Satellite Technology Research Articles

LSL-SS-Net: level set loss-guided semantic segmentation networks for landslide extraction

ABSTRACT A landslide inventory is essential for numerous applications, including disaster prevention and mitigation. The emergence of advanced satellite technologies and the proliferation of extensive satellite imagery have greatly enhanced the role of deep learning-based semantic segmentation networks in the development of comprehensive landslide inventories. In deep learning, especially in semantic segmentation tasks, the loss function is crucial to guide the training of neural networks. However, typical loss functions have several challenges and shortcomings, including difficulty in handling objects of different scales and neglecting spatial correlation. These drawbacks hinder semantic segmentation networks from solely concentrating on accurately predicting landslide categories with rich semantic information. The spatial details (e.g. small landslides and precise landslide boundary information) in the segmentation results may be lost, reducing the accuracy of landslide extraction. Most studies only focus on training and detection in the local region. Consequently, detecting new unexplored region and detrimental to emergency response is challenging. This study presents a level set loss-guided semantic segmentation network, which can be integrated with different semantic segmentation networks, to address these issues. Three out of five study areas (e.g. Bijie, Jiuzhaigou, and Taitung) are used for model training and accuracy evaluation, while the rest, which are newly unexplored regions (e.g. Hokkaido and Haiti), are used to test the transferability of the proposed loss function in different regions. The study results validate the effectiveness of the designed level set loss in various semantic segmentation networks and in different study areas for landslide extraction.

Advancing Renewable Energy Operations with Satellite Technology

Advancing Renewable Energy Operations with Satellite Technology

Examining noncommunicable diseases using satellite imagery: a systematic literature review

IntroductionNoncommunicable diseases (NCDs) are the leading cause of morbidity and mortality worldwide, accounting for 74% of deaths annually. Satellite imagery provides previously unattainable data about factors related to NCDs that overcome limitations of traditional, non-satellite-derived environmental data, such as subjectivity and requirements of a smaller geographic area of focus. This systematic literature review determined how satellite imagery has been used to address the top NCDs in the world, including cardiovascular diseases, cancers, chronic respiratory diseases, and diabetes.MethodsA literature search was performed using PubMed (including MEDLINE), CINAHL, Web of Science, Science Direct, Green FILE, and Engineering Village for articles published through June 6, 2023. Quantitative, qualitative, and mixed-methods peer-reviewed studies about satellite imagery in the context of the top NCDs (cancer, cardiovascular disease, chronic respiratory disease, and diabetes) were included. Articles were assessed for quality using the criteria from the Oxford Centre for Evidence-Based Medicine.ResultsA total of 43 studies were included, including 5 prospective comparative cohort trials, 22 retrospective cohort studies, and 16 cross-sectional studies. Country economies of the included studies were 72% high-income, 16% upper-middle-income, 9% lower-middle-income, and 0% low-income. One study was global. 93% of the studies found an association between the satellite data and NCD outcome(s). A variety of methods were used to extract satellite data, with the main methods being using publicly available algorithms (79.1%), preprocessing techniques (34.9%), external resource tools (30.2%) and publicly available models (13.9%). All four NCD types examined appeared in at least 20% of the studies.ConclusionResearchers have demonstrated they can successfully use satellite imagery data to investigate the world’s top NCDs. However, given the rapid increase in satellite technology and artificial intelligence, much of satellite imagery used to address NCDs remains largely untapped. In particular, with most existing studies focusing on high-income countries, future research should use satellite data, to overcome limitations of traditional data, from lower-income countries which have a greater burden of morbidity and mortality from NCDs. Furthermore, creating and refining effective methods to extract and process satellite data may facilitate satellite data’s use among scientists studying NCDs worldwide.

Semantic Segmentation of Satellite Images for Landslide Detection Using Foreground-Aware and Multi-Scale Convolutional Attention Mechanism.

Advancements in satellite and aerial imagery technology have made it easier to obtain high-resolution remote sensing images, leading to widespread research and applications in various fields. Remote sensing image semantic segmentation is a crucial task that provides semantic and localization information for target objects. In addition to the large-scale variation issues common in most semantic segmentation datasets, aerial images present unique challenges, including high background complexity and imbalanced foreground-background ratios. However, general semantic segmentation methods primarily address scale variations in natural scenes and often neglect the specific challenges in remote sensing images, such as inadequate foreground modeling. In this paper, we present a foreground-aware remote sensing semantic segmentation model. The model introduces a multi-scale convolutional attention mechanism and utilizes a feature pyramid network architecture to extract multi-scale features, addressing the multi-scale problem. Additionally, we introduce a Foreground-Scene Relation Module to mitigate false alarms. The model enhances the foreground features by modeling the relationship between the foreground and the scene. In the loss function, a Soft Focal Loss is employed to focus on foreground samples during training, alleviating the foreground-background imbalance issue. Experimental results indicate that our proposed method outperforms current state-of-the-art general semantic segmentation methods and transformer-based methods on the LS dataset benchmark.

Assessment of geoid models for geopotential values determination in Mexico'S continuous monitoring network

The requirements of the reference systems related to the vertical part are aimed at adopting a global height system that allows taking advantage of the development of satellite technology that provides terrestrial data with higher quality. Due to this need, the International Association of Geodesy (IAG) has proposed the realization and implementation of the International Height Reference System (IHRS), based on geopotential numbers obtained from the gravity potential difference at a point on the Earth's surface (W(P)) referred to the geoid (W0), whose value adopted by convention corresponds to 62636853.4 m2s-2. Nowadays, efforts are underway worldwide to materialize this system. Considering this, the present work entails the analysis of information gathered from SIRGAS continuous monitoring stations in Mexico: MTY2, IDGO, INEG, MERI, and ICAM, to estimate its gravity potential from sufficiently precise geocentric coordinates and absolute gravity values, which when combined with heights derived from geoid models, allows obtaining a modern height system such as the IHRS. The analysis aims to evaluate the feasibility of estimating the gravity potential at the stations, by using precise geocentric coordinates and absolute gravity values which when combined with high-resolution geoid models such as EGM2008, GGM10, EIGEN6C4, XGM2019e_2159, SGGUGM2, and the xGEOID20B, to implement a modern height reference system in Mexico; as well as to contribute to the International Height Reference Framework (IHRF), which in its latest version only includes the MERI station, located in Mérida, Yucatán. Therefore, we consider that for their characteristics the stations analyzed can be included to densify the frame in the implementation of the system. The results show the differences in gravity potential in the different regions of the country, reinforcing the need to consider more reference stations. Additionally, benchmark points close to the selected stations were analyzed to determine the reference potential value of the GGM10, with which it was also possible to analyze the behavior between the observed geoid and the different geoid models, toward the modernization of the official height system, which is based on orthometric heights called NAVD88, which shows us the differences between the geoid observed with the official height system and the analyzed models. Finally, the study examines the consistency of the calculated orthometric heights with geopotential numbers derived from the different geoid models.

APPLICATION OF KEPLER'S LAWS IN PHYSICS

Kepler’s laws of planetary motion provide fundamental insights into the mechanics of celestial bodies. These laws are crucial in understanding orbital mechanics, which play a significant role in modern astrophysics, satellite engineering, and space exploration. This paper presents a comprehensive exploration of the application of Kepler's laws in physics, emphasizing their relevance to both historical and contemporary scientific advancements. The study evaluates the laws' contributions to Newtonian mechanics and modern applications in satellite technology and gravitational theory.

Selective separation and recovery of rare-earth elements (REEs) from acidic solutions and coal fly ash leachate by novel TODGA-Impregnated organosilica media

Rare-earth elements (REEs) are essential in a wide range of applications including magnets and satellite technology, with demand driven by geopolitical factors. While multiple techniques exist for REE recovery including solvent extraction and selective precipitation, solid–liquid extraction offers key advantages in selectivity, scalability, and recyclability. N, N, N′, N′-tetraoctyl diglycolamide (TODGA) is a tridentate ligand widely used in solvent extraction, but the incorporation in a solid support to separate individual lanthanides from acid solutions remains largely unexplored. A novel TODGA-organosilica sorbent material was developed and tested to evaluate recovery of 17 elements (REEs and thorium) in terms of selectivity, kinetics, and equilibrium isotherms in 0.01 to 15.9 M nitric acid. Characterization of the new sorbent was performed before and after sorption using FTIR, XPS, and SSA techniques to help elucidate sorption mechanisms and limiting variables. REE adsorption increased from 1.26 mg g−1 to 10 mg g−1 with nitric acid concentrations of 0.01 M and 15.9 M, respectively. TODGA-organosilica exhibits high selectivity towards heavy REEs (Dy to Lu), with minimal sorption of lighter REEs (Ce to Gd). Batch adsorption experiments confirm pseudo-second-order kinetics and a Langmuir adsorption isotherm with TODGA binding to REEs in a 3:1 complex. A packed bed column study confirmed the high selectivity of TODGA-impregnated organosilica towards heavy REEs and a proof-of concept experiment with coal fly ash leachate shows high selectivity for REEs over Al, Fe, and Ca present in several orders of magnitude higher concentrations. Fractionation was observed where loading cycle effluent contained 84 % light REEs while strip cycle effluent contained 80 % heavy REEs. More than 90 % of loaded REEs were successfully stripped with water as the stripping agent.

Methodological proposal for satellite communication on offshore self-elevating platform

The use of information and communication technologies has expanded the ways of working and communicating with others. However, these offer challenges when people or organizations are in places where communication is difficult. Self-elevating platforms and vessels with oil and fishing activities are in the middle of the sea, and the distance between them and the nearest port is very far. This affects the flow of information and communication that it generates to function properly. The objective of this work is to implement a satellite communication system for an offshore platform using various tools and equipment related to local area networks and satellite networks, specifically VSAT technology. The proposed and used methodology is based on experience and considers site analysis, site design, equipment installation and satellite communication tests. This has been tested in previous satellite link implementations. As a result, the parameters obtained in antenna pointing, isolation and latency tests are adequate and optimal. The applied methodology presents practical steps with specific results that can be replicated or adapted to any satellite technology implementation.

Selected applications of satellite technologies in rail transport

Global Navigation Satellite Systems (GNSS) are increasingly being used in various modes of transport, including rail transport. When this technology is applied to railway traffic control, it is essential to ensure a high level of safety and reliability. The approval of a railway traffic control system requires a safety analysis, which includes hazard analysis and risk analysis. This also includes GNSS-based solutions in terms of their compliance with safety integrity requirements, i.e. THR (Tolerable Hazard Rate) and SIL (Safety Integrity Level) parameters, as defined in normative documents. In the case of railway traffic control systems, the level of dependability of the determined train position, referred to as position integrity, is very important in ensuring safety. Position integrity is affected by many factors, including: errors due to SIS (Signal-In-Space) propagation, multipath errors, signal interference or GNSS receiver errors. In order to improve position integrity, among other things, new data processing methods can be used to improve the accuracy and reliability of measurements. The paper presents the concept of satellite signal processing for precise determination of the position of objects in selected railway systems. The Kalman filter based model of satellite signal filtration and its selected applications, which were tested in the real condition, was presented. The application of Kalman filtration indicated in the paper is a universal method that improves the estimated measurement parameters and can be used in many applications of satellite systems for railway tasks. The applicability of satellite systems to automatic train operation, defect positioning in automatic and manual flaw detection tests, determining track spatial orientation and train integrity control have been considered. The conducted tests confirmed the correctness of the adopted concept and the model of satellite signals filtration developed for this purpose. According to the authors, the described methods can also be used in many other tasks related to rail transport.

Combining KAN with CNN: KonvNeXt’s Performance in Remote Sensing and Patent Insights

Rapid advancements in satellite technology have led to a significant increase in high-resolution remote sensing (RS) images, necessitating the use of advanced processing methods. Additionally, patent analysis revealed a substantial increase in deep learning and machine learning applications in remote sensing, highlighting the growing importance of these technologies. Therefore, this paper introduces the Kolmogorov-Arnold Network (KAN) model to remote sensing to enhance efficiency and performance in RS applications. We conducted several experiments to validate KAN’s applicability, starting with the EuroSAT dataset, where we combined the KAN layer with multiple pre-trained CNN models. Optimal performance was achieved using ConvNeXt, leading to the development of the KonvNeXt model. KonvNeXt was evaluated on the Optimal-31, AID, and Merced datasets for validation and achieved accuracies of 90.59%, 94.1%, and 98.1%, respectively. The model also showed fast processing speed, with the Optimal-31 and Merced datasets completed in 107.63 s each, while the bigger and more complicated AID dataset took 545.91 s. This result is meaningful since it achieved faster speeds and comparable accuracy compared to the existing study, which utilized VIT and proved KonvNeXt’s applicability for remote sensing classification tasks. Furthermore, we investigated the model’s interpretability by utilizing Occlusion Sensitivity, and by displaying the influential regions, we validated its potential use in a variety of domains, including medical imaging and weather forecasting. This paper is meaningful in that it is the first to use KAN in remote sensing classification, proving its adaptability and efficiency.

Estimation of Contact Time Among Animals from Telemetry Data

Continuous processes in most applications are measured discretely with error. This complicates the task of detecting intersections and the number of intersections between two continuous processes (i.e., when the processes have the same value). Intersections of continuous processes are scientifically important, but challenging to estimate from data. For example, in the field of animal ecology, intersections of the paths of moving animals tracked with satellite technologies can be used to understand disease transmission. We illustrate how to quantify contact between animals using telemetry data (i.e., the recorded locations of an animal over time). We introduce our method to quantify contact time with accessible concepts from introductory stochastic process literature, such as Brownian motion. Then, we provide two data examples using white-tailed deer (Odocoileus virginianus) and mule deer (Odocoileus hemionus) telemetry data in a region with high prevalence of chronic wasting disease. Our work provides a needed connection between existing model-based literature for animal movement and rule-based literature for animal interaction. Further, our work illustrates a unique statistical problem receiving minimal attention with broad applicability in human and livestock tracking.

Patent output and patent transfer network in China universities: Moderating roles of absorptive capacity and desorptive capacity

Although absorptive capacity and desorptive capacity has been widely present in university-industry relationships, there has been no research on their potential impact on the relationship between university patent output and university patent transfer network. We constructed datasets of patent applications and patent transfers to firms from 383 universities between 2002 and 2021 in China, and empirically demonstrated the effect of the patent output on university patent transfer network using negative binomial regression models. We also examined the moderating effects of absorptive capacity and desorptive capacity. The results reveal that university patent output positively affects the size and the connection strength of the university patent transfer network. Technology transfer offices and satellite institutions, for desorptive capacity in universities, negatively moderate the relationship between the university patent output and the university patent transfer network, respectively. For absorptive capacity in firms, research and development intensity of new products and the level of research and development personnel input, respectively, have a positively moderate effect on the relationship between the university patent output and the university patent transfer network. Our research provides insights into the dynamics of the university patent transfer network; especially against the backdrop of a sharp increase in university patent output and insufficient development of the university patent transfer network, this study provides evidence to the substitution effect of desorptive capacity in universities and the bidirectional effect of absorptive capacity in firms.

A machine-learning enabled digital-twin framework for next generation precision agriculture and forestry

This work utilizes the modern synergy between flexible, rapid, simulations and quick assimilation of data in order to develop next-generation tools for precise biomass management of large-scale agricultural and forestry systems. Additionally, when integrated with satellite and drone-based digital elevation technologies, the results lead to digital replicas of physical systems, or so-called digital-twins, which offer a powerful framework by which to optimally manage agricultural and forestry assets. Specifically, this enables the investigation of inverse problems seeking to ascertain ideal parameter combinations, such as the number of plants/trees, plant/tree spacing, light intensity, water availability, soil resources, available planting surface area, initial seedling size, genetic variation, etc. to obtain optimal system performance. Towards this goal, a digital-twin framework is developed, consisting of a rapid computational physics engine to simulate an agricultural installation, containing thousands of growing, interacting, plants/trees. This model is then driven by a machine-learning algorithm to obtain optimal parameter sets that match observed statistical representations of a time series of growing agricultural canopy surfaces, measured by digital elevation models. Model simulations are provided to illustrate the approach and to show how such a tool can be used for large-scale biomass management.

MsFNet: Multi-Scale Fusion Network Based on Dynamic Spectral Features for Multi-Temporal Hyperspectral Image Change Detection

With the development of satellite technology, the importance of multi-temporal remote sensing (RS) image change detection (CD) in urban planning, environmental monitoring, and other fields is increasingly prominent. Deep learning techniques enable a profound exploration of the intrinsic features within hyperspectral (HS) data, leading to substantial enhancements in CD accuracy while addressing several challenges posed by traditional methodologies. However, existing convolutional neural network (CNN)-based CD approaches frequently encounter issues during the feature extraction process, such as the loss of detailed information due to downsampling, which hampers a model’s ability to accurately capture complex spectral features. Additionally, these methods often neglect the integration of multi-scale information, resulting in suboptimal local feature extraction and, consequently, diminished model performance. To address these limitations, we propose a multi-scale fusion network (MsFNet) which leverages dynamic spectral features for effective multi-temporal HS-CD. Our approach incorporates a dynamic convolution module with spectral attention, which adaptively modulates the receptive field size according to the spectral characteristics of different bands. This flexibility enhances the model’s capacity to focus on critical bands, thereby improving its ability to identify and differentiate changes across spectral dimensions. Furthermore, we develop a multi-scale feature fusion module which extracts and integrates features from deep feature maps, enriching local information and augmenting the model’s sensitivity to local variations. Experimental evaluations conducted on three real-world HS-CD datasets demonstrate that the proposed MsFNet significantly outperforms contemporary advanced CD methods in terms of both efficacy and performance.

Capability of space borne multispectral image for detecting discoloration in optically complex coastal waters

Coastal pollutants, from harmful algal blooms, sewage and industrial discharges, pose severe risks to marine ecosystems and public health. Recently, Promenade Beach in Puducherry, Southeast-India, experienced reddish-brown water discoloration, suspected to result from either algal blooms or suspended matter. This study monitored the spatial extent and characteristics of the discoloration using Sentinel-2 satellite images from September to November 2023, with field observations and laboratory analyses. Analyses included measurements of chlorophyll-a (Chl-a), Total Suspended Matter (TSM), and the Normalized Difference Chlorophyll Index (NDCI) to differentiate between algal blooms and other pollutants. The satellite data indicated extents of discoloration, with high TSM concentrations (>45 g/m3) and negative NDCI values suggesting absence of algal blooms. No mortality of aquatic organisms was observed during this discoloration, indicating no deleterious impact on aquatic life. This approach highlights the importance of combining satellite technology with field data for effective coastal pollution monitoring, essential for protecting marine ecosystems.

Four-parameter beta mixed models with survey and sentinel 2A satellite data for predicting paddy productivity

Ensuring food security, fostering agricultural sustainability, and driving economic development. However, existing prediction models often overlook the unique characteristics of paddy productivity distribution, which varies between areas, skewed, and bounded within a certain minimum and maximum range, following a four-parameter beta distribution. Consequently, these models yield less accurate and potentially misleading predictions. Additionally, most approaches fail to capture the complex interrelationships among variables that often occur when we incorporate satellite data alongside survey data that has been recognized as a key approach for improving prediction accuracy and optimizing farming practices. To address these shortcomings, this study introduces a four-parameter beta Generalized Linear Mixed Model (GLMM) augmented within a four-parameter beta Generalized Mixed Effect Tree (GMET). The four-parameter beta GMET, an extension of the four-parameter beta GLMM model integrated with a regression tree, offers enhanced flexibility in modeling complex relationships. Application of this methodology to an empirical study in Central Kalimantan and Karawang reveals notable improvements over previous methods, as evidenced by substantially lower AIC and RRMSE values. Notably, the analysis identifies lagged values of band 4, band 8, and NDVI from Sentinel-2A satellite data as significant predictors of paddy productivity, overriding the importance of farmer survey variables. This underscores the potential of satellite data to be utilized in paddy productivity predictions, offering a more efficient and cost-effective alternative to farmer survey-based methods. By enhancing satellite technology, future efforts in paddy productivity prediction can achieve higher efficiency and accuracy, contributing to informed decision-making in agricultural management.

Recent Developments in Satellite Remote Sensing for Air Pollution Surveillance in Support of Sustainable Development Goals

Air pollution is an integral part of climatic, environmental, and socioeconomic current affairs and a cross-cutting component of certain United Nations Sustainable Development Goals (SDGs). Hence, reliable information on air pollution and human exposure is a crucial element in policy recommendations and decisions. At the same time, Earth Observation is steadily gaining confidence as a data input in the calculation of various SDG indicators. The current paper focuses on the usability of modern satellite remote sensing in the context of SDGs relevant to air quality. We introduce the socioeconomic importance of air quality and discuss the current uptake of geospatial information. The latest developments in Earth Observation provide measurements of finer spatial, temporal, and radiometric resolution products with increased global coverage, long-term continuation, and coherence in measurements. Leveraging on the two latest operational satellite technologies available, namely the Sentinel-5P and the Geostationary Environment Monitoring Spectrometer (GEMS) missions, we demonstrate two potential operational applications for quantifying air pollution at city and regional scales. Based on the two examples and by discussing the near-future anticipated geospatial capabilities, we showcase and advocate that the potential of satellite remote sensing as a, complementary to ground station networks, source of air pollution information is gaining confidence. As such, it can be an invaluable tool for quantifying global air pollution and deriving robust population exposure estimates.

CANSat-IDS: An adaptive distributed Intrusion Detection System for satellites, based on combined classification of CAN traffic

The increasing dependence on satellite technology for critical applications, such as telecommunications, Earth observation, and navigation, underscores the need for robust security measures to safeguard these assets from potential cyber threats. Moreover, as many satellite systems rely on the Controller Area Network (CAN) protocol for efficient data exchange among onboard subsystems, they become prime targets for cyberattacks. While contributions present various options for detecting attacks in the CAN bus, no one proposes an architecture suitable for satellite systems. To address this concern, this paper presents a novel approach to develop an adaptive distributed Intrusion Detection System (IDS) for satellites, which integrates machine and deep learning techniques for the classification of CAN frames. This system is specifically designed to overcome the inherent power and computational challenges of satellite operations by executing time-based anomaly detection on board, and content-based detection at the ground segment. To evaluate the effectiveness of the proposed solution, experiments are conducted using representative Datasets. The obtained results demonstrate that the distributed IDS presented in this research offers a promising solution to improve the security of satellite systems by achieving high detection rates ranging from 91.12% to 99.86% (F1-score).

Operational Tests for Delay-Tolerant Network between the Moon and Earth Using the Korea Pathfinder Lunar Orbiter in Lunar Orbit

The Korea Pathfinder Lunar Orbiter (KPLO) was launched on 5 August 2022, equipped on the SpaceX Falcon 9 launch vehicle. At present, the KPLO is effectively carrying out its scientific mission in lunar orbit. The KPLO serves as a cornerstone for the development and validation of Korean space science and deep space technology. Among its payloads is the DTNPL, enabling the first-ever test of delay-tolerant network (DTN) technology for satellites in lunar orbit. DTN technology represents a significant advancement in space communication, offering stable communication capabilities characterized by high delay tolerance, reliability, and asymmetric communication speeds—a necessity for existing satellite and space communication systems to evolve. In this paper, we briefly give an overview of the Korea Lunar Exploration Program (KLEP) and present scientific data gathered through the KPLO mission. Specifically, we focus on the operational tests for DTN-ION conducted for message and file transfer, as well as real-time video streaming, during the initial operations of the KPLO. Lastly, this study offers insights and lessons learned from KPLO DTNPL operations, with the goal of providing valuable guidance for future advancements in space communication.

Utilization of Satellite Technologies for Agriculture

Utilization of Satellite Technologies for Agriculture