Rapid urbanization and economic development have led to the development of heavy industry and structural re-equalization in mainland China. This has resulted in scattered and disorderly layouts becoming prominent in the region. Furthermore, economic development has exacerbated pressures on regional resources and the environment and has threatened sustainable and coordinated development in the region. The NASA Land Science Investigator Processing System (Land-SIPS) Visible Infrared Imaging Radiometer (VIIRS) 375-m active fire product (VNP14IMG) was selected from the Fire Information for Resource Management System (FIRMS) to study the spatiotemporal patterns of heavy industry development. Furthermore, we employed an improved adaptive K-means algorithm to realize the spatial segmentation of long-order VNP14IMG and constructed heat source objects. Lastly, we used a threshold recognition model to identify heavy industry objects from normal heat source objects. Results suggest that the method is an accurate and effective way to monitor heat sources generated from heavy industry. Moreover, some conclusions about heavy industrial heat source distribution in mainland China at different scales were obtained. Those can be beneficial for policy-makers and heavy industry regulation.

Room-temperature observation for reverse intersystem crossing (RISC) from triplet to singlet charge-transfer states (CT 3 → CT 1 ) and clarification of its physical mechanisms are the key requirements for designing highly efficient exciplex-based organic light-emitting diodes (OLEDs). Herein, balanced and unbalanced exciplex-based OLEDs were fabricated by employing different holeinjection layers, and RISC of CT states was directly observed via analyzing magnetoconductance (MC) and magneto-electroluminescence (MEL) traces of the balanced device at room temperature. Specifically, current-dependent MC traces of the balanced device always present B-mediated RISC features, whereas those from the unbalanced one depict the superposition of B-mediated intersystem crossing (ISC) and the dissociation of CT 3 by excessive charge carriers. Simultaneously, MEL curves of the balanced device display the conversion from ISC to RISC with lowering bias current, but those from the unbalanced one always show ISC under all of bias currents. Moreover, although all of current-dependent magneto-efficiency (Mη) traces exhibit ISC, Mη values are ∼2 times lower in the balanced device than the unbalanced one. These rich changes of magnetic-field responses demonstrate that balanced carrier injection can facilitate the occurrence of RISC by reducing the dissociation of CT 3 . Expectedly, the current efficiency of electroluminescence from the balanced device is increased by ∼2.2 times, which originates from the improvement of delayed luminescence because of the enhanced RISC. Accordingly, this work not only clarifies the prerequisite for observing RISC of CT states but also provides strategies for designing high-efficiency exciplex-based OLEDs.

A high‐level reverse intersystem crossing (HL‐RISC, T2 → S1 → S0 + hν) process has recently been discovered as a promising route for achieving highly efficient organic light‐emitting diodes (OLEDs), but the prerequisites for the occurrence of HL‐RISC in rubrene is still vague and the reported external quantum efficiencies (EQEs) of rubrene‐doped OLEDs are typically limited to several percent. Herein, using the fingerprint magneto‐electroluminescence tools, it is found that the energy confinement of high‐lying triplet states (T2, rub) is of great importance for the achievement of the HL‐RISC process. Namely, when the triplet energies of hosts satisfy the criterion of E(T1, host) ≥ E(T2, rub), the high‐level Dexter energy transfer channel (T1, host → T2, rub) can facilitate the occurrence of HL‐RISC (T2, rub → S1, rub) in rubrene. Most importantly, through selecting an exciplex with a high triplet energy as the co‐host for rubrene dopant so as to simultaneously utilize the HL‐RISC of the dopant and the RISC of the host, a record high EQE up to 16.1% is achieved and no obvious efficiency roll‐off is observed at high luminance due to the absence of triplet‐charge annihilation. Accordingly, this work not only deepens the physical understanding of this amazing HL‐RISC channel, but also provides a new direction for designing a series of highly efficient OLEDs.

Abstract:Remote sensing (RS) images play a significant role in disaster emergency response. Web2.0 changes the way data are created, making it possible for the public to participate in scientific issues. In this paper, an experiment is designed to evaluate the reliability of crowdsourcing buildings collapse assessment in the early time after an earthquake based on aerial remote sensing image. The procedure of RS data pre-processing and crowdsourcing data collection is presented. A probabilistic model including maximum likelihood estimation (MLE), Bayes' theorem and expectation-maximization (EM) algorithm are applied to quantitatively estimate the individual error-rate and "ground truth" according to multiple participants' assessment results. An experimental area of Yushu earthquake is provided to present the results contributed by participants. Following the results, some discussion is provided regarding accuracy and variation among participants. The features of buildings labeled as the same damage type are found highly consistent. This suggests that the building damage assessment contributed by crowdsourcing can be treated as reliable samples. This study shows potential for a rapid building collapse assessment through crowdsourcing and quantitatively inferring "ground truth" according to crowdsourcing data in the early time after the earthquake based on aerial remote sensing image.

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Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN) model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs) is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remote sensing imagery classification. accuracy in certain situations. Considering the complexity of land cover that contains vegetation, water, soil and other physical land features, including those created solely by human activities, it is still challenging for object-oriented approaches to improve classification accuracy.Traditionally, classification of remote sensing imagery focused on two steps, i.e., feature extraction methods and supervised learning algorithms. Various methods have been proposed to extract features using hand-crafted descriptors, such as Scale-Invariant Feature Transform (SIFT) [11] and Histogram of Oriented Gradients (HOG) [12]. However, the two steps mentioned above are typically viewed as distinct approaches. Convolutional neural networks (CNNs) combine the two steps into one network that learns contextual features at different scales and computes the score of each class at the end of the network. Another benefit of CNNs is that they can be trained to optimize all weights in the network using an efficient weight update technique such as stochastic gradient descent (SGD) [13] end-to-end. Although CNNs have achieved remarkable results in image categorization, they do not consider pixel-wise semantic classification. In image categorization, the input of a network is an image, while the goal is to predict the correct category of the entire image. FCN [14]-based models have been successfully applied to natural image semantic segmentation and are currently the state-of-the-art for PASCAL VOC datasets [15]. However, FCN models do not take into account relations between pixels and ignore the spatial regularity in the usual pixel-wise classification method. In addition, the vast majority of research focuses on optimization of the network structure without considering efficient use of labeled data that is very important to deep neural networks.To overcome the above challenges, in this paper, we introduce a novel pipeline for pixel-wise classification of high-resolution remote sensing imagery. Our method consists of three parts: ...

Current-dependent MEL and MC experimental curves of Dev. 1 at room temperature and 100 K and at 100 μA; temperature-dependent MEL curves of device 3 at 100 μA; rubrene and DCJTB concentration-dependent MEL experimental curves of Dev. 1; and schematic diagram of spin-pair states in Dev. 1 with a high DCJTB doping concentration (PDF)■ AUTHOR INFORMATION

Class imbalance is a key issue for the application of deep learning for remote sensing image classification because a model generated by imbalanced samples training has low classification accuracy for minority classes. In this study, an accurate classification approach using the multistage sampling method and deep neural networks was proposed to classify imbalanced data. We first balance samples by multistage sampling to obtain the training sets. Then, a state-of-the-art model is adopted by combining the advantages of atrous spatial pyramid pooling (ASPP) and Encoder-Decoder for pixel-wise classification, which are two different types of fully convolutional networks (FCNs) that can obtain contextual information of multiple levels in the Encoder stage. The details and spatial dimensions of targets are restored using such information during the Decoder stage. We employ four deep learning-based classification algorithms (basic FCN, FCN-8S, ASPP, and Encoder-Decoder with ASPP of our approach) on multistage training sets (original, MUS1, and MUS2) of WorldView-3 images in southeastern Qinghai-Tibet Plateau and GF-2 images in northeastern Beijing for comparison. The experiments show that, compared with existing sets (original, MUS1, and identical) and existing method (cost weighting), the MUS2 training set of multistage sampling significantly enhance the classification performance for minority classes. Our approach shows distinct advantages for imbalanced data.