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Improving service accessibility and equity for sustainable development goals without newly facilities by rural settlement reconstruction

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Geography and Sustainability 2025年第1期6卷 105-117页

作者： Caihui Cui Zhigang Han Feng Liu Jingru Ma Haiying Wang Xiang Chen College of Geographical Sciences Faculty of Geographical Science and EngineeringHenan UniversityZhengzhou 450046China College of Remote Sensing and Geoinformatics Engineering Faculty of Geographical Science and EngineeringHenan UniversityZhengzhou 450046China Henan Industrial Technology Academy of Spatiotemporal Big Data Henan UniversityZhengzhou 450046China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan UniversityZhengzhou 450046China Key Research Institute of Yellow River Civilization and Sustainable Development Henan UniversityKaifeng 475004China Department of Geography University of ConnecticutStorrsCT 06269USA

Ensuring the provision of accessible,affordable,and high-quality public services to all individuals aligns with one of the paramount aims of the United Nations’Sustainable Development Goals(SDGs).In the face of esca lating urbanization and a dwindling rural populace in China,reconstructing rural settlements to enhance public service accessibility has become a fundamental strategy for achieving the SDGs in rural ***,few stud ies have examined the optimal methods for rural settlement reconstruction that ensure accessible and equitable public services while considering multiple existing facilities and service *** paper focuses on rural settlement reconstruction in the context of the SDGs,employing an inverted MCLP-CC(maximal coverage loca tion problem for complementary coverage)model to identify optimal rural settlements and a rank-based method for their *** in Changyuan,a county-level city in Henan Province,China,this study observed significant enhancements in both accessibility and equity following rural settlement reconstruction by utilizing the MH3SFCA(modified Huff 3-step floating catchment area)and the spatial Lorenz curve ***,these improvements were achieved without the addition of new facilities,with the accessibility increasing by 44.21%,4.97%,and 3.11%;Gini coefficients decreasing by 19.53%,1.64%,and 3.18%;Ricci-Schutz coef-ficients decreasing by 21.09%,2.09%,and 4.33%for educational,medical,and cultural and sports facilities,*** indicated that rural settlement reconstruction can bolster the accessibility and equity of public ser-vices by leveraging existing *** paper provides a new framework for stakeholders to better reconstruct rural settlements and promote sustainable development in rural areas in China.

关键词： SDGs Equity Accessibility Rural settlement reconstruction Spatial optimization

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Deep learning method for makeup style transfer: A survey

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Cognitive Robotics 2021年 1卷 182-187页

作者： Ma, Xiaohan Zhang, Fengquan Wei, Huan Xu, Liuqing School of Information Science and Technology North China University of Technology Beijing100144 China Airborne remote sensing center Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Makeup transfer is one of the applications of image style transfer, which refers to transfer the reference makeup to the face without makeup, and maintaining the original appearance of the plain face and the makeup style of the reference face. In order to understand the research status of makeup transfer, this paper systematically sorts out makeup transfer technology. According to the development process of the method of makeup transfer, our paper first introduces and analyzes the traditional methods of makeup transfer. In particular, the methods of makeup transfer based on deep learning framework are summarized, covering both disadvantages and advantages. Finally, some key points in the current challenges and future development direction of makeup transfer technology are discussed. © 2021

关键词： Deep learning

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High-Resolution Reconstruction and Image Classification Based on Optical Multi-Angle Information

High-Resolution Reconstruction and Image Classification Base...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Yifei Wang Xiaoning Zhang Ziti Jiao Fan Ye Zhaoyang Peng School of Mechatronical Engineering Beijing Institute of Technology Beijing China State Key Laboratory of Remote Sensing Science Beijing Normal University Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Earth observation technology plays an important role in military reconnaissance, agriculture and forestry plant protection, emergency disaster relief, etc. In this paper, we mainly apply a prototype inversion algorithm: based on the multi-angle multi-spatial scale data of three scenes including trees, buildings and mixed objects simulated by the three-dimensional radiative transfer model LESS, we take the 500m/pixel coarse-resolution BRDF as an archetype priori information to reconstruct the high-resolution multi-angle information at 10m/pixel level. Then, sensitive feature indices are used to categorize the reconstructed 10m images, and the simulated 10m images are used as standard values to evaluate the classification accuracy. The results show that the average accuracies of BRDF inversion for the three scenes are 96.2%, 50.08% and 71.43%, respectively, and the plant class is more suitable for this inversion model. In terms of the classification accuracies, the three scenes are 60.52%, 96.84% and 76.60%, respectively, with building scene shows the highest accuracy.

关键词： Reflectivity Analytical models Accuracy Buildings Vegetation mapping Vegetation Feature extraction

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Multi-Label Guided Supervised Contrastive Learning for Earth Observation Pretraining

Multi-Label Guided Supervised Contrastive Learning for Earth...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Yi Wang Conrad M Albrecht Xiao Xiang Zhu Data Science in Earth Observation Technical University of Munich (TUM) Germany Remote Sensing Technology Institute (IMF) German Aerospace Center (DLR) Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Pretraining foundation models on large-scale satellite imagery has raised great interest in Earth observation. While most pretraining is conducted purely self-supervised, many land cover land use products that provide free and global annotations tend to be overlooked. To bridge this gap, we propose to exploit land-cover-generated multi-label annotations to guide supervised contrastive learning for Earth observation. We match the SSL4EO-S12 dataset with Dynamic World land cover maps and integrate image-level multi-label annotations. During pretraining, the label similarities between different images are calculated, and those with high similarity scores are pulled together in the embedding space. Experimental results on classification and segmentation downstream tasks demonstrate the effectiveness of the proposed method.

关键词： Earth Training Image segmentation Annotations Land surface Contrastive learning Sensors

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Post-Earthquake SAR-Optical Dataset for Quick Damaged-Building Detection

Post-Earthquake SAR-Optical Dataset for Quick Damaged-Buildi...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Yao Sun Yi Wang Michael Eineder Data Science in Earth Observation Technical University of Munich (TUM) Germany Remote Sensing Technology Institute (IMF) German Aerospace Center (DLR) Germany

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This work introduces a dataset for automated earthquake-damaged building detection from post-event satellite imagery. Using very high-resolution Synthetic Aperture Radar (SAR) and optical data from the 2023 Turkey-Syria earthquakes, the dataset includes over four thousand co-registered building footprints and patches. The task is framed as a binary image classification problem, serving as a reference for researchers to expedite algorithm development for rapid damaged building detection in future events. The dataset and codes together with detailed explanations will be made publicly available at https://***/ya0-sun/PostEQ-SARopt-BuildingDamage.

关键词： Buildings Urban areas Optical imaging Adaptive optics Radar polarimetry Satellite images Classification algorithms

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MGTIPM: A Multi-granularity Time Intention Prediction Model for Next Point-of-Interest Recommendation 11

MGTIPM: A Multi-granularity Time Intention Prediction Model ...

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11th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Wu, Junyi Li, Hao Yang, Can Li, Jian Yan, Kai Zhang, Mingda Liu, Ruixiang Wuhan University School of Remote Sensing and Information Engineering Wuhan China State Key Laboratory of Intelligent Vehicle Safty Technology Chongqing Changan Automobile Co.Ltd. Chongqing China Hubei University Faculty of Resources and Environmental Science Wuhan China

ISBN: (纸本)9798350303513

Next Point-of-Interest (POI) recommendation aims at ranking several POIs that the user tends to visit next time based on the user's historical trajectories. Most of the previous studies directly predict the next check-in POIs. However, the user's time intention and revisit preference play important roles in the effective recommendation and can be excavated based on the strong relation between check-in time and location. Therefore, in this study, a new model named MGTIPM is discussed. Specifically, MGTIPM designs a multi-granularity time intention prediction module to effectively predict the next check-in time within a period of one day. Moreover, a balancing mechanism integrates the time intention into the check-in sequence POI recommendation model so that MGTIPM can recognize the users' intention to revisit POIs. Experiments are conducted on two datasets and the results show that the proposed MGTIPM outperforms baselines regarding recommendation performance. © 2023 IEEE.

关键词： Forecasting

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Radar vegetation indices for monitoring surface vegetation: Developments, challenges, and trends

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science of the Total Environment 2024年 945卷 173974页

作者： Hu, Xueqian Li, Li Huang, Jianxi Zeng, Yelu Zhang, Shuo Su, Yiran Hong, Yujiao Hong, Zixiang College of Land Science and Technology China Agricultural University Beijing100083 China Key Laboratory of Remote Sensing for Agri-Hazards Ministry of Agriculture and Rural Affairs Beijing100083 China

Monitoring surface vegetation is essential for environmental protection, disaster prevention, and carbon sequestration in forests. However, optical remote-sensing methods and their derivative technologies typically fail to fully meet this requirement due to constraints such as lighting and weather. Radar vegetation indices (RVIs), developed based on microwave remote-sensing data, describe the dielectric properties and morphological structure of vegetation and have been applied for vegetation monitoring at various scales. This technical review is the first to systematically summarize RVIs;it analyzes and discusses their principles, developments, categories and applications, and provides a comprehensive guide for their use. Additionally, the challenges faced by RVIs, as well as their applicability, were analyzed, and future improvements and development trends were carefully projected. The selection of RVIs must consider the type of data used, the terrain and location of the study area, and the major vegetation types. The effectiveness of RVIs applied to vegetation monitoring can be affected by various factors, including index performance, sensor type, study area, and data type and quality. These factors reduce the reliability and robustness of results, as well as guide the improvement direction of RVIs. The development of technologies, such as artificial intelligence, in remote sensing offers new possibilities for RVIs, enabling the removal of background scattering, improvement in interpretation accuracy, and reduction in application thresholds. Additionally, the development trends in high resolution, multi-polarization, multi-base, multi-dimensional, and networked synthetic aperture radar (SAR) and their satellite platforms offer data support for the next generation of RVIs. The rapid development of RVIs strongly supports the use of surface vegetation monitoring and terrestrial ecosystem research. © 2024 Elsevier B.V.

关键词： Polarization

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Early Wildfire Detection Based on Temporal, Spatial and Spectral Information Fusion

Early Wildfire Detection Based on Temporal, Spatial and Spec...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Qiang Zhang Jian Zhu Center of Hyperspectral Imaging in Remote Sensing (CHIRS) Information Science and Technology College Dalian Maritime University Dalian China

In this work, we propose a novel framework for near-real-time and early-stage wildfire detection using Himawari-8 satellite 10-minute data. Dispense with cloud detection and setting too many manual thresholds. The proposed framework jointly combines spatial, temporal and spectral information for wildfire detection. Compared with JAXA’s wildfire products, the proposed framework can more accurately detect the wildfire points. Especially for the early-stage wildfire detection.

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Analysis Of Net Primary Production Changes and Driving Factors in Chengdu Plain Based On Google Earth Engine And Optimized Parameter Geographical Detector

SSRN

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SSRN 2024年

作者： Wei, Tianyi Yang, Bin School of Environment and Resources Southwest University of Science and Technology Sichuan Mianyang621010 China National Remote Sensing Center Mianyang Science and Technology City Branch Sichuan Mianyang621010 China Sichuan Chengdu610000 China

Changes in Net Primary Production (NPP) and its controlling factors are crucial for understanding the mechanisms sustaining terrestrial ecosystems under the current conditions of natural, environmental, and human activity changes. However, understanding the nonlinear characteristics and spatiotemporal heterogeneity of factors influencing vegetation NPP changes remains insufficient. This study systematically analyzes NPP changes and their driving factors in the Chengdu Plain from 2001 to 2021 using the Google Earth Engine (GEE) platform, combined with the Theil-Sen trend, Hurst index, non-parametric Mann-Kendall test, and an optimal parameters-based geographical detector (OPGD) model. The study quantifies the impact of various factors on NPP changes and their interactive effects. The results indicate: (1) The average annual NPP of vegetation in the study area has increased yearly, with an overall growth of about 85.06 gCm-2yr-1. Spatially, the NPP is higher in the eastern and central parts and lower in the western and southern parts. (2) 79.97% of the area's NPP showed an increasing trend, with 77.07% of the area showing significant growth (p © 2024, The Authors. All rights reserved.

关键词： Abiotic

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Cloud Detection from AWiFS Imagery using Deep Learning

Cloud Detection from AWiFS Imagery using Deep Learning

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Machine Intelligence for GeoAnalytics and remote sensing (MIGARS), International Conference on

作者： Shubham Singhal Latha James Anjaneyulu R V G Srinivas C V Murali Krishnan S Rama Rao Nidamanuri Data Processing Area National Remote Sensing Centre Hyderabad India Aerial Services and Data Management National Remote Sensing Centre Hyderabad India Dept. of Earth and Space Science Indian Institute of Space Science and Technology Thiruvanthapuram India

Cloud detection is an essential pre-processing step to delineate cloud-affected regions for many applications involving optical remote sensing data. This paper presents a deep learning segmentation approach using attention-based U-net architecture, which is used to detect clouds for the Indian remote sensing (IRS) Resourcesat series Advance Wide Field Sensor (AWiFS) imagery. The network can identify cloud cover from the top-of-atmosphere reflectance scene using an Encoder and Decoder along with an attention mechanism. The attention mechanism optimizes the computational resources by minimizing activations, and the Decoder can pay attention only to the relevant part of the scene. This approach results in an overall accuracy of 87.3%. The result demonstrates that the proposed method can be considered a benchmark for AWiFS imagery.

关键词： Deep learning Reflectivity Cloud computing Image segmentation Optical imaging Decoding Optical sensors

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