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Dynamic clustering transformer network for point cloud segmentation Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-23 Dening Lu, Jun Zhou, Kyle (Yilin) Gao, Jing Du, Linlin Xu, Jonathan Li
Point cloud segmentation is one of the most important tasks in LiDAR remote sensing with widespread scientific, industrial, and commercial applications. The research thereof has resulted in many breakthroughs in 3D object and scene understanding. Existing methods typically utilize hierarchical architectures for feature representation. However, the commonly used sampling and grouping methods in hierarchical
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Dynamics of the 2021 unrest at Changbaishan Tianchi volcano from ALOS-2/PALSAR-2 and seismic data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-23 Lianhuan Wei, Ying Sun, Xingyu Pan, Guoming Liu, Elisa Trasatti, Cristiano Tolomei, Guido Ventura, Christian Bignami, Meng Ao, Shanjun Liu
The Changbaishan Tianchi intraplate volcano is one of the most active and hazardous volcanoes of NE Asia, characterized by a summit caldera formed after the 946 CE ‘Millennium’ Plinian eruption. From December 2020 to June 2021, the frequency and magnitude of earthquakes at Tianchi were significantly higher than during background periods, with hundreds of earthquakes (46 events per month in average)
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GFSegNet: A multi-scale segmentation model for mining area ground fissures Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-22 Peng Chen, Peixian Li, Bing Wang, Xingcheng Ding, Yongliang Zhang, Tao Zhang, TianXiang Yu
Precise identification of ground fissures is of paramount importance for the safety and environmental management of coal mining areas. However, the surface environment in coal mining regions is complex, and, to date, the efficiency of artificial fissure detection has been relatively low. Therefore, we have proposed a ground fissure automatic identification model based on an encoder–decoder architecture
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A semi-analytical approach for estimating inland water inherent optical properties and chlorophyll a using airborne hyperspectral imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-22 Chao Niu, Kun Tan, Xue Wang, Peijun Du, Chen Pan
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YOLOv8-BYTE: Ship tracking algorithm using short-time sequence SAR images for disaster response leveraging GeoAI Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-22 Muhammad Yasir, Liu Shanwei, Xu Mingming, Wan Jianhua, Sheng Hui, Shah Nazir, Xin Zhang, Arife Tugsan Isiacik Colak
Ship tracking technology is crucial for emergency rescue in the event of a disaster. Quickly identifying the position and status of vessels is vital for rescue teams to be able to deploy efficiently in disaster areas. When responding to emergencies or natural disasters, ship tracking technology plays a critical role in supporting emergency rescue operations and resource allocation, improving the overall
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Mitigating underestimation of fire emissions from the Advanced Himawari Imager: A machine learning and multi-satellite ensemble approach Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-21 Yoojin Kang, Jungho Im
The accurate estimation of biomass burning emissions has played a crucial role in air quality and climate forecast modeling. Satellite-based fire radiative power (FRP) has proven effective for calculating biomass burning emissions. However, FRP-based emission estimations in East Asia often rely on polar-orbiting satellites owing to the unstable performance of Japan Aerospace Exploration Agency Advanced
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Frequency and spatial based multi-layer context network (FSCNet) for remote sensing scene classification Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-21 Wei Wang, Yujie Sun, Ji Li, Xin Wang
Remote Sensing Scene Classification (RSSC) is an important and challenging research topic due to the variety of land cover sizes and spatial combinations, as well as significant interclass similarity and intraclass variability. Currently, convolutional neural network (CNN)-based methods have been widely used in RSSC tasks with significant results. However, CNNs lack the ability to obtain long-term
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Detecting and quantifying zero tillage technology adoption in Indian smallholder systems using Sentinel-2 multi-spectral imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-21 Monish Vijay Deshpande, Dhanyalekshmi Pillai, Vijesh V. Krishna, Meha Jain
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High temporal resolution quasi-global landscape soil freeze–thaw map from spaceborne GNSS-R technology and SMAP radiometer measurements Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-21 Wentao Yang, Fei Guo, Xiaohong Zhang, Zhiyu Zhang, Yifan Zhu
Landscape freeze–thaw (F/T) state parameters are an integral part of research on terrestrial hydrological processes, vegetation growth dynamics, and terrestrial–atmospheric trace gas exchange. Therefore, the development of large-scale, continuous, and rapid F/T observation records is essential. This study proposes a scheme to effectively retrieve quasi-global daily soil F/T states from spaceborne Global
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Sentinel-2 MSI image time series reveal hydrological and geomorphological control of the sedimentation processes in an Amazonian hydropower dam Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-20 Diego R. Alves e Santos, Jean-Michel Martinez, Diogo Olivetti, André Zumak, David Guimarães, Keila Aniceto, Ednaldo Severo, Osmair Ferreira, Tristan Harmel, Mauricio Cordeiro, Naziano Fillizola, Bruna Sell, Daniel Fernandes, Camila Souto, Henrique Roig
Monitoring sediment transport in a run-of-river hydropower dam was investigated to detect and quantify the siltation processes using Sentinel-2/MSI satellite images. We developed a method allowing us to assess the fate of sediment discharge in reservoirs and to map the locations of eroding and silting stream cross sections. This monitoring was achieved by retrieving the seasonal and interannual variation
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A 3D virtual geographic environment for flood representation towards risk communication Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-20 Weilian Li, Jun Zhu, Saied Pirasteh, Qing Zhu, Yukun Guo, Lan Luo, Youness Dehbi
Risk communication seeks to develop a shared understanding of disaster among stakeholders, thereby amplifying public awareness and empowering them to respond more effectively to emergencies. However, existing studies have overemphasized specialized numerical modelling, making the professional output challenging to understand and use by non-research stakeholders. In this context, this article proposes
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REPS: Rotation equivariant Siamese network enhanced by probability segmentation for satellite video tracking Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-20 Yuzeng Chen, Yuqi Tang, Qiangqiang Yuan, Liangpei Zhang
Satellite video is an emerging surface observation data that has drawn increasing interest due to its potential in spatiotemporal dynamic analysis. Single object tracking of satellite videos allows the continuous acquisition of the positions and ranges of objects and establishes the correspondences in the video sequence. However, small-sized objects are vulnerable to rotation and non-rigid deformation
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A multiscale enhanced pavement crack segmentation network coupling spectral and spatial information of UAV hyperspectral imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-19 Xiao Chen, Xianfeng Zhang, Miao Ren, Bo Zhou, Min Sun, Ziyuan Feng, Baoying Chen, Xiaobo Zhi
Road pavement cracks are a critical factor affecting the health conditions of road pavements. Accurate crack detection contributes to providing data support for road maintenance measures. Compared to conventional crack detection algorithms, deep learning based crack segmentation methods have practical significance for road maintenance and traffic safety management due to their high precision and automation
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A high-precision fusion bathymetry of multi-channel waveform curvature for bathymetric LiDAR systems Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-19 Lin Wu, Yifu Chen, Yuan Le, Yue Qian, Dongfang Zhang, Lizhe Wang
Airborne LiDAR bathymetry (ALB) system is an attractive and efficient method for nearshore bathymetry and underwater topography mapping. To ensure and improve the measurement accuracy and reliability in various water environments, different receivers using a segmented field of view (FOV) have been designed and are implemented in ALB. These are used to obtain various echo data of multiple channels and
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Not all points are balanced: Class balanced single-stage outdoor multi-class 3D object detector from point clouds Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-19 Yidong Chen, Guorong Cai, Qiming Xia, Zhaoliang Liu, Binghui Zeng, Zongliang Zhang, Jinhe Su, Zongyue Wang
Outdoor 3D object detection is a hot topic in autonomous driving. The mainstream pure point cloud method is down-sampling through different task-oriented strategies to retain representative foreground points. Although such strategies are conducive to finding instances, these methods still suffer from two issues: during down-sampling stages, and in the final retained point clouds. The former imbalance
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Precision in mapping and assessing mangrove Biomass: Insights from the Persian Gulf coasts Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-16 Saied Pirasteh, Davood Mafi-Gholami, Huxiong Li, Zhaoxi Fang, Akram Nouri-Kamari, Behnam Khorrami
This groundbreaking research makes a contribution to climate change adaptation studies by filling a crucial knowledge gap related to the precise evaluation of mangrove biomass—an essential element influencing the future trends of coastal ecosystems. Specifically, the study concentrates on the Hara Biosphere Reserve (HBR) on the coasts of the Persian Gulf (PG), aiming to generate precise maps of mangrove
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Robust change detection for remote sensing images based on temporospatial interactive attention module Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-16 Jinjiang Wei, Kaimin Sun, Wenzhuo Li, Wangbin Li, Song Gao, Shunxia Miao, Qinhui Zhou, Junyi Liu
Change Detection (CD) is a vital monitoring method in Earth observation, especially pertinent for land-use analysis, city management, and disaster damage assessment. However, in the era of constellation interconnection and air-sky collaboration, the changes in the Regions Of Interest (ROI) cause many false detections due to geometric perspective rotation and temporal style difference. In response to
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Spatial domain transfer: Cross-regional paddy rice mapping with a few samples based on Sentinel-1 and Sentinel-2 data on GEE Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-16 Lingyu Sun, Yuxin Lou, Qian shi, Liangpei Zhang
The global population is on the rise, leading to an increased demand for food resources. Accurate mapping and monitoring of paddy rice fields have become crucial for effective food management and yield estimation. Several large-scale paddy rice mapping products with medium-to-high resolution have been made by using threshold-based phenological methods. The main shortcoming is that paddy rice varies
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Automated deformation detection and interpretation using InSAR data and a multi-task ViT model Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-16 Mahmoud Abdallah, Samaa Younis, Songbo Wu, Xiaoli Ding
Many geological hazards are associated with ground deformations. Prompt and accurate detection and interpretation of ground deformation is therefore vital to geohazard mitigation. Multitemporal Interferometric Synthetic Aperture Radar (MT-InSAR) is an effective geodetic technique for monitoring ground deformation. However, accurate computation and interpretation of deformation using InSAR are often
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Corrigendum to “Spatial and spectral analysis of fairy circles in namibia on a landscape scale using satellite image processing and machine learning analysis” [Int. J. Appl. Earth Observ. Geoinform. 121 (2023) 103377] Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-14 Klil Noy, Micha Silver, Ondrej Pesek, Hezi Yizhaq, Eugene Marais, Arnon Karnieli
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The relationship between remotely-sensed spectral heterogeneity and bird diversity is modulated by landscape type Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-14 Dominika Prajzlerová, Vojtěch Barták, Petr Keil, Vítězslav Moudrý, Markéta Zikmundová, Petr Balej, François Leroy, Duccio Rocchini, Michela Perrone, Marco Malavasi, Petra Šímová
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Corrigendum to “A fast and robust cirrus removal method for Landsat 8/9 images” [Int. J. Appl. Earth Obs. and Geoinf. 128 (2024) 103691] Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-13 Tao Jiang, Huanfeng Shen, Huifang Li, Chi Zhang, Liying Xu, Dekun Lin
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Long-term series wetland classification of Guangdong-Hong Kong-Macao Greater Bay Area based on APSMnet Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-13 Anjun Lou, Zhi He, Chengle Zhou, Guanglin Lai
Wetlands play a crucial role in achieving carbon peak and carbon neutrality goals. Exploring spatiotemporal distribution is one of the fundamental task in wetland research. However, existing large-scale and long-term series wetland mapping methods have challenges related to classification accuracy and obtaining inter-annual wetland samples. Therefore, a rapid sample collection and precise classification
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A temporary soil dump settlement and landslide risk analysis using the improved small baseline subset-InSAR and continuous medium model Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-13 Xiaoqiong Qin, Yuanjun Huang, Chisheng Wang, Kebin Jiang, Linfu Xie, Rong Liu, Xuguo Shi, Xiangsheng Chen, Bochen Zhang
Abandoned soil disposal in China has become increasingly challenging, which may pose significant safety hazards such as soil settlement and landslides. Interferometry Synthetic Aperture Radar (InSAR) is effective in surface deformation monitoring and Small Baseline Subset-InSAR (SBAS-InSAR) can obtain sufficient coherent point density from the bare soil surface. This study investigated Chishi soil
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A rapid soil Chromium pollution detection method based on hyperspectral remote sensing data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-13 Lihan Chen, Kun Tan, Xue Wang, Yu Chen
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Combining vector and raster data in regionalization: A unified framework for delineating spatial unit boundaries for socio-environmental systems analyses Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-13 Xin Feng, Jennifer Koch
Regionalization has emerged as a crucial research area for the past 50 years, including aggregating smaller areas into larger, contiguous, and/or homogeneous regions. Spatial optimization techniques are advantageous for solving regionalization problems, yet their nondeterministic polynomial-time (NP) hard nature leads to computational complexity and time consumption, especially with extensive datasets
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Spatiotemporal mapping of urban trade and shopping patterns: A geospatial big data approach Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-12 Bakhtiar Feizizadeh, Davoud Omarzadeh, Thomas Blaschke
The economic viability of an urban area in terms of trade and shopping significantly impacts its residents’ quality of life and is crucial for any sustainable development initiative. Geographic information systems (GIS) are well established, but the use of GIS technology within finance and trade analysis is still in its infancy. In this article, we highlighted the potential of GIS technology and big
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ScribbleCDNet: Change detection on high-resolution remote sensing imagery with scribble interaction Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-12 Zhipan Wang, Minduan Xu, Zhongwu Wang, Qing Guo, Qingling Zhang
Change detection on high-resolution remote sensing imagery using end-to-end deep learning methods has attracted considerable attention in recent years. Nevertheless, the performance of end-to-end models on complicated scenarios still is limited. Interactive deep-learning models have proven to be a valuable technique for enhancing model performance with minimal human interaction. For instance, the clicks-based
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Wildfire risk assessment using deep learning in Guangdong Province, China Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-12 Wenyu Jiang, Yuming Qiao, Xinxin Zheng, Jiahao Zhou, Juncai Jiang, Qingxiang Meng, Guofeng Su, Shaobo Zhong, Fei Wang
The severe wildfires that have ravaged Guangdong province, China, present a significant threat to the local ecosystem, socio-economics, and public health. Effective risk assessment is essential for early warning and timely prevention in wildfire management, thereby mitigating disaster losses. In this study, we compiled a dataset comprising 11,507 historical wildfire incidents in Guangdong Province
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Enhancing SDGSAT-1 night light images using a panchromatic guidance denoising algorithm Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-12 Ning Wang, Yonghong Hu, Xiao-Ming Li, Yiling Lan, Chuanli Kang, Lin Yan, Changyong Dou, Chen Miao
The Glimmer Imager on board the Sustainable Development Science Satellite 1 (SDGSAT-1) is capable of providing high-resolution nighttime light images. However, the presence of stripe noise and dark current noise in L4A images affects the authenticity of image information, thus limiting its scientific applications. This study proposes a panchromatic guidance denoising algorithm to enhance the quality
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OASL: Orientation-aware adaptive sampling learning for arbitrary oriented object detection Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-12 Zifei Zhao, Shengyang Li
Arbitrary oriented object detection (AOOD) is a fundamental task in aeiral image interpretation, which is commonly implemented by optimizing three subtasks: classification, localization, and orientation. The consistency of classification, localization, and orientation is crucial for achieving high performance of AOOD detectors. However, independent prediction between subtasks and differences in quality
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Interpretable deep learning for consistent large-scale urban population estimation using Earth observation data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-12 Sugandha Doda, Matthias Kahl, Kim Ouan, Ivica Obadic, Yuanyuan Wang, Hannes Taubenböck, Xiao Xiang Zhu
Accurate and up-to-date mapping of the human population is fundamental for a wide range of disciplines, from effective governance and establishing policies to disaster management and crisis dilution. The traditional method of gathering population data through census is costly and time-consuming. Recently, with the availability of large amounts of Earth observation data sets, deep learning methods have
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S[formula omitted]WaveNet: A novel spectral–spatial wave network for hyperspectral image classification Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-11 Yanan Jiang, Zitong Zhang, Chunlei Zhang, Heng Zhou, Qiaoyu Ma, Chengcheng Zhong
Deep learning has made significant progress in hyperspectral image (HSI) classification, and its powerful ability to automatically learn abstract features is well recognized. Recently, the simple architecture of multi-layer perceptron (MLP) has been extensively employed to extract long-range dependencies of HSI and achieved impressive results. However, existing MLP-based models exhibit insufficient
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Mapping the landscape and roadmap of geospatial artificial intelligence (GeoAI) in quantitative human geography: An extensive systematic review Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-11 Siqin Wang, Xiao Huang, Pengyuan Liu, Mengxi Zhang, Filip Biljecki, Tao Hu, Xiaokang Fu, Lingbo Liu, Xintao Liu, Ruomei Wang, Yuanyuan Huang, Jingjing Yan, Jinghan Jiang, Michaelmary Chukwu, Seyed Reza Naghedi, Moein Hemmati, Yaxiong Shao, Nan Jia, Zhiyang Xiao, Tian Tian, Yaxin Hu, Lixiaona Yu, Winston Yap, Edgardo Macatulad, Zhuo Chen, Yunhe Cui, Koichi Ito, Mengbi Ye, Zicheng Fan, Binyu Lei, Shuming
This paper brings a comprehensive systematic review of the application of geospatial artificial intelligence (GeoAI) in quantitative human geography studies, including the subdomains of cultural, economic, political, historical, urban, population, social, health, rural, regional, tourism, behavioural, environmental and transport geography. In this extensive review, we obtain 14,537 papers from the
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Airborne data and machine learning for urban tree species mapping: Enhancing the legend design to improve the map applicability for city greenery management Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-11 Jan Niedzielko, Dominik Kopeć, Justyna Wylazłowska, Adam Kania, Jakub Charyton, Anna Halladin-Dąbrowska, Maria Niedzielko, Karol Berłowski
Presently remote sensing appears to be the only technology that makes it possible to conduct an inventory of tree taxa throughout the whole city. One of the main challenges during a remote sensing implementation project is the constructing of a map legend for tree taxa. The article presents the first comprehensive investigation into the construction of a legend for urban tree species mapping in the
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Forest disturbance characterization in the era of earth observation big data: A mapping review Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-10 Enmanuel Rodríguez Paulino, Martin Schlerf, Achim Röder, Johannes Stoffels, Thomas Udelhoven
Forests play a crucial role throughout the world as highly productive ecosystems, serving approximately a quarter of the human population by providing valuable services. However, these ecosystems are subject to various natural and human-induced disturbances such as insect outbreaks, fires, windthrow, snow damage, selective logging, and harvest, which significantly influence the composition and structure
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Diffusion models for spatio-temporal-spectral fusion of homogeneous Gaofen-1 satellite platforms Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-10 Jingbo Wei, Lei Gan, Wenchao Tang, Ming Li, Yuejun Song
Due to hardware technology limitations, satellite sensors are unable to capture images with high temporal, spatial, and spectral resolutions simultaneously. However, the Gaofen-1 satellite overcomes this challenge by incorporating 2-meter panchromatic, 8-meter multispectral, and 16-meter wide-field cameras, allowing for the integration of images from these sensors. To address this issue, we propose
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Background covariance discriminative dictionary learning for hyperspectral target detection Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-10 Zhiyuan Li, Tingkui Mu, Bin Wang, Qiujie Yang, Haishan Dai
Hyperspectral target detection (HTD) aims to identifying targets within a hyperspectral image (HSI) based on provided target spectra. In the current HTD field, representation-based detectors have attracted much attention. However, there are two prominent challenges that are particularly noteworthy. First, the background class encompasses diverse land covers, making its accurate representation challenging
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Understanding urban expansion and shrinkage via green plastic cover mapping based on GEE cloud platform: A case study of Shandong, China Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-10 Jiantao Liu, Yan Zhang, Quanlong Feng, Gaofei Yin, Dong Zhang, Yi Li, Jianhua Gong, Yexiang Li, Jingxian Li
Green Plastic Cover (GPC) has been widely used in construction sites of China as the main measure for dust suppression. As a unique land cover in China, GPC could be viewed as a sign for evaluating the intensity of urban construction. Therefore, time series GPC maps could provide a new perspective to understand the pattern of urban development, including both urban expansion and urban shrinkage. The
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A new vegetation index based on UAV for extracting plateau vegetation information Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-07 Cheng Chen, Xiping Yuan, Shu Gan, WeiDong Luo, Rui Bi, RaoBo Li, Sha Gao
In the realm of remote sensing, the vegetation index (VI) can bring out the subtleties and characteristics of a class of features or a specific feature and can indicate crop development, vegetation and non-vegetation, soil, and other related information. However, in the plateau region, due to characteristics such as plant type, soil type, and vegetation density, the currently available vegetation indices
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Self-supervised multi-task learning framework for safety and health-oriented road environment surveillance based on connected vehicle visual perception Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-06 Shaocheng Jia, Wei Yao
Cutting-edge connected vehicle (CV) technologies have drawn much attention in recent years. The real-time traffic data captured by a CV can be shared with other CVs and data centers so as to open new possibilities for solving diverse transportation problems. The trajectory data of CVs have been well-studied and widely used. However, image data captured by onboard cameras in a connected environment
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Measuring cyclists’ subjective perceptions of the street riding environment using K-means SMOTE-RF model and street view imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-06 Qisheng Zeng, Zheng Gong, Songtai Wu, Caigang Zhuang, Shaoying Li
Cyclists' willingness to ride is usually influenced by their subjective perception of the street riding environment. Measuring this perception is crucial for enhancing residents' willingness to ride. We propose an SSB framework (Public Security, Traffic Safety, Scenic Beauty) to quantify cyclists' subjective perception using street view imagery (SVI) and volunteer rating data. To address the issue
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Forest degradation contributes more to carbon loss than forest cover loss in North American boreal forests Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-06 Ling Yu, Lei Fan, Philippe Ciais, Jingfeng Xiao, Frédéric Frappart, Stephen Sitch, Jingming Chen, Xiangming Xiao, Rasmus Fensholt, Zhongbing Chang, Hongqian Fang, Xiaojun Li, Tiangxiang Cui, Mingguo Ma, Jean-Pierre Wigneron
The carbon sinks of North American boreal forests have been threatened by global warming and forest disturbances in recent decades, but knowledge about the carbon balance of these forests in recent years remains unknown. We tracked annual aboveground carbon (AGC) changes from 2016 to 2021 across the forest regions of NASA’s Arctic Boreal Vulnerability Experiment (ABoVE) core study domain, using Vegetation
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Large-scale and high-resolution paddy rice intensity mapping using downscaling and phenology-based algorithms on Google Earth Engine Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-05 Liangli Meng, Yunfei Li, Ruoque Shen, Yi Zheng, Baihong Pan, Wenping Yuan, Jun Li, Li Zhuo
Accurate mapping of paddy rice cropping intensity (PRCI) affects precision agriculture, water use management, and informed decision-making. Many PRCI mapping approaches have been developed and achieved remarkable performance. However, large-scale and high-resolution PRCI mapping in southern China, especially in the Hunan Province, remains challenging. In this region, optical data suffer from serious
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Enhancing mineral prospectivity mapping with geospatial artificial intelligence: A geographically neural network-weighted logistic regression approach Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-04 Luoqi Wang, Jie Yang, Sensen Wu, Linshu Hu, Yunzhao Ge, Zhenhong Du
Accurate prediction of mineral resources is imperative to meet the energy demands of modern society. Nonetheless, this task is often difficult due to estimation bias and limited interpretability of conventional statistical techniques and machine learning methods. To address these shortcomings, we propose a novel geospatial artificial intelligence approach, denoted as geographically neural network-weighted
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Modelling soil moisture and daily actual evapotranspiration: Integrating remote sensing surface energy balance and 1D Richards equation Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-04 Hassan Awada, Costantino Sirca, Serena Marras, Mirko Castellini, Donatella Spano, Mario Pirastru
Evapotranspiration (ET) is a crucial component of the soil–plant-atmosphere system. In semi-arid Mediterranean regions, most land water loss occurs through ET, encompassing both evaporation from the earth's surface and plant transpiration. A comprehensive understanding of the actual ET spatiotemporal dynamics is critically important for hydrological modelling and effective water resource management
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Using the surface scattering mechanism from dual-pol SAR data to estimate topsoil particle-sizefractions Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-03-03 Sandra Cristina Deodoro, Rafael de Andrade Moral, Réamonn Fealy, Tim McCarthy, Rowan Fealy
Data extracted from Synthetic Aperture Radar (SAR) have been widely employed to estimate soil properties. However, these studies are typically constrained to bare soil conditions, as soil information retrieval in vegetated areas remains challenging. Polarimetric decomposition has emerged as a potentially useful method to separate the scattering contributions of different targets (e.g. canopy/leaves
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Sources of uncertainty in satellite-derived chlorophyll-a concentration—An Adriatic Sea case study Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-02-20 Leon Ćatipović, Shubha Sathyendranath, Frano Matić, Žarko Kovač, Luka Kovačić, Živana Ninčević Gladan, Sanda Skejić, Hrvoje Kalinić
This paper analyses a time series of chlorophyll-a profiles in the Adriatic from 1997 to 2019, and compares the data with satellite products with the view of analysing and reducing uncertainties in the corresponding satellite products. Three sources of uncertainties in satellite chlorophyll-a concentration are examined: (a) the algorithm itself; (b) the vertical structure of the water column; and (c)
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DF-DRUNet: A decoder fusion model for automatic road extraction leveraging remote sensing images and GPS trajectory data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-02-06 Bingnan Li, Jiuchong Gao, Shuiping Chen, Samsung Lim, Hai Jiang
Accurate road networks are of great importance to online food delivery (OFD) services. In recent years, various data sources have been used to extract road information. Remote sensing images and Global Positioning System (GPS) trajectories can provide complementary information about roads, and the fusion of these two data sources allows to enhance the accuracy of automatic road extraction. To make
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A multimodal fusion framework for urban scene understanding and functional identification using geospatial data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-02-02 Chen Su, Xinli Hu, Qingyan Meng, Linlin Zhang, Wenxu Shi, Maofan Zhao
Urban scene understanding and functional identification are essential for accurately characterizing the spatial structure and optimizing the city layouts during rapid urbanization. Multimodal data is important for recognizing the distribution patterns of urban functions and revealing internal details. Previous studies have focused primarily on remote sensing imagery and points of interest (POIs) data
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Bathymetry derivation and slope-assisted benthic mapping using optical satellite imagery in combination with ICESat-2 Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-02-03 Yuhui Liu, Yu Zhou, Xiaoqiang Yang
Mapping benthic reefs at high resolution and accuracy is vital for the management and conservation of coral habitats. Optical remote sensing data has emerged as a valuable tool for large-scale reef mapping in the past decades, with numerous data sets and methods being utilised and developed. In this study, we present a comprehensive comparison of optical remote sensing based bathymetry and benthic
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SWCARE: Switchable learning and connectivity-aware refinement method for multi-city and diverse-scenario road mapping using remote sensing images Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-02-03 Lixian Zhang, Shuai Yuan, Runmin Dong, Juepeng Zheng, Bin Gan, Dengmao Fang, Yang Liu, Haohuan Fu
Accurate and efficient mapping of road networks is crucial for evaluating urban development, transportation accessibility, and environmental impact. However, existing road extraction methods utilizing remote sensing images suffer from limited generalization ability and object occlusion, resulting in fragmented and discontinuous segmentation. Consequently, these limitations impede the practical applicability
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Semantic-aware room-level indoor modeling from point clouds Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-02-03 Dong Chen, Lincheng Wan, Fan Hu, Jing Li, Yanming Chen, Yueqian Shen, Jiju Peethambaran
This paper introduces a framework for reconstructing fine-grained room-level models from indoor point clouds. The motivation behind our method stems from the consistent floorwise appearance of building shapes in urban buildings along the vertical direction. To this end, each floor’s points are horizontally sliced to obtain a representative cross-section, from which the linear primitives are detected
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Crowdsourced geospatial data is reshaping urban sciences Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-02-01 Xiao Huang, Siqin Wang, Tianjun Lu, Yisi Liu, Leticia Serrano-Estrada
For many years, urban sciences relied heavily on traditional, authoritative data sources. However, a paradigm shift has occurred recently with the advent of citizen-driven data contribution. This evolution in data acquisition for urban science is attributable to advancements in positioning and navigation technologies, widespread use of digital devices, the rise of Web 2.0, enhanced broadband communications
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Detection of oak decline using radiative transfer modelling and machine learning from multispectral and thermal RPAS imagery Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-02-01 A. Hornero, P.J. Zarco-Tejada, I. Marengo, N. Faria, R. Hernández-Clemente
Oak trees are declining at an unprecedented rate due to the interaction of many factors, such as pests, diseases, droughts, pollution and flooding. Such abiotic- and biotic-induced stress produces anomalies in plant physiological and functional traits (PTs) that may be spectrally detected, serving to quantify trees’ health status and condition. Previous studies have demonstrated that PTs’ dynamic response
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A new feature extraction algorithm for measuring the spatial arrangement of texture Primitives: Distance coding diversity Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-02-01 Wenquan Zhu, Xinyi Yang, Ruoyang Liu, Cenliang Zhao
Texture is a key spatial feature for object recognition in remote sensing images. Currently, most texture feature extraction methods mainly focus on the repeated patterns of texture primitives (the basic texture units) but rarely consider their spatial arrangement. Although some methods can capture the spatial arrangement of texture primitives to some extent, their principles and algorithms are complex
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Quantifying earthquake-induced bathymetric changes in a tufa lake using high-resolution remote sensing data Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-01-31 Jinchen He, Shuhang Zhang, Wei Feng, Jiayuan Lin
Detecting earthquake-induced bathymetric changes helps to understand the geomorphologic process of tufa lakes. Traditional field measurement methods are difficult for spatially complete and continuous bathymetric mapping. Multi-temporal high-resolution optical satellite images are cost-efficient data used for bathymetric change detection. However, for detecting bathymetric changes in tufa lakes, collecting
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Towards the digital twin of urban forest: 3D modeling and parameterization of large-scale urban trees from close-range laser scanning Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-01-31 Chen Chen, Haodong Wang, Duanchu Wang, Di Wang
Trees play a crucial role in urban environment, offering distinct ecological and aesthetic values. Fine-grained urban tree models hold valuable potential for urban landscape planning and green space management. Consequently, in recent years, how to reconstruct detailed tree models using digital twin technology has become a focal point of interest. Point cloud data has become a major source for tree
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Time-varying quadruple collocation for enhanced satellite and reanalysis precipitation data error estimation and integration Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-02-01 Angelika L. Alcantara, Kuk-Hyun Ahn
For the past few years, there have been improvements in collocation approaches that do not rely on in-situ observations to estimate data uncertainties. These approaches have been applied in various applications, including data fusion. A notable development is the Quadruple Collocation (QC) approach, which effectively accounts for error cross-correlation between datasets, setting it apart from previous
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GDSNet: A gated dual-stream convolutional neural network for automatic recognition of coseismic landslides Int. J. Appl. Earth Obs. Geoinf. (IF 7.5) Pub Date : 2024-02-01 Xuewen Wang, Xianmin Wang, Yuchen Zheng, Zhiwei Liu, Wenxiang Xia, Haixiang Guo, Dongdong Li
Automatic recognition of numerous coseismic landslides after a violent earthquake is crucial for emergency rescue and post-disaster reconstruction. Currently, deep learning techniques have achieved state-of-the-art performance in coseismic landslide recognition. However, Convolutional Neural Networks (CNNs) often lose detailed information during downsampling and cannot adequately learn changeable shapes