Big Earth Data

Satellite remote sensing, characterized by extensive coverage, frequent revisits, and continuous monitoring, provides essential data support for addressing global challenges. Over the past six decades, thousands of Earth observation satellites and sensors have been deployed worldwide. These valuable Earth observation assets are contributed independently by various nations and organizations employing diverse methodologies. This poses a significant challenge in effectively discovering global Earth observation resources and realizing their full potential. In this paper, we describe the development of GEOSatDB, the most complete semantic database of civil Earth observation satellites developed based on a unified ontology model. A similarity matching method is used to integrate satellite information and a prompt strategy is used to extract unstructured sensor information. The resulting semantic database contains 127,949 semantic statements for 2,340 remote sensing satellites and 1,021 observation sensors. The global Earth observation capabilities of 195 countries worldwide have been analyzed in detail, and a concrete use case along with an associated query demonstration is presented. This database provides significant value in effectively facilitating the semantic understanding and sharing of Earth observation resources.

Ming Lin

Meng Jin

Juanzi Li

Yuqi Bai

GEOSatDB: global civil earth observation satellite semantic database

GEOSatDB：全球民用地球观测卫星语义数据库

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Siqin Wang

Ruomei Wang

Xiao Huang

Zhenlong Li

Shuming Bao

A GIS-based analytical framework for evaluating the effect of COVID-19 on the restaurant industry with big data

基于GIS的分析框架：利用大数据评估COVID-19对餐饮业的影响

Tailings ponds are critical facilities in the mining industry, and accurate monitoring and management of these ponds are of paramount importance. However, conventional object detection methodologies, including recent advancements, often face significant challenges in addressing the complexities inherent to tailings pond environments. This is particularly due to deficiencies in their loss function design, which can result in protracted convergence times and suboptimal performance when detecting smaller targets. In this study, we introduce an innovative loss function termed the Rapid Intersection over Union (RIoU) loss function, which incorporates a focal weight and is integrated into the YOLOv5 object detection framework to develop the YOLOv5-RF model. This approach aims to enhance both convergence speed and improve convergence accuracy in the tailings pond identification process by comprehensively addressing the specific challenges posed by complex environmental conditions, thereby enhancing the precision and robustness of tailings pond target detection. It integrates the concepts of the central triangle and the aspect ratio of the circumscribed rectangle, assigning specific weights and penalty terms to optimize the model's performance in object detection tasks. We validated the efficacy of YOLOv5-RF through simulation experiments and high-resolution remote sensing images of tailings ponds. The experimental results indicate that RIoU facilitates faster convergence rates. Specifically, YOLOv5-RF achieves accuracy and recall rates that are 2% and 2.1% higher than those of YOLOv5, respectively. Furthermore, it completes 120 iterations in 1.08 hours less time compared to its predecessor model while exhibiting an inference time that is 11.7 ms shorter than that for YOLOv5. These findings suggest that our model significantly enhances processing speed without compromising accuracy levels. This research offers novel technical approaches as well as theoretical support for monitoring tailings ponds using computer vision and remote sensing technologies.

Weiming Zhang

Wenliang Jiang

Qiang Li

Yi Luo

Heng Zhang

Qisong Jiao

Yongsheng Li

Hongbo Jiang

YOLOv5-RF: a deep learning method for tailings pond identification in high-resolution remote sensing images based on improved loss function

YOLOv5-RF：基于改进损失函数的高分辨率遥感影像尾矿库识别深度学习方法

Huangchuang Zhang

Ge Li

Space-time unified coordinate coding model for digital twin cities

数字孪生城市的时空统一坐标编码模型

Danni Yang

Shanshan Yang

Jiaojiao Huang

Shuyu Zhang

Sha Zhang

Jiahua Zhang

Yun Bai

Improving time upscaling of instantaneous evapotranspiration based on machine learning models

基于机器学习模型的瞬时蒸散发时间尺度提升方法改进

Richard M. Law

James Ardo

Using a discrete global grid system for a scalable, interoperable, and reproducible system of land-use mapping

使用离散全球网格系统实现可扩展、互操作和可重现的土地利用制图系统

Sahel Mahdavi

Meisam Amani

Terry Bullock

Steven Beale

Enhanced oceanic fog nowcasting through satellite-based recurrent neural networks

基于卫星的循环神经网络增强海洋雾现预报

Abbreviation	Big. Earth. Data.
Journal Impact	4.40
Quartiles(Global)	REMOTE SENSING(Q2)

ISSN	2096-4471, 2574-5417
h-index	25

Publication Information	Publisher: Taylor and Francis Ltd.，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2017，Proportion of original research papers: 100.00%，Self Citation Rate:2.40%， Gold OA Rate: 98.15%
Journal Fees	$1250Details
Average review cycle	网友分享经验：10 Weeks

Big Earth Data

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GEOSatDB: global civil earth observation satellite semantic database

A GIS-based analytical framework for evaluating the effect of COVID-19 on the restaurant industry with big data

YOLOv5-RF: a deep learning method for tailings pond identification in high-resolution remote sensing images based on improved loss function

Space-time unified coordinate coding model for digital twin cities

Improving time upscaling of instantaneous evapotranspiration based on machine learning models

Space-time unified coordinate coding model for digital twin cities

YOLOv5-RF: a deep learning method for tailings pond identification in high-resolution remote sensing images based on improved loss function

Using a discrete global grid system for a scalable, interoperable, and reproducible system of land-use mapping

Improving time upscaling of instantaneous evapotranspiration based on machine learning models

Enhanced oceanic fog nowcasting through satellite-based recurrent neural networks