This article was submitted to IEEE Geoscience and Remote Sensing Magazine.Access to labeled reference data is one of the grand challenges in supervised machine learning endeavors. This is especially true for an automated analysis of remote sensing images on a global scale, which enables us to address global challenges such as urbanization and climate change using state-of-theart machine learning techniques. To meet these pressing needs, especially in urban research, we provide open access to a valuable benchmark dataset named "So2Sat LCZ42," which consists of local climate zone (LCZ) labels of about half a million Sentinel-1 and Sentinel-2 image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe. This dataset was labeled by 15 domain experts following a carefully designed labeling work flow and evaluation process over a period of six months. As rarely done in other labeled remote sensing dataset, we conducted rigorous quality assessment by domain experts. The dataset achieved an overall confidence of 85%. We believe this LCZ dataset is a first step towards an unbiased globallydistributed dataset for urban growth monitoring using machine learning methods, because LCZ provide a rather objective measure other than many other semantic land use and land cover classifications. It provides measures of the morphology, compactness, and height of urban areas, which are less dependent on human and culture. This dataset can be accessed from

This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE XPlore.Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data. Although deep learning has been introduced in Synthetic Aperture Radar (SAR) data processing, despite successful first attempts, its huge potential remains locked. In this paper, we provide an introduction to the most relevant deep learning models and concepts, point out possible pitfalls by analyzing special characteristics of SAR data, review the state-of-the-art of deep learning applied to SAR in depth, summarize available benchmarks, and recommend some important future research directions. With this effort, we hope to stimulate more research in this interesting yet under-exploited research field and to pave the way for use of deep learning in big SAR data processing workflows.

Automatic building extraction from optical imagery remains a challenge due to, for example, the complexity of building shapes. Semantic segmentation is an efficient approach for this task. The latest development in deep convolutional neural networks (DCNNs) has made accurate pixel-level classification tasks possible. Yet one central issue remains: the precise delineation of boundaries. Deep architectures generally fail to produce finegrained segmentation with accurate boundaries due to their progressive down-sampling. Hence, we introduce a generic framework to overcome the issue, integrating the graph convolutional network (GCN) and deep structured feature embedding (DSFE) into an end-to-end workflow. Furthermore, instead of using a classic graph convolutional neural network, we propose a gated graph convolutional network, which enables the refinement of weak and coarse semantic predictions to generate sharp borders and fine-grained pixel-level classification. Taking the semantic segmentation of building footprints as a practical example, we compared different feature embedding architectures and graph neural networks. Our proposed framework with the new GCN architecture outperforms state-of-the-art approaches. Although our main task in this work is building footprint extraction, the proposed method can be generally applied to other binary or multi-label segmentation tasks.

The well-known polar R3MTQ7 is a large family of noncentrosymmetric chalcogenides, and despite of adopting the same crystal structure type, its members show distinctively different nonlinear optical (NLO) properties, which is quite unusual. Yet, the intrinsic reason remains unknown. Herein, we report the discovery of six new members, La3Ga0.5(Ge0.5/Ga0.5)­S7 (1), La3In0.5(Ge0.5/In0.5)­S7 (2), Sm3Ga0.5(Ge0.5/Ga0.5)­S7 (3), La3In0.33GeS7 (4), Sm3In0.33GeS7 (5), and Gd3In0.33GeS7 (6). Remarkably, polycrystalline 1 and 2 show the strongest second harmonic generation (SHG) of this family, 4.8 and 1.8 times that of the benchmark AgGaS2 at 2.05 μm in the same particle size of 74–106 μm. For the first time we reveal that for the R3MTQ7 family the atomic distribution mainly determines the NLO property, and members showing strong SHG must have a formula of R3M0.5TQ7. Furthermore, we illustrate whether the building unit MS6 octahedron is half occupied (1–3) or one-third occupied (4–6) is total energy driven and charge balance controlled.

Tomographic synthetic aperture radar (TomoSAR) inversion of urban areas is an inherently sparse reconstruction problem and, hence, can be solved using compressive sensing (CS) algorithms. This paper proposes solutions for two notorious problems in this field. First, TomoSAR requires a high number of data sets, which makes the technique expensive. However, it can be shown that the number of acquisitions and the signal-to-noise ratio (SNR) can be traded off against each other, because it is asymptotically only the product of the number of acquisitions and SNR that determines the reconstruction quality. We propose to increase SNR by integrating nonlocal (NL) estimation into the inversion and show that a reasonable reconstruction of buildings from only seven interferograms is feasible. Second, CS-based inversion is computationally expensive and therefore, barely suitable for large-scale applications. We introduce a new fast and accurate algorithm for solving the NL L1-L2-minimization problem, central to CS-based reconstruction algorithms. The applicability of the algorithm is demonstrated using simulated data and TerraSAR-X high-resolution spotlight images over an area in Munich, Germany.

Building footprint maps are vital to many remote sensing (RS) applications, such as 3-D building modeling, urban planning, and disaster management. Due to the complexity of buildings, the accurate and reliable generation of the building footprint from RS imagery is still a challenging task. In this article, an end-to-end building footprint generation approach that integrates convolution neural network (CNN) and graph model is proposed. CNN serves as the feature extractor, while the graph model can take spatial correlation into consideration. Moreover, we propose to implement the feature pairwise conditional random field (FPCRF) as a graph model to preserve sharp boundaries and fine-grained segmentation. Experiments are conducted on four different data sets: 1) Planetscope satellite imagery of the cities of Munich, Paris, Rome, and Zurich; 2) ISPRS Benchmark data from the city of Potsdam; 3) Dstl Kaggle data set; and 4) Inria Aerial Image Labeling data of Austin, Chicago, Kitsap County, Western Tyrol, and Vienna. It is found that the proposed end-toend building footprint generation framework with the FPCRF as the graph model can further improve the accuracy of building footprint generation by using only CNN, which is the current state of the art.

This is a preprint. To read the final version please visit IEEE XPlore.With the objective of exploiting hardware capabilities and preparing the ground for the next-generation X-band synthetic aperture radar (SAR) missions, TerraSAR-X and TanDEM-X are now able to operate in staring spotlight mode, which is characterized by an increased azimuth resolution of approximately 0.24 m compared to 1.1 m of the conventional sliding spotlight mode. In this paper, we demonstrate for the first time its potential for SAR tomography. To this end, we tailored our interferometric and tomographic processors for the distinctive features of the staring spotlight mode, which will be analyzed accordingly. By means of its higher spatial resolution, the staring spotlight mode will not only lead to a denser point cloud, but also to more accurate height estimates due to the higher signal-to-clutter ratio. As a result of a first comparison between sliding and staring spotlight TomoSAR, the following were observed: 1) the density of the staring spotlight point cloud is approximately 5.1-5.5 times as high; 2) the relative height accuracy of the staring spotlight point cloud is approximately 1.7 times as high.

Orthorhombic Ag8SnS6 submicropyramids with high surface energy have been synthesized by the solventless method. The four outer surfaces are (4̅11), (112), (211̅) and (41̅3) lattice planes according to scanning electron microscopy, transmission electron microscopy, high resolution TEM, fast Fourier transform analyses, and angle measurements. Such morphology agrees well with the crystallographic symmetry requirement of the space group Pna21 of orthorhombic Ag8SnS6. Effects of polar washing solvent, reaction temperature, and time that influence the morphology are systematically investigated. Interestingly, the as-synthesized Ag8SnS6 submicropyramids exhibit superior photocatalytic activity to commercial P25 TiO2 under visible light, which may owe to the high surface energy.