74 research outputs found
LIGHT: Joint Individual Building Extraction and Height Estimation from Satellite Images through a Unified Multitask Learning Network
Building extraction and height estimation are two important basic tasks in
remote sensing image interpretation, which are widely used in urban planning,
real-world 3D construction, and other fields. Most of the existing research
regards the two tasks as independent studies. Therefore the height information
cannot be fully used to improve the accuracy of building extraction and vice
versa. In this work, we combine the individuaL buIlding extraction and heiGHt
estimation through a unified multiTask learning network (LIGHT) for the first
time, which simultaneously outputs a height map, bounding boxes, and a
segmentation mask map of buildings. Specifically, LIGHT consists of an instance
segmentation branch and a height estimation branch. In particular, so as to
effectively unify multi-scale feature branches and alleviate feature spans
between branches, we propose a Gated Cross Task Interaction (GCTI) module that
can efficiently perform feature interaction between branches. Experiments on
the DFC2023 dataset show that our LIGHT can achieve superior performance, and
our GCTI module with ResNet101 as the backbone can significantly improve the
performance of multitask learning by 2.8% AP50 and 6.5% delta1, respectively
DCP-Net: A Distributed Collaborative Perception Network for Remote Sensing Semantic Segmentation
Onboard intelligent processing is widely applied in emergency tasks in the
field of remote sensing. However, it is predominantly confined to an individual
platform with a limited observation range as well as susceptibility to
interference, resulting in limited accuracy. Considering the current state of
multi-platform collaborative observation, this article innovatively presents a
distributed collaborative perception network called DCP-Net. Firstly, the
proposed DCP-Net helps members to enhance perception performance by integrating
features from other platforms. Secondly, a self-mutual information match module
is proposed to identify collaboration opportunities and select suitable
partners, prioritizing critical collaborative features and reducing redundant
transmission cost. Thirdly, a related feature fusion module is designed to
address the misalignment between local and collaborative features, improving
the quality of fused features for the downstream task. We conduct extensive
experiments and visualization analyses using three semantic segmentation
datasets, including Potsdam, iSAID and DFC23. The results demonstrate that
DCP-Net outperforms the existing methods comprehensively, improving mIoU by
2.61%~16.89% at the highest collaboration efficiency, which promotes the
performance to a state-of-the-art level
Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoders
Semantic segmentation of point clouds generates comprehensive understanding
of scenes through densely predicting the category for each point. Due to the
unicity of receptive field, semantic segmentation of point clouds remains
challenging for the expression of multi-receptive field features, which brings
about the misclassification of instances with similar spatial structures. In
this paper, we propose a graph convolutional network DGFA-Net rooted in dilated
graph feature aggregation (DGFA), guided by multi-basis aggregation loss
(MALoss) calculated through Pyramid Decoders. To configure multi-receptive
field features, DGFA which takes the proposed dilated graph convolution
(DGConv) as its basic building block, is designed to aggregate multi-scale
feature representation by capturing dilated graphs with various receptive
regions. By simultaneously considering penalizing the receptive field
information with point sets of different resolutions as calculation bases, we
introduce Pyramid Decoders driven by MALoss for the diversity of receptive
field bases. Combining these two aspects, DGFA-Net significantly improves the
segmentation performance of instances with similar spatial structures.
Experiments on S3DIS, ShapeNetPart and Toronto-3D show that DGFA-Net
outperforms the baseline approach, achieving a new state-of-the-art
segmentation performance.Comment: accepted to AAAI Workshop 202
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
A novel multivariate logistic model for predicting risk factors of failed treatment with carbapenem-resistant Acinetobacter baumannii ventilator-associated pneumonia
BackgroundThis study aimed to explore the risk factors for failed treatment of carbapenem-resistant Acinetobacter baumannii ventilator-associated pneumonia (CRAB-VAP) with tigecycline and to establish a predictive model to predict the incidence of failed treatment and the prognosis of CRAB-VAP.MethodsA total of 189 CRAB-VAP patients were included in the safety analysis set from two Grade 3 A national-level hospitals between 1 January 2022 and 31 December 2022. The risk factors for failed treatment with CRAB-VAP were identified using univariate analysis, multivariate logistic analysis, and an independent nomogram to show the results.ResultsOf the 189 patients, 106 (56.1%) patients were in the successful treatment group, and 83 (43.9%) patients were in the failed treatment group. The multivariate logistic model analysis showed that age (OR = 1.04, 95% CI: 1.02, 1.07, p = 0.001), yes. of hypoproteinemia (OR = 2.43, 95% CI: 1.20, 4.90, p = 0.013), the daily dose of 200 mg (OR = 2.31, 95% CI: 1.07, 5.00, p = 0.034), yes. of medication within 14 days prior to surgical intervention (OR = 2.98, 95% CI: 1.19, 7.44, p = 0.019), and no. of microbial clearance (OR = 0.31, 95% CI: 0.14, 0.70, p = 0.005) were risk factors for the failure of tigecycline treatment. Receiver operating characteristic (ROC) analysis showed that the AUC area of the prediction model was 0.745 (0.675–0.815), and the decision curve analysis (DCA) showed that the model was effective in clinical practice.ConclusionAge, hypoproteinemia, daily dose, medication within 14 days prior to surgical intervention, and microbial clearance are all significant risk factors for failed treatment with CRAB-VAP, with the nomogram model indicating that high age was the most important factor. Because the failure rate of CRAB-VAP treatment with tigecycline was high, this prediction model can help doctors correct or avoid risk factors during clinical treatment
Triphenylphosphine-Based Covalent Organic Frameworks and Heterogeneous Rh-P-COFs Catalysts
The synthesis of phosphine-based functional covalent organic frameworks (COFs) has attracted great attention recently. Herein, we present two examples of triphenylphosphine-based COFs (termed P-COFs) with well-defined crystalline structures, high specific surface areas, and good thermal stability. Furthermore, rhodium catalysts with these P-COFs as support material show high turnover frequency for the hydroformylation of olefins, as well as excellent recycling performance. This work not only extends the phosphine-based COF family, but also demonstrates their application in immobilizing homogeneous metal-based (e.g., Rh-phosphine) catalysts for application in heterogeneous catalysis
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
The enormous repetitive Antarctic krill genome reveals environmental adaptations and population insights
Antarctic krill (Euphausia superba) is Earth’smost abundant wild animal, and its enormous biomass is vital to
the Southern Ocean ecosystem. Here, we report a 48.01-Gb chromosome-level Antarctic krill genome, whose
large genome size appears to have resulted from inter-genic transposable element expansions. Our assembly
reveals the molecular architecture of the Antarctic krill circadian clock and uncovers expanded gene
families associated with molting and energy metabolism, providing insights into adaptations to the cold
and highly seasonal Antarctic environment. Population-level genome re-sequencing from four geographical
sites around the Antarctic continent reveals no clear population structure but highlights natural selection
associated with environmental variables. An apparent drastic reduction in krill population size 10 mya and
a subsequent rebound 100 thousand years ago coincides with climate change events. Our findings uncover
the genomic basis of Antarctic krill adaptations to the Southern Ocean and provide valuable resources for
future Antarctic research
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