Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery

Building 3D reconstruction from remote sensing images has a wide range of applications in smart cities, photogrammetry and other fields. Methods for automatic 3D urban building modeling typically employ multi-view images as input to algorithms to recover point clouds and 3D models of buildings. However, such models rely heavily on multi-view images of buildings, which are time-intensive and limit the applicability and practicality of the models. To solve these issues, we focus on designing an efficient DSM estimation-driven reconstruction framework (Building3D), which aims to reconstruct 3D building models from the input single-view remote sensing image. First, we propose a Semantic Flow Field-guided DSM Estimation (SFFDE) network, which utilizes the proposed concept of elevation semantic flow to achieve the registration of local and global features. Specifically, in order to make the network semantics globally aware, we propose an Elevation Semantic Globalization (ESG) module to realize the semantic globalization of instances. Further, in order to alleviate the semantic span of global features and original local features, we propose a Local-to-Global Elevation Semantic Registration (L2G-ESR) module based on elevation semantic flow. Our Building3D is rooted in the SFFDE network for building elevation prediction, synchronized with a building extraction network for building masks, and then sequentially performs point cloud reconstruction, surface reconstruction (or CityGML model reconstruction). On this basis, our Building3D can optionally generate CityGML models or surface mesh models of the buildings. Extensive experiments on ISPRS Vaihingen and DFC2019 datasets on the DSM estimation task show that our SFFDE significantly improves upon state-of-the-arts. Furthermore, our Building3D achieves impressive results in the 3D point cloud and 3D model reconstruction process

RingMo-lite: A Remote Sensing Multi-task Lightweight Network with CNN-Transformer Hybrid Framework

In recent years, remote sensing (RS) vision foundation models such as RingMo have emerged and achieved excellent performance in various downstream tasks. However, the high demand for computing resources limits the application of these models on edge devices. It is necessary to design a more lightweight foundation model to support on-orbit RS image interpretation. Existing methods face challenges in achieving lightweight solutions while retaining generalization in RS image interpretation. This is due to the complex high and low-frequency spectral components in RS images, which make traditional single CNN or Vision Transformer methods unsuitable for the task. Therefore, this paper proposes RingMo-lite, an RS multi-task lightweight network with a CNN-Transformer hybrid framework, which effectively exploits the frequency-domain properties of RS to optimize the interpretation process. It is combined by the Transformer module as a low-pass filter to extract global features of RS images through a dual-branch structure, and the CNN module as a stacked high-pass filter to extract fine-grained details effectively. Furthermore, in the pretraining stage, the designed frequency-domain masked image modeling (FD-MIM) combines each image patch's high-frequency and low-frequency characteristics, effectively capturing the latent feature representation in RS data. As shown in Fig. 1, compared with RingMo, the proposed RingMo-lite reduces the parameters over 60% in various RS image interpretation tasks, the average accuracy drops by less than 2% in most of the scenes and achieves SOTA performance compared to models of the similar size. In addition, our work will be integrated into the MindSpore computing platform in the near future

Inefficient Investment and Corporate Sustainable Growth

The inefficient investment behavior exists widely in enterprises, and few literatures expand its economic consequences to the perspective of sustainable growth of enterprises. This paper selects eligible Chinese A-share private listed companies in Shanghai and Shenzhen exchanges from 2014 to 2018 as the sample to explore whether the inefficient investment has an impact on the firm sustainable growth ability, and whether the inefficient investment under different conditions have different impacts on the sustainable growth ability of enterprises. The empirical results show that the inefficient investment behaviors of private enterprises reduce the sustainable growth ability of enterprises. Whether it is the over-investment or the under-investment, it may inhibit the corporate sustainable growth. The under-investment has more effect on the corporate sustainable growth than the over-investment. The number of under-investment enterprises is more than over-investment enterprises. According to the different formation mechanisms of under-investment, the under-investment on the condition of sufficient funds has a negative impact on the sustainable growth of the enterprise, which is the performance of ultra-conservation. The under-investment on the condition of insufficient funds has also a significantly negative impact on the corporate sustainable growth to avoid risks. Our evidences are consistent with the hypotheses and have important policy implications. Keywords: Inefficient investment, Over-investment, Under-investment, Corporate sustainable growth DOI: 10.7176/EJBM/13-24-02 Publication date: December 31st 202

Vehicle Re-Identification in Aerial Imagery Based on Normalized Virtual Softmax Loss

With the development and popularization of unmanned aerial vehicles (UAVs) and surveillance cameras, vehicle re-identification (ReID) task plays an important role in the field of urban safety. The biggest challenge in the field of vehicle ReID is how to robustly learn the common visual representation of vehicle from different viewpoints while discriminate different vehicles with similar visual appearance. In order to solve this problem, this paper designs the normalized virtual softmax loss to enlarge the inter-class distance and decrease the intra-class distance, and a vehicle ReID model is proposed by jointly training the network with the proposed loss and triplet loss. In addition, we contribute a novel UAV vehicle ReID dataset from multiple viewpoint images to verify the robustness of methods. The experimental results show that comparing with the other softmax-based losses, our method achieves better performance and gets 76.70% and 98.95% in Rank-1 on VRAI and VRAI_AIR dataset, respectively

Diagenesis of deep sandstone reservoirs and a quantitative model of porosity evolution: Taking the third member of Shahejie Formation in the Wendong Oilfield, Dongpu Sag, as an example

The diagenesis and porosity evolution in the deep 3rd member of Shahejie Formation of the Wendong Oilfield was analyzed using thin-sections, casting thin-sections, X-ray diffractometry, scanning electron micrograph observations, and other data. Sandstone reservoirs are currently at the late diagenetic period. Pores consist of primary pores and the inter-granular dissolved and intra-granular dissolved pores of feldspar, debris and carbonate cements. Physical properties are mainly controlled by carbonate cementation and dissolution, and distribution of abnormally high fluid pressure. The evolution of porosity parameters shows that primary porosity is 36.75%, the porosity loss rate is 40.49% during the process of mechanic compaction, the porosity loss rate is 37.25% during the process of cementation and metasomasis, and the porosity increase rate is 17.88% during the process of dissolution. The proportion of primary porosity is 55.03%, and that of the secondary porosity is 44.97%. The error rate in the quantitative study of porosity is 0.96%, and the main influencing factor of the error rate is sorting coefficient of detrital rock (S0). Key words: deep reservoir, Dongpu Sag, Wendong Oilfield, porosity evolution model, secondary por

SR-Net: Saliency Region Representation Network for Vehicle Detection in Remote Sensing Images

Vehicle detection in remote sensing imagery is a challenging task because of its inherent attributes, e.g., dense parking, small sizes, various angles, etc. Prevalent vehicle detectors adopt an oriented/rotated bounding box as a basic representation, which needs to apply a distance regression of height, width, and angles of objects. These distance-regression-based detectors suffer from two challenges: (1) the periodicity of the angle causes a discontinuity of regression values, and (2) small regression deviations may also cause objects to be missed. To this end, in this paper, we propose a new vehicle modeling strategy, i.e., regarding each vehicle-rotated bounding box as a saliency area. Based on the new representation, we propose SR-Net (saliency region representation network), which transforms the vehicle detection task into a saliency object detection task. The proposed SR-Net, running in a distance (e.g., height, width, and angle)-regression-free way, can generate more accurate detection results. Experiments show that SR-Net outperforms prevalent detectors on multiple benchmark datasets. Specifically, our model yields 52.30%, 62.44%, 68.25%, and 55.81% in terms of AP on DOTA, UCAS-AOD, DLR 3K Munich, and VEDAI, respectively

AF-EMS Detector: Improve the Multi-Scale Detection Performance of the Anchor-Free Detector

As a precursor step for computer vision algorithms, object detection plays an important role in various practical application scenarios. With the objects to be detected becoming more complex, the problem of multi-scale object detection has attracted more and more attention, especially in the field of remote sensing detection. Early convolutional neural network detection algorithms are mostly based on artificially preset anchor-boxes to divide different regions in the image, and then obtain the prior position of the target. However, the anchor box is difficult to set reasonably and will cause a large amount of computational redundancy, which affects the generality of the detection model obtained under fixed parameters. In the past two years, anchor-free detection algorithm has achieved remarkable development in the field of detection on natural image. However, there is no sufficient research on how to deal with multi-scale detection more effectively in anchor-free framework and use these detectors on remote sensing images. In this paper, we propose a specific-attention Feature Pyramid Network (FPN) module, which is able to generate a feature pyramid, basing on the characteristics of objects with various sizes. In addition, this pyramid suits multi-scale object detection better. Besides, a scale-aware detection head is proposed which contains a multi-receptive feature fusion module and a size-based feature compensation module. The new anchor-free detector can obtain a more effective multi-scale feature expression. Experiments on challenging datasets show that our approach performs favorably against other methods in terms of the multi-scale object detection performance

Scattering Information and Meta-learning Based SAR Images Interpretation for Aircraft Target Recognition

The sample scarcity issue is still challenged for SAR images interpretation. The number of geospatial targets related images is constrained of the SAR images interpretation ability of data acquisition, sample labeling, and the lack of target coverage. Our SAR-ATR method is demonstrated based on scattering information and meta-learning. First, the discrete distribution of the spatial structure of different types of aircraft is quite different in SAR images. An associated scattering classifier is designed to guide the network to learn more discriminative intra-class and inter-class feature descriptions. Our proposed classifier facilitates the modeling of discrete degree of the aircraft target quantitatively and balance the weights of sample pairs dynamically through the differentiated analysis of different target discrete distributions. In addition, an adaptive feature refinement module is designed to optimize the network cohesion for the key parts of the aircraft and reduce the interference of background noise. The proposed method integrates the target scattering distribution properties to the network learning process. On 5-way 1-shot emerging categorized recognition task involved only few samples, our experimental results demonstrate that the recognition accuracy of this method is 59.90%, which is 3.85% higher than the benchmark. After reducing the amount of training data by half, the proposed method is still competitive on the new category of few-shot recognition tasks

DataSheet_1_Citrus Huanglongbing correlated with incidence of Diaphorina citri carrying Candidatus Liberibacter asiaticus and citrus phyllosphere microbiome.docx

In China, citrus Huanglongbing (HLB) disease is caused by the Candidatus Liberibacter asiaticus bacterium, which is carried by the Asian citrus psyllid Diaphorina citri Kuwayama. It was hypothesized that the epidemic of the HLB may related with the rate of bacterium presence in the insect vector and bacterium content in plant tissues, as well as the phyllosphere microbe communities changes. This study systematically analyzed the presence or absence of Ca. L. asiaticus in citrus tree leaves and in the insect vector D. citri over a 6-year period using real-time PCR. In addition, changes in the number of bacteria carried by D. citri over 12 months were quantified, as well as the relationship between the proportion of D. citri carrying Ca. L. asiaticus and the proportion of plants infected with Ca. L. asiaticus were analyzed. Results showed that the proportion of D. citri carrying bacteria was stable and relatively low from January to September. The bacteria in citrus leaves relatively low in spring and summer, then peaked in December. The proportion of D. citri carrying bacteria gradually declined from 2014 to 2019. The proportion of D. citri carrying Ca. L. asiaticus showed a significant positive correlation with the proportion of diseased citrus. The phyllosphere bacterial and fungal communities on the healthy citrus leaf were significantly different with the disease leaf in April and December. Pathogenic invasions change the citrus phyllosphere microbial community structure. It could be summarized that citrus Huanglongbing correlated with incidence of Diaphorina citri carrying Candidatus Liberibacter asiaticus and citrus phyllosphere microbiome.</p