1,243 research outputs found
Assessing sustainable development in industrial regions towards smart built environment management using Earth observation big data
This thesis investigates the sustainability of nationwide industrial regions using Earth observation big data, from environmental and socio-economic perspectives. The research contributes to spatial methodology design and decision-making support. New spatial methods, including the robust geographical detector and the concept of geocomplexity, are proposed to demonstrate the spatial properties of industrial sustainability. The study delivers scientific decision-making advice to industry stakeholders and policymakers for the post-construction assessment and future planning phases. The research has been published in prestigious geography journals, demonstrating its success
Robust geographical detector
Geographical detector (GD) is a method to measure spatial associations using a power of determinant (PD) value that compares the variance of data within spatial zones and in the whole study area. Recent studies have implemented GD in diverse fields, such as environmental and socio-economic issues. Spatial data discretization is an essential stage for determining zones using explanatory variables. However, the spatial data discretization process has been sensitive to the GD results. To address this issue, this article proposes a Robust Geographical Detector (RGD) to overcome the limitations of the sensitivity in spatial data discretization and estimate robust PD values of explanatory variables using a B-value. The RGD determines spatial zones with numerical interval breaks using an optimization algorithm of variance-based change point detection. In this study, RGD is implemented in a nationwide case study exploring potential factors of nitrogen dioxide (NO2) density in industrial regions across Australia, where data on both NO2 and potential factors are sourced from satellite images and remote sensing products using Google Earth Engine. Results show that RGD can effectively explore the maximum PD values of spatial associations between response and explanatory variables due to the optimization algorithm-based spatial zones. In addition, RGD-based PD values are generally higher, more robust, and more stable than GD-based PD values since RGD can guarantee the increment of PD values with the increase of interval numbers, which is a challenge in previous GD models. Finally, RGD could provide a more reliable interpretation of PD as RGD finds optimal intervals-based spatial zones determined by potential factors. This study demonstrates that the developed RGD model can provide robust and reliable solutions to explore spatial associations and identify geographical factors
TFDet: Target-aware Fusion for RGB-T Pedestrian Detection
Pedestrian detection plays a critical role in computer vision as it
contributes to ensuring traffic safety. Existing methods that rely solely on
RGB images suffer from performance degradation under low-light conditions due
to the lack of useful information. To address this issue, recent multispectral
detection approaches have combined thermal images to provide complementary
information and have obtained enhanced performances. Nevertheless, few
approaches focus on the negative effects of false positives caused by noisy
fused feature maps. Different from them, we comprehensively analyze the impacts
of false positives on the detection performance and find that enhancing feature
contrast can significantly reduce these false positives. In this paper, we
propose a novel target-aware fusion strategy for multispectral pedestrian
detection, named TFDet. Our fusion strategy highlights the pedestrian-related
features while suppressing unrelated ones, resulting in more discriminative
fused features. TFDet achieves state-of-the-art performance on both KAIST and
LLVIP benchmarks, with an efficiency comparable to the previous
state-of-the-art counterpart. Importantly, TFDet performs remarkably well even
under low-light conditions, which is a significant advancement for ensuring
road safety. The code will be made publicly available at
\url{https://github.com/XueZ-phd/TFDet.git}
Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods
This paper investigates the problem of online statistical inference of model
parameters in stochastic optimization problems via the Kiefer-Wolfowitz
algorithm with random search directions. We first present the asymptotic
distribution for the Polyak-Ruppert-averaging type Kiefer-Wolfowitz (AKW)
estimators, whose asymptotic covariance matrices depend on the function-value
query complexity and the distribution of search directions. The distributional
result reflects the trade-off between statistical efficiency and function query
complexity. We further analyze the choices of random search directions to
minimize the asymptotic covariance matrix, and conclude that the optimal search
direction depends on the optimality criteria with respect to different summary
statistics of the Fisher information matrix. Based on the asymptotic
distribution result, we conduct online statistical inference by providing two
construction procedures of valid confidence intervals. We provide numerical
experiments verifying our theoretical results with the practical effectiveness
of the procedures
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