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Landslide mapping via pixel-wise classification of remote sensing imagery is essential for hazard prevention and risk assessment. Deep-learning-based change detection greatly aids landslide mapping by identifying the down-slope movement of soil, rock and other materials from bitemporal images, benefiting from the feature representation capabilities of convolutional neural networks. However, these networks rely on large amounts of pixel-level annotated data to achieve their promising performance and they normally exhibit weak generalization capability on heterogeneous image data from unseen domains. To address these issues, we propose a prototype-guided domain-aware progressive representation learning (PG-DPRL) method for cross-domain landslide mapping from large-scale remote sensing images based on the multitarget domain adaptation (MTDA) technique. PG-DPRL attempts to learn a shared landslide mapping network that performs well in multiple target domains with no additional effort for sample annotation. Specifically, PG-DPRL adopts a near-to-far adaptation strategy to gradually align the representation distributions of all target domains with the source domain, considering discrepancies between them. On this basis, cross-domain prototype learning is exploited to generate reliable domain-specific pseudo-labels and aggregate representations across domains to learn a shared decision boundary. In each DPRL step, the prototype-guided adversarial learning (PGAL) algorithm is performed to achieve category-wise representation alignment and improve the discriminative capability of representations by introducing the Wasserstein distance metric and cross-domain prototype consistency (CPC) loss. Experiments on a global very-high-resolution landslide mapping (GVLM) dataset consisting of 17 heterogeneous domains from different landslide sites demonstrate the effectiveness and robustness of PG-DPRL. It considerably improves the transferability of landslide mapping networks and outperforms several state-of-the-art approaches in terms of total and average accuracy metrics among all target domains. [ABSTRACT FROM AUTHOR]

Using remote sensing techniques to monitor landslides and their resultant land cover changes is fundamentally important for risk assessment and hazard prevention. Despite enormous efforts in developing intelligent landslide mapping (LM) approaches, LM remains challenging owing to high spectral heterogeneity of very-high-resolution (VHR) images and the daunting labeling efforts. To this end, a deep learning model based on semi-supervised multi-temporal deep representation fusion network, namely SMDRF-Net, is proposed for reliable and efficient LM. In comparison with previous methods, the SMDRF-Net possesses three distinct properties. (1) Unsupervised deep representation learning at the pixel- and object-level is performed by transfer learning using the Wasserstein generative adversarial network with gradient penalty to learn discriminative deep features and retain precise outlines of landslide objects in the high-level feature space. (2) Attention-based adaptive fusion of multi-temporal and multi-level deep representations is developed to exploit the spatio-temporal dependencies of deep representations and enhance the feature representation capability of the network. (3) The network is optimized using limited samples with pseudo-labels that are automatically generated based on a comprehensive uncertainty index. Experimental results from the analysis of VHR aerial orthophotos demonstrate the reliability and robustness of the proposed approach for LM in comparison with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Large-scale high-resolution three-dimensional (3D) maps play a vital role in the development of smart cities. In this work, a novel deep learning-based multi-view-stereo method is proposed for reconstructing the 3D maps in large-scale urban environments by exploiting a monocular camera. Compared with other existing works, the proposed method can perform 3D depth estimation more efficiently in terms of computational complexity and graphics processing unit memory usage. As a result, the proposed method can practically perform depth estimation for each pixel before generating 3D maps for even large-scale scenes. Extensive experiments on the well-known DTU dataset and real-life data collected on our campus confirm the good performance of the proposed method. [ABSTRACT FROM AUTHOR]