those LU probabilities together with the original imagery are re-used as inputs to the MLP to strengthen the spatial and spectral feature representations. This process of updating the MLP and CNN forms a joint distribution, where both LC and LU are classified simultaneously through iteration. The proposed JDL method provides a general framework within which the pixel-based MLP and the patch-based CNN provide mutually complementary information to each other, such that both are refined in the classification process through iteration. Given the well-known complexities associated with the classification of very fine spatial resolution (VFSR) imagery, the effectiveness of the proposed JDL was tested on aerial photography of two large urban and suburban areas in Great Britain (Southampton and Manchester). The JDL consistently demonstrated greatly increased accuracies with increasing iteration, not only for the LU classification, but for both 2 the LC and LU classifications, achieving by far the greatest accuracies for each at around 10 iterations. The average overall classification accuracies were 90.18% for LC and 87.92% for LU for the two study sites, far higher than the initial accuracies and consistently outperforming benchmark comparators (three each for LC and LU classification). This research, thus, represents the first attempt to unify the remote sensing classification of LC (state; what is there?) and LU (function; what is going on there?), where previously each had been considered separately only. It, thus, has the potential to transform the way that LC and LU classification is undertaken in future. Moreover, it paves the way to address effectively the complex tasks of classifying LC and LU from VFSR remotely sensed imagery via joint reinforcement, and in an automatic manner.

The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and texturalbased MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

Three‐dimensional building models are often now produced from lidar and photogrammetric data. The quality control of these models is a relevant issue both from the scientific and practical points of view. This work presents a method for the quality control of such models. The input model (3D building data) is co‐registered to the verification data using a 3D surface matching method. The 3D surface matching evaluates the Euclidean distances between the verification and input data‐sets. The Euclidean distances give appropriate metrics for the 3D model quality. This metric is independent of the method of data capture. The proposed method can favourably address the reference system accuracy, positional accuracy and completeness. Three practical examples of the method are provided for demonstration.

Choosing appropriate scales for remotely sensed image classification is extremely important yet still an open question in relation to deep convolutional neural networks (CNN), due to the impact of spatial scale (i.e., input patch size) on the recognition of ground objects. Currently, the optimal scale selection processes are extremely cumbersome and timeconsuming requiring repetitive experiments involving trial-and-error procedures, which significantly reduces the practical utility of the corresponding classification methods. This issue is crucial when trying to classify large-scale land use (LU) and land cover (LC) jointly (Zhang et al., 2019). In this paper, a simple and parsimonious scale sequence joint deep learning (SS-JDL) method is proposed for joint LU and LC classification, in which a sequence of scales is embedded in the iterative process of fitting the joint distribution implicit in the joint deep learning (JDL) method, thus, replacing the previous paradigm of scale selection. The sequence of scales, derived autonomously and used to define the CNN input patch sizes, provides consecutive information transmission from small-scale features to large-scale representations, and from simple LC states to complex LU characterisations. The effectiveness 2 of the novel SS-JDL method was tested on aerial digital photography of three complex and heterogeneous landscapes, two in Southern England (Bournemouth and Southampton) and one in North West England (Manchester). Benchmark comparisons were provided in the form of a range of LU and LC methods, including the state-of-the-art joint deep learning (JDL) method. The experimental results demonstrated that the SS-JDL consistently outperformed all of the state-of-the-art baselines in terms of both LU and LC classification accuracies, as well as computational efficiency. The proposed SS-JDL method, therefore, represents a fast and effective implementation of the state-of-the-art JDL method. By creating a single, unifying joint distribution framework for classifying higher order feature representations, including LU, the SS-JDL method has the potential to transform the classification paradigm in remote sensing, and in machine learning more generally.