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Abstract The spatiotemporal resolution of diagnostic X-ray images is limited by the erosion and rupture of conventional stationary and rotating anodes of X-ray tubes from extreme density of input power and thermal cycling of the anode material. Conversely, detector technology has developed rapidly. Finer detector pixels demand improved output from brilliant keV-type X-ray sources with smaller X-ray focal spots than today and would be available to improve the efficacy of medical imaging. In addition, novel cancer therapy demands for greatly improved output from X-ray sources. However, since its advent in 1929, the technology of high-output compact X-ray tubes has relied upon focused electrons hitting a spinning rigid rotating anode; a technology that, despite of substantial investment in material technology, has become the primary bottleneck of further improvement. In the current study, an alternative target concept employing a stream of fast discrete metallic microparticles that intersect with the electron beam is explored by simulations that cover the most critical uncertainties. The concept is expected to have far-reaching impact in diagnostic imaging, radiation cancer therapy and non-destructive testing. We outline technical implementations that may become the basis of future X-ray source developments based on the suggested paradigm shift.

Abstract Various successful applications of deep artificial neural networks are effectively facilitated by the possibility to increase the number of layers and neurons in the network at the expense of the growing computational complexity. Increasing computational complexity to improve performance makes hardware implementation more difficult and directly affects both power consumption and the accumulation of signal processing latency, which are critical issues in many applications. Power consumption can be potentially reduced using analog neural networks, the performance of which, however, is limited by noise aggregation. Following the idea of physics-inspired machine learning, we propose here a type of neural network using stochastic resonances as a dynamic nonlinear node and demonstrate the possibility of considerably reducing the number of neurons required for a given prediction accuracy. We also observe that the performance of such neural networks is more robust against the impact of noise in the training data compared to conventional networks.

Abstract Accurate estimation of battery state of health is crucial for effective electric vehicle battery management. Here, we propose five health indicators that can be extracted online from real-world electric vehicle operation and develop a machine learning-based method to estimate the battery state of health. The proposed indicators provide physical insights into the energy and power fade of the battery and enable accurate capacity estimation even with partially missing data. Moreover, they can be computed for portions of the charging profile and real-world driving discharging conditions, facilitating real-time battery degradation estimation. The indicators are computed using experimental data from five cells aged under electric vehicle conditions, and a linear regression model is used to estimate the state of health. The results show that models trained with power autocorrelation and energy-based features achieve capacity estimation with maximum absolute percentage error within 1.5% to 2.5%.

Abstract Ventricular arrhythmias after myocardial infarction (MI) originate from discrete areas within the MI border zone (BZ), identified during functional electrophysiology tests. Accurate sampling of arrhythmogenic sites for ex-vivo study remains challenging, yet is critical to identify their tissue, cellular and molecular signature. In this study, we developed, validated, and applied a targeted sampling methodology based on individualized 3D prints of the human-sized pig heart. To this end, 3D anatomical models of the left ventricle were created from magnetic resonance imaging and fused with biplane fluoroscopy. Regions of interest for sampling were annotated on the anatomical models, from which we created a unique 3D printed cast with custom slits identifying the annotated regions for sampling. The methodology was validated by retrieving ablation lesions created at predefined locations on the anatomical model. We applied the methodology to sample arrhythmia-vulnerable regions after MI during adrenergic stimulation. A pipeline of imaging was developed to create a 3D high-resolution map of each sample, highlighting the complex interplay of cellular organization, and altered innervation in the BZ.

Abstract Understanding Pd effects on NOx storage and release is crucial for designing passive NOx adsorber (PNA) to control NOx emissions during diesel cold-starts. Herein, we report two oxidation states of Pd species on CexZr1-xO2 regulated by metal-support interaction. Pdδ+ (0

Abstract Condensation trail (contrail) formation in an airplane’s wake requires thermodynamics supersaturation and ice nucleation to form visible ice crystals. Here, using a thermodynamic analysis, we evaluate the potential for forming contrails in a carbon-free, ammonia-powered propulsion system compared to conventional planes powered by jet fuel. The analysis calculates the moisture released by fuel into the atmosphere for each one-degree increase in air temperature due to exhaust gas. It then determines if this moisture can saturate the initially undersaturated atmosphere, maintain saturation as temperature rises, and result in supersaturation with respect to ice while leaving enough moisture for a visible cloud to form. With ammonia increases the critical temperature required for supersaturation. Although ammonia does not generate soot particles in the exhaust gas, various aerosols exist in the atmosphere through other sources that can facilitate heterogeneous ice nucleation. Hence, while ammonia’s contrails might not be as dense, they can form at lower altitudes where the air is warmer and endure longer due to the increased water content, which preserves supersaturation for longer as fresh air dilutes the contrail.

Abstract Understanding biological activities in cells or deep tissues requires high-speed three-dimensional (3D) imaging. Substantial progress has been made with the emergence of 3D random-access microscopy. However, current solutions for fast 3D random-access imaging remain complex and costly. Herein we propose a simple, cost-effective, and fast 3D random-access confocal microscopy with remote focusing system. Our system shows isotropic response times across the x, y, and z axes, with a 34-fold improvement in axial response time over traditional piezo stages. We demonstrate its volumetric imaging performance with fluorescent particles and live cells. Furthermore, we validate the 3D random-access imaging capability of this system by continuously monitoring the signals in three different planes, showing a refresh rate of 500 Hz on two different positions in 3D. The simplicity, versatility, and affordability of our system promise widespread applications in research and industry.

Abstract It is not currently possible for an infrared camera to see through a hot window. The window’s own blinding thermal emission prevents objects on the other side from being imaged. Here, we demonstrate a path to overcoming this challenge by coating a hot window with an asymmetrically emitting infrared metasurface whose specially engineered imaginary index of refraction produces an asymmetric spatial distribution of absorption losses in its constituent nanoscale resonators. Operating at 873 K, this metasurface-coated window suppresses thermal emission towards the camera while being sufficiently transparent for thermal imaging, doubling the thermal imaging contrast when compared to a control window at the same temperature

Abstract Surface lattice resonances (SLR) have been demonstrated to enhance the sensitivity and reduce the full width at half maximum (FWHM) of the plasmonic resonances. However, their widespread application in immunoassays has been hindered by limitations of high structural defect sensitivity and fabrication costs. Here, we design a novel three-layer cylindrical SLR array that exhibits high tolerance against structural defects, which would facilitate straightforward fabrication. By integrating metal evaporation and nanoimprint lithography, we demonstrate the replication of the SLR array with exceptional quality. Theoretical simulations indicate that the resonance dips of these arrays exhibit are not sensitive to various structural defects. The experimental results reveal that the FWHM of these arrays can be as low as 5.1 nm while maintaining robust resonance characteristics. Furthermore, we demonstrated the high spectral sensitivity of the SLR array, which enabled the detection of immunoglobulin G (IgG) at concentrations as low as 609 pg/mL. These findings emphasize the potential of the defect-insensitive SLR array as a highly scalable immunoassay platform with exceptional performance.

Abstract Limited to the direct modulation on the surface acoustic wave (SAW) by the rotation, the conventional SAW gyroscopes incur weak Coriolis effects and gyroscopic effects. In this paper, we innovatively utilize a phononic metamaterial (PM) operated at whispering-gallery modes (WGMs) as the vehicle for the Coriolis effect rather than SAW itself. The gyroscopic effects of this PM are investigated, and a new SAW gyroscope is subsequently proposed based on the slow SAW in PM. We show, combining theoretical modeling and finite element method simulation, that the rate of rotation can linearly induce the splitting of WGMs and modulate the phase velocity of SAW down to 4600 m/s (initial phase velocity of 5355 m/s); the direction of rotation results in the chiral symmetry of the PM vibration and the asymmetric distribution of the transmissive SAW. Besides, the proposed SAW gyroscope measures the angular velocity by detecting the phase shift resulting from rotation-dependent slow SAW in PM, obtaining a sensitivity of 0.016 deg/Hz when 50-cell PM. Compared with the existing SAW gyroscopes based on phase velocity modulation, the gyroscopic gain factor in this paper is enhanced by 430–1600 times. This work jumps out of the framework of directly modulating SAW in gyroscopes and provides an innovative scheme of the indirect modulations from the rotation-dependent PM on SAW, showing excellent performance and potential for angular velocity measurement in extreme environments.

Abstract The emergence of cracks in cementitious composites and concrete, stemming from both autogenous processes and external stressors, poses a significant challenge to the long-term durability and safety of structures. In response to this challenge, researchers have developed an innovative method featuring inherent capabilities for autonomous crack repair, known as self-healing concrete, which offers promising benefits for enhancing structural longevity and reducing environmental impact. This method offers not only an autogenous healing mechanism but also the potential for autonomous repair, significantly reducing the need for maintenance while concurrently extending the lifespan of concrete structures. This paper comprehensively reviews the working principles, fabrication techniques, and self-healing capabilities, specifically in relation to crack recovery and mechanical performance, of concrete incorporated with chemical-based self-healing agents. These agents encompass a range of materials, such as sodium silicate, calcium nitrate, dicyclopentadiene, calcium hydroxide, calcium sulfoaluminate (CSA), silica, and superabsorbent polymers. In this review, both autogenous and autonomous self-healing were considered, and literature studies suggested cracks can effectively heal, and mechanical performances can be significantly restored. Healing performance and mechanical performance restoration can vary significantly with the type of healing agents and their concentrations. Many researchers have achieved more than 100% restoration of mechanical performance and fully healed crack depths. Superabsorbent polymers (SAP) are highly effective self-healing agents, particularly when combined with autogenous healing agents like calcium nitrate or CSA. This combination can lead to significant healing in cracks up to 750 μm. Chemical-based self-healing agents can be an efficient way to enhance the durability of concrete, ensuring safety and promoting their practical application.

Abstract The Himalayan region is one of the most seismically active areas in the world due to its tectonic setting. This vulnerability poses significant risks to buildings, resulting in substantial loss of life, property, and infrastructure. This study investigates the seismic performance of a reinforced concrete (RC) low-rise building using the seismic codes of countries situated along the Himalayan belt: China, India, Nepal, and Pakistan. These countries, despite sharing a common geological seismic threat, have developed a distinct code and standards for construction. This inconsistency concerns the effectiveness and suitability of seismic design practices in this region, hence prompting a comparative study. This study begins by reviewing the development of seismic codes in these countries. Subsequently, a prototype RC low-rise building was modeled and analyzed using finite element software, employing the linear static method of analysis according to each country's seismic codes. This study focused on key seismic responses, including the fundamental period, base shear, displacement, and story drift. The analysis revealed significant differences in seismic responses among the codes. Notably, the Chinese seismic code resulted in the highest values for the parameters studied, followed by the Nepalese, Pakistani, and Indian codes. The comparison revealed that the base shear from the Chinese code was the highest, being 3.10 times greater than the Indian code, 1.66 times greater than the Nepalese code, and 1.86 times greater than the Pakistani code. Similar ratios for displacements and story drifts were found in the study.

Abstract Double-deck cable-stayed bridges are appropriate when there is a high traffic volume and little space for the structure. They combine road and rail transit to maximize the utilization of bridge structures and tackle traffic issues. This study investigates the fluctuations in the modal properties caused by several factors that impact the modal characteristics of bridges. The aim is to ascertain the system’s inherent frequencies, associated natural periods, and mode shapes. The modal analysis inquiry was conducted utilizing a software model grounded in the finite element method. The bridge deck slab, cable, pier, and pylon are discretized utilizing SHELL 181, CABLE 280, and BEAM 188 elements in this finite element model. The model’s dependability was validated by mesh convergence analysis and comparison with findings from prior research investigations. The cumulative mass participation factor analysis reveals that the bridge induces vibrations in 90% of the mass associated with the first five modes in the research of cumulative modal vibration. The application of prestressing force has significantly increased the natural frequencies of the bridges, resulting in a large rise. An increase in the damping ratio leads to a reduction in the natural frequency. Several factors, such as the existence or absence of prestressing force and different damping ratios in the bridge system, will affect free vibration results. Additionally, it can provide the foundation for subsequent fatigue analysis related to different dynamic loads.

Abstract Transverse cracking in flexible pavements poses significant challenges to road infrastructure, impacting durability and increasing maintenance costs. This study addresses the lack of predictive models specifically for transverse cracking by employing machine learning techniques. Utilizing data from the long-term pavement performance (LTPP) program, we analyzed the influence of various factors including pavement structure, environmental conditions, and traffic loads. Descriptive statistical analysis revealed a positive skew in the frequency of transverse cracks, while a heatmap correlation matrix identified pavement age, effective asphalt content, and freeze indices as key contributors to cracking. Machine learning models were evaluated, with the Exponential Gaussian Process Regression (GPR) model demonstrating superior predictive accuracy, achieving an R-squared value of 0.70 and RMSE of 17.50, outperforming other models such as the Cubic SVM. Sensitivity analysis emphasized the linear relationship between pavement age and transverse cracking, highlighting the progressive impact of aging on pavement integrity. This research provides a comprehensive and innovative machine learning-based approach to predict transverse cracking, offering valuable insights for pavement management strategies that aim to optimize maintenance schedules and extend pavement service life, particularly in regions with varying environmental conditions.

Abstract Supply chain management has shown to be an aspect of construction that is neglected, yet with great impact on project delivery time frames and the overall cost of construction projects. This research seeks to identify, assess, and propose solutions for the challenges hindering the adoption of digital technologies in construction supply chain management, hence creating a sustainable foundation for practitioners and relevant agencies to leverage the benefits of digital technologies in the management of construction supply chain. The literature review identified challenges of digital technology adoption in construction supply chain management, guiding subsequent research. A structured questionnaire captured demographic data and insights, with 171 industry professionals surveyed. Data analysis, including Likert scale ratings and Relative Impact/Feasibility Index, informed an Impact-Feasibility Matrix. This matrix categorized challenges and strategies into quadrants, aiding prioritization. A conceptual framework emerged, offering actionable insights for addressing adoption challenges. The study identified 14 challenges to digital technology adoption in construction supply chain management, ranked by respondents. These challenges were categorized based on their impact and feasibility, with strategies mapped accordingly. The resulting framework prioritizes High Impact/High Feasibility challenges for action, while Low Impact/Low Feasibility challenges may require further exploration. This strategic approach aims to address key hurdles efficiently, enhancing the adoption of digital technologies in construction supply management. The findings from this study will inform construction practitioners and relevant agencies about the challenges hindering the seamless integration of digital technologies into construction supply chain management and propose feasible strategies for addressing these challenges.

Abstract This study investigates the effects of various fibers and their combinations on the mechanical properties of fiber-reinforced cementitious composites (FRCC). Sixteen distinct mix designs were prepared, each incorporating different types and proportions of fibers, Polyvinyl Alcohol (PVA), Polypropylene (PP), Basalt, and Banana fibers, while maintaining a consistent cement-to-sand ratio of 25% to 75%. The objective was to evaluate how these fibers influence the compressive, flexural, and tensile strengths of the FRCC. The results revealed significant variations in mechanical properties based on fiber type and content. Mixes containing PVA and Basalt fibers demonstrated superior compressive strength, flexural strength, and tensile strength compared to those with other fibers. Notably, hybrid fiber combinations, such as PVA and Basalt or PVA and PP, showed enhanced mechanical performance, indicating the synergistic benefits of combining different fiber types. In contrast, Banana fibers alone were less effective in improving mechanical properties but contributed positively when combined with synthetic fibers. The study highlights the potential of fiber hybridization in optimizing the performance of cementitious composites. The strategic use of PVA and Basalt fibers, both individually and in combination, provides a promising approach for developing high-strength for diverse construction applications. These findings offer valuable insights for future research and the development of advanced composite materials with tailored mechanical properties. Graphical Abstract

Abstract This research explores the advanced application of nano-silica (nS) in concrete, emphasizing its significant role in improving the mechanical strength and longevity of concrete structures. Unlike prior studies on nS in concrete, this work goes beyond evaluating typical performance metrics such as compressive strength, water absorption, air permeability, and drying shrinkage, by also examining the effect on production costs. The study involved detailed experimental procedures, where concrete mixtures were prepared with varying water-to-binder (w/b) ratios of 0.33, 0.45, and 0.55, and nS contents of 0, 1, 3, and 5 percent by binder weight. The results indicate that a 3% nS admixture significantly boosts compressive strength and reduces both water and gas permeability, although it also leads to an increase in drying shrinkage. Furthermore, the cost analysis shows that even a small 1% addition of nS can raise production costs, underscoring the importance of balancing cost-effectiveness with performance improvements when considering nS for concrete applications.

Abstract For its outstanding aerodynamic capabilities, the integrated design of the waverider and inlet is widely accepted in hypersonic aircraft. Three aspects of the integrated design of the waverider and inlet since 2020 are mainly described in this paper. Firstly, the advantages and shortcomings of the conventional integrated design are examined. Then, different layouts of integrated design are proposed, and the aerodynamic performance optimization methods of the waverider, inlet, and their coupling are introduced. Besides, a series of methods for increasing the calculating accuracy of the waverider and inlet are presented, and the focus of attention is to consider the lateral flow problem.

Solar Power Plants (PLTS) have become a very popular energy source and have great potential in reducing dependence on fossil energy sources. The Node MCU ESP8266 is one type of microcontroller that is highly popular in the development of IoT projects. In generating electrical energy, the Internet of Things (IoT) technology has rapidly developed and become one of the most popular technologies in the field of electrical energy. With a PLTS monitoring system using a website, users can monitor the performance of the PLTS in real-time. Solar panels or solar cells are equipment modules that can convert solar energy into electrical energy with DC current. Monitoring is the process of systematically and continuously collecting and analyzing information based on predetermined indicators of activities/programs, allowing corrective actions to be taken for the improvement of the program/activity. The researchers will also discuss the importance of monitoring PLTS, as well as the potential of IoT technology to improve the efficiency and quality of electrical energy production. The website is used to operate IoT principles designed for fast and easy real-time reading. Functional testing is divided into three parts: voltage sensor testing, ACS712 current sensor testing, and NodeMCU testing. The electrical parameters to be measured include output current and output voltage. The PLTS monitoring system device, also known as the Box Panel, has been designed, built, and tested. The testing phase is carried out by conducting functional tests on the system to determine the success rate and accuracy of the device. Once the box panel functions properly, it can be used to monitor the output of the PLTS, and the measurement results will be displayed on the website that has been created.

Load variations found in PT PLN Persero customers, such as resistive, inductive, or capacitive loads, can potentially affect the error rate in kWh meter readings. To determine the error rate of kWh meter readings, it is necessary to compare the reading data from each kWh meter with the measurement data from a 6-in-1 multimeter display. This involves comparing the prepaid digital kWh meter, the postpaid semi-digital kWh meter, and the postpaid analog kWh meter, all assembled in series. Based on the test results, the average reading error rate for resistive loads was found to be 0,11% for the prepaid digital kWh meter, 6,91% for the postpaid semi-digital kWh meter, and 0,36% for the postpaid analog kWh meter. For inductive loads, the average reading error rate was 0,36% for the prepaid digital kWh meter, 7,00% for the postpaid semi-digital kWh meter, and 0,27% for the postpaid analog kWh meter. For capacitive loads, the average reading error rate was 0,09% for the prepaid digital kWh meter, 7,52% for the postpaid semi-digital kWh meter, and 0,62% for the postpaid analog kWh meter. This research is important for the electricity supply industry because the accuracy of electrical energy measurement is a key factor in maintaining operational efficiency and improving tariff fairness for customers.

The reliability index of the distribution system is a measure of the system's ability to continuously supply electrical energy to loads over a specific period and under certain conditions. PT. PLN (Persero) Rayon Samalanga, as the electricity service provider in Bireun Regency, adheres to service standards in accordance with SPLN 59:1985 and the IEEE Reliability Index standard 1366-2003. This research analyzes the reliability level of PT. PLN (Persero) Rayon Samalanga's distribution network on six (6) feeders: feeder SL-1, feeder SL-2, feeder SL-3, feeder SL-4, feeder SL-5, and feeder SL-6. The reliability index calculation is based on the estimated failure rate (λ) and the estimated interruption duration (U) for each installed component. The calculation results show that the distribution lines of PT. PLN (Persero) Rayon Samalanga are categorized as reliable according to SPLN 59:1985 and the IEEE Reliability Index standard 1366-2003, with the highest reliability index on feeder SL-4 at SAIFI = 2.04 times/failure; the best SAIDI value is on feeder SL-3 with SAIDI = 2.06852 hours/year; the best CAIDI value is on feeder SL-3 with CAIDI = 0.21 times/failure; the best MAIFI value is on feeder SL-2 with MAIFI = 14.82 times/customer; the best ASAI value is on feeder SL-3 with ASAI = 0.99999684, and the best ASUI value is on feeder SL-2 with ASUI = 0.0000081

Distributed Generator (DG) is a small-capacity power plant located within the electrical distribution system and is usually placed on buses directly connected to the load. The placement of distributed generators is a technical effort to reduce voltage drops and power losses in the system. Additionally, Power flow analysis is conducted to plan and determine the power levels within an electrical power system. The results in terms of power losses after adding a DG were achieved in the fifth experiment on bus 149, where the system's total active power loss (P) decreased from 720.822 kW to 682.939 kW, and the total reactive power loss (Q) decreased from 530.02 kVar to 405.835 kVar. These results were obtained from power flow calculations using Electrical Transient Analyzer Program (ETAP) software.Therefore, it can be concluded that the electrical network system is operating efficiently. The results show that the more wind turbine generators are added to the buses, the more the voltage drop is reduced. After simulation, the overall voltage drops still meet the standards according to the text report in ETAP.

Electrical energy is essential for the growth and advancement of technological knowledge that continues to increase. Solar cells are devices that convert solar energy through photovoltaic devices. Solar cell public street lighting (PJUBS) uses unlimited and free solar energy, is a feasible and cheap alternative to providing electric lighting. The Islamic center mosque is one of the places of worship for the majority of Muslims in the city of Lhokseumawe using public street lighting to support public security and safety. Installation of lamps for lighting is installed at several points, namely right, left or in the middle of the road, as needed, including flyovers, underpasses, and bridges. Public street lighting that is efficient and environmentally friendly is in accordance with SNI 7391: 2008. In this study, the lamps used in this evaluation study are solar types with a power of 50 Watts. The calculation results show that the illumination produced at the end of the road when using a 50 Watt solar LED lamp is 0.29 lux so that this does not comply with the provisions of BSN SNI 7391: 2008. This condition is influenced by several factors, namely surface temperature, shadows, inclination angle, light intensity, irradiation, and the condition of the panel surface in order to maximize the conversion value of sunlight into electrical energy

Geospatial technology, including remote sensing, Geographic Information Systems (GIS), and Global Navigation Satellite Systems (GNSS), is identified as a key component in disaster management. It enables hazard, risk, and vulnerability mapping, aiding in the development of effective evacuation plans and resource allocation strategies. Moreover, it facilitates the evaluation of the Sustainable Development Goals (SDGs) and can contribute to improving accountability. While geospatial technology has seen rapid development primarily in developed countries, there's a growing need to expand its adoption globally, particularly in developing and vulnerable regions. Capacity building initiatives are highlighted as crucial in achieving this goal, emphasizing the importance of education, training, and infrastructure development. India is a leading example in the application of geospatial technology for disaster risk reduction, with significant integration into various phases of disaster management. The government's commitment and leadership, along with extensive training programs facilitated by institutions like Indian Space Research Organization, have been instrumental in promoting the widespread use of geospatial technology. Furthermore, India's efforts extend beyond its borders, with capacity building initiatives targeted towards the Asia-Pacific region through the Centre for Space Science and Technology Education in Asia and the Pacific (CSSTEAP). These initiatives aim to equip officials with the necessary skills to apply geospatial technology in disaster management within their respective countries. The paper underscores the importance of integrating geospatial technology into DRR strategies to build resilient communities and minimize the impact of disasters. It emphasizes the need for concerted efforts in capacity building to meet the growing demand for skilled professionals and promote evidence-based decision-making in disaster management.

Annually, a considerable quantity of forest is burned on a global scale. Therefore, it is essential to obtain precise and fast information regarding the size of burned regions in order to effectively monitor the adverse consequences of wildfires. The objective of this investigation is to indicate the effectiveness and usefulness of a deep learning (DL) architecture, such as Convolutional Neural Networks (CNNs), in the mapping of areas affected by fire, employing an eXplainable artificial intelligence (XAI) algorithm known as SHapley Additive exPlanations (SHAP) with accuracy evaluation criteria. Furthermore, this paper presents the evaluation of the Çanakkale-Kizilkeçili village wildfire. The research investigated the impacts of a variety of spectral indices, including the normalized burn index (NBR), differentiated normalized burn index (dNBR), Green-Red Vegetation Index (GRVI), simple ratio vegetation index (RVI), and normalized difference vegetation index (NDVI). At the end of the training process, the model achieved a training accuracy of approximately 0.99, with model loss values converging to approximately 0.1. The findings of the burned area identification analysis indicate that by incorporating spectral indices as supplementary information, the CNN model achieved a high level of accuracy, with an overall accuracy of 98.88% and a Kappa Coefficient of 0.98. Additionally, the SHAP technique was employed to gain insights into the output of the models. The feature importances of the spectral bands were determined through the SHAP analysis of the CNN model. Hence, the significance of the auxiliary data generated by the NBR, dNBR, and NDVI indices was identified as being the highest among the original bands and auxiliary data employed in this investigation.

Poplars (Populus sp.), a tree that grows rapidly species, are significant as industrial forest products. The delineation and monitoring of poplar cultivated areas are invaluable for decision-making processes. With the remote sensing technology, accurate detection of poplar planted areas could be determined much faster, more economically, and with minimum labor requirements. The objective of this research is to create a map of poplar plantations in the Sakarya region of Turkey utilizing Worldview-3 satellite imagery. Object-based image analysis (OBIA) through the application of the multi-resolution segmentation method (MRS) was employed to generate image segments, and then three prevailing machine learning algorithms, namely Support Vector Machine (SVM), Random Forest (RF) and Rotation Forest (RotFor) were implemented to produce LULC maps of the study area including 11 landscape features. The most effective and contributing object features that assure high separability between landscape features were determined using a filter-based Chi-square algorithm for the prediction models constructed with SVM, RF, and RotFor classifiers. Results revealed that the SVM classifier achieved the highest overall accuracy (91.73%) with 38 features out of 88 features, about 3% improvement compared to the other algorithms. According to the SHAP analysis, the IHS feature was the most effective one in the constructed RF model, followed by the CI (red edge), NDVI-1 and NDVI-2 vegetation indices.

The paper investigates multidisciplinary education and specialization at the master's level, analyzes assessment methods, and proposes a technique for evaluating geospatial technology education. It emphasises is on pedagogical approaches, and highlights the importance of capacity building to meet the growing demand for skilled professionals in spatial information management. The research provides guidelines for improving course content, structure, and teaching methodologies, aiming to enhance the quality and relevance of geospatial education for both students and stakeholders. This paper is based on online data gathered from 115 universities across the globe, who are imparting geospatial education, campus-based and e-learning, also a survey was conducted of two thousand students. It supported in analysis and suggestion to develop a specific robust technique for assessment of the geospatial technology education and provides the corrective measures to the users of the technique. It proposed a framework aimed at enhancing the course content, organization, and teaching methods substantially. Further an attempt was made to adopt AHP model for investigating the statistical approach. It was aimed to know the actual values and significance of each parameter used for analysis of the study. In order to prepare a common guideline for improving the course structure, course content and teaching methodology, ANFIS architecture (Artificial Neural Fuzzy-Inference System) was adopted. Spatial information represented in the form of thematic maps in relation with the respective attribute data in Geographical Information System (GIS) environment is now used by all spectrum of the society, be it government organizations, industry, and academia. The result of the study has designed the criteria and common guidelines, which may be implemented, would able to help and curtail the challenges of the geospatial education as global vision for Local Action.

Lake Mainit is the fourth largest lake in the Philippines and water is one of the most important natural resources for humans and other living organisms. Sensors on satellites can measure the amount of solar radiation absorbed by surface water at different wavelengths, which can be compared with parameters of water quality. This study explored the use of Landsat 8 Operational Land Imager (OLI) and Geographic Information System (GIS) techniques for assessing the Phosphorus (P) content in the lake. Water quality data of Phosphorus from Environmental Management Bureau (EMB) was utilized to correlate with spectral values. Linear forward regression results yielded significant correlation with band combination B2/B4 with an R2 of 55% for Phosphorus (P). The model was then used to map the P content in the lake and was found out that the concentration ranges from 0.0228768 mg/L to 0.23145 mg/L. The study was effective in correlating P content and the bands of Landsat 8 OLI. In addition, GIS techniques were used in extracting spectral values of bands.

Despite the high susceptibility of the Philippines to natural disasters, there is a significant lack of human resources focused on disaster risk reduction (DRR). To address this, the University of the Cordilleras, in collaboration with the ERASMUS CBHE Plus Program: Geomatics in Disaster Risk Reduction of the European Union, has developed a curriculum for a GeoDRR program. This program is designed to produce professionals with expertise in disaster risk reduction, geomatics, and remote sensing. The curriculum development employed a mixed-methods approach using the ADDIE (Analyze, Design, Develop, Implement, Evaluate) Framework and the DACUM (Developing a Curriculum) method. The ADDIE Framework guided the structured development process, while the DACUM method identified the necessary competencies for students. The resulting UC MGeoDRR curriculum comprises 15 units of Core Courses, 15 units of Professional Courses, 9 units of Electives, and a 6-unit Capstone project. The curriculum is designed to be localized and contextually relevant, integrating DRR into research, innovation, and extension activities. The pilot program is recommended for graduates of related undergraduate programs and DRR professionals and practitioners.

This study investigates the national expansion of urban and artificial coverages in Chile between 2000 and 2022 using remote sensing techniques. The research employed Google Earth Engine and Landsat imagery to classify land use and land cover (LULC) changes. By analyzing annual cloud-free composites and employing random forest algorithms, the study produced detailed LULC maps, focusing on the 'infrastructure' class, which includes urban areas and major roads. Results indicate a significant 33% increase in infrastructure coverage, growing from approximately 380,000 hectares in 2000 to 510,000 hectares in 2022. This expansion was predominantly observed in seven regions, which accounted for 68% of the total infrastructure area and 83% of the national increase. A notable correlation (0.6) between urban growth and population growth was identified, suggesting that urban expansion is largely driven by demographic changes. However, regions with higher population growth exhibited more vertical urban development. The study highlights the complex interplay between urbanization, population dynamics, and regional planning, emphasizing the need for sustainable development strategies to manage urban growth effectively. Validation efforts showed high classification accuracies, with infrastructure class accuracies averaging 82% for the User and 87% for the Producer across the evaluated years.

This study focuses on the development of a people-centred early warning system (EWS) against floods in the Sirba River basin between Niger and Burkina Faso. This densely populated area has witnessed an increase in extreme flooding events in recent years. Several flood forecasting systems in the Sahel exist, although there is no EWS that integrates the four components of people-centred EWSs, namely risk knowledge, monitoring and warning service, dissemination and communication, and response capacity. The proposed EWS, named SLAPIS, includes a risk knowledge component that involves defining four levels of vigilance. Its monitoring and alert component involves a user-friendly web application containing real-time data collected through automatic stations. The EWS communicate seamlessly with the national alert system. The response capacity is strengthened through the creation of a flood zone atlas. In this framework, the EWS integrates significant geoinformatics in preparation of local risk reduction plans and the awareness of local communities. In the SLAPIS case study, multi-temporal classifications were conducted using Sentinel-2 data and high-resolution images (approximately 10 cm) generated through Structure from Motion (SfM) techniques. Digital Terrain Model (DTM) creation for hydraulic model calibration employed a multiscale approach, incorporating GNSS survey data processed via Precise Point Positioning (PPP), HydroSHEDS (approximately 100m resolution), and commercial 10m resolution data. All information was calibrated, harmonised, and integrated into the EWS model, which is accessible via a web platform. Capacity building encompassed direct training and field implementation to streamline the primary EWS generation steps.

The environmental repercussions of climate change triggered substantial alterations in existing ecosystem balances leading to a gradual reduction in biodiversity and the increasing degradation of soil and the built environment. This has a direct effect on agricultural production in particular, leading to increased crop water requirements, the spread of new pests and pathogens that are difficult to manage, and, thus, a general reduction in productivity. Through the analysis of multispectral data remotely sensed with latest generation of optical sensors (RGB, thermal, NIR and multi-spectral cameras), it is possible to recognize the health status and growth stages of crops. In fact, in relation to the amount of radiation reflected in the different bands it is possible to calculate spectral indices, or vegetation indices. The main vegetative indices are NDVI, SAVI, MSAVI2, GNDVI and NDRE, which are calculated by differentially combining the ultraviolet spectrum, the visible range, and the near and mid-infrared band.In this paper the outcomes of a multi-year monitoring of a citrus grove field near Rosolini (Syracuse), in south-eastern Sicily-Italy, are presented. This monitoring activity was conducted to highlight areas of productive greenery under stress, on which to act as a priority with specific agronomic interventions. In parallel with the direct monitoring phases of the citrus grove, the trend of average monthly and annual temperature and precipitation values over the past 24 years was also studied to more specifically contextualize the possible presence of significant variations in climatic trends and relate them to the results returned by the calculation of the indices.

Agriculture faces unprecedented challenges due to the increasing frequency and severity of natural disasters and extreme weather events. Agriculture sector needs resilience to support food production and livelihoods because it is susceptible to cyclones, floods, droughts, soil erosion, pests, and disease outbreaks. Geospatial technology plays a significant role in disaster management for agriculture, offering tools for preparedness, response, and recovery. This study explores the role of geospatial technology in early warning systems and risk assessment for different types of natural disasters that impact agriculture activity. Sustainable farming practices that are essential for resilience include crop diversity, climate-resilience varieties, and adaptation strategies. Capacity building and training are vital for effective geospatial technology utilization, especially in developing countries like India, where infrastructure and technology access may be limited. Tailored capacity-building programs are essential, emphasizing climate-smart practices, sustainable land management, and post-disaster recovery strategies. Access to financial services and insurance schemes enhances resilience by helping farmers cope with losses and recover from disasters. In order to improve resilience and sustainability in agriculture, geospatial decision support systems make it possible to evaluate alternative planning, optimise resource allocation, and implement adaptive management.

Geographic Information System Principles is one of the professional core courses for the students majoring in Geographic Information Science. It plays an important role in providing a theoretical foundation and technical support for subsequent professional courses. The course is offered in the second semester of the freshman year and is the first professional core course for students. There are four key issues that need to be addressed. Firstly, the main content of this course is extensive and the difficulty level is relatively high. Secondly,theory and software operation are disconnected in traditional classes. Thirdly, the traditional teaching mode can not well stimulate students' interest. Fourthly, the traditional teaching and learning evaluation method focuses on results but not process. Hence, we rely on the online teaching platform of our school to carry out Hybrid Instruction Mode, hoping to improve students' learning effect and improve teaching quality. Based on the outcome-based education (OBE) concept, we refine the teaching objectives, and subsequently enhance the teaching materials. Then, we have built a blended teaching mode. The whole teaching process is divided into four parts including "course guidance", "pre-class self-study", "in-class practice", and "post-class research". Online and offline teaching are connected with various activities. Discussions, exercises, reflections, assessments, critical thinking, paper reading and homework are online activities. Guidance, analysis and practice are offline activities. During the teaching process, we also reformed teaching method and carried out ecological civilization and scientific spirit education.We optimized the assessment method using the formative evaluation. The course objectives are almost achieved.

The study presented in this paper aims to contribute to the broader discourse on urban planning challenges by presenting a preliminary walkability assessment for the city of Zagreb, Croatia. Using the open-source OS-WALK-EU walkability assessment tool and open government and data from OSM, we demonstrated how data-based walkability assessment can provide information to support relevant policy making. The assessment considered two walkability user perspectives, one focused on enjoying free time and other on daily obligations (e.g. commuting to work). Preliminary findings indicate that walkability index scores are higher in the city centre and decrease towards the periphery for both user perspectives. Additionally, the results indicate that despite different user perspectives give different walkability scores, these are almost uniform across Zagreb, making all the assessed districts equally attractive for both, enjoying free time and do daily obligations.

Mangrove forests and seagrass meadows, collectively termed as blue carbon ecosystems (BCEs), play a pivotal role in carbon sequestration and climate mitigation. Blue carbon research enables advancing scientific knowledge and gaining insights into their current state, which is crucial for making informed decisions on its conservation and management practices. The Japan International Cooperation Agency (JICA) launched the “Comprehensive Assessment and Conservation of Blue Carbon (BC) Ecosystems and their Services in the Coral Triangle” or BlueCARES to pioneer joint research on blue carbon ecosystems and formulate conservation strategies at local and national levels. BlueCARES aims to establish a robust Blue Carbon Strategy and initiate the establishment of the Blue Carbon Network (BCnet), convening various stakeholders, including government agencies, local government units, academic institutions, and non-governmental organizations. BCnet organizes summits, workshops, and conferences to facilitate collaboration. Through capacity development initiatives, the project empowers stakeholders to actively engage in field surveys, conservation efforts, and the implementation of the Blue Carbon Strategy, even after the project concludes. This paper narrates the capacity-building engagements made by the Geomatics team, leveraging remote sensing and geospatial technologies to foster sustainable blue carbon management practices and ensure the long-term resilience of BCEs in the country.

Understanding the historical trajectory of land use and land cover (LULC) and predicting future alterations is essential to advancing the SDGs. Current researches on LULC in agriculture-dominated cities primarily focuses on the spatiotemporal pattern analysis of carbon storage changes, with relatively little attention given to LULC simulation studies. Moreover, most studies employing cellular automata (CA) models concentrate on urban centers, overlooking the specific sustainable development of agricultural cities. This paper selects Siracusa, Italy, as a case study, incorporating multiple drivers and constructing four development scenarios. The patch-generating land use simulation (PLUS) model is employed to predict the LULC for Siracusa by 2030 and to examine potential transformations under each scenario. Our findings show that 60% of the land in Siracusa is designated as cropland, predominantly situated in flat regions. Our simulation results reveal that urbanization and demographic growth are the main factors driving cropland conversion to urban areas. Different development scenarios exhibit significant variations in future land use structures; the cropland protection scenario emphasizes the stability and sustainability of agricultural development, whereas the economic development scenario highlights the substantial impact of urban and industrial expansion on agricultural land. These insights are instrumental for land use planning and policy-making in Siracusa and other agriculture-centric cities, providing a framework to guide urbanization while promoting agricultural sustainability.

In the digital age, open data has become a critical asset for fostering transparency, accountability, and civic engagement. However, the widespread availability of data has outpaced citizens' ability to effectively utilize it, underscoring the need for data literacy skills, especially among youth. This paper addresses the gap in data literacy by proposing an innovative teaching approach that leverages map-based storytelling to engage young people and enhance their data literacy. By integrating real-world open datasets into interactive visual narratives, this approach aims to captivate young audiences, develop critical thinking skills, and foster a deeper understanding of socio-economic issues. Through a case study of a Datathon project titled “Croatian Youth vs. EU: Who has it better?”, this research demonstrates the efficacy of combining data visualization and storytelling to promote data literacy and civic engagement. The findings highlight the potential of open data initiatives and open-source technologies in empowering youth and enhancing their participation in open government initiatives and policy-making processes.

Access to sanitation remains a major concern in the Philippines due to the increasing population and growing demand for basic needs. This study addresses poor sanitary conditions in slum areas, specifically in BaSECo Compound, Manila, where informal settlers rely on shared toilets. Utilizing GIS and MCDA, the study identifies areas suitable for community toilet facility (CTF) development. The Modified Huff Three-Step Floating Catchment Area (MH3SFCA) method quantified spatial accessibility to existing toilets. The SPAI map shows low accessibility in blocks 15 and New Site, with Gawad Kalinga, Habitat, and Old Site (N) having none. The Analytical Hierarchy Process (AHP) prioritized six criteria, with accessibility being the most significant (33.18%), followed by proximity to water supply (31.76%), communal septic tanks (14.53%), land use (8.2%), flood-prone areas (7.44%), and ground slope (4.89%). The final suitability map identified 14.12% of the 0.6 sq. km. area as highly suitable, including Block 15, Gawad Kalinga, Habitat, and Old Site (N). Ground truth verification visited five candidate sites, which were tested for improved SPAI maps. The inclusion of facilities in Block 15, GK 2, OSN, and H improved accessibility in northern BaSECo. Significant service area improvements for each CTF were observed using Voronoi polygons. This study demonstrates the effectiveness of integrating GIS and MCDA methods for CTF site selection.

To date, basic theoretical and methodological ideas of animated cartography have been formed, as well as general schemes for animated maps compilation. However, the technological issue remains relevant in this domain due to the abundance of modern tools that can be used for compilation of animated maps. Particularly, most of currently implemented cartographic animation methodologies require the use of a variety of specialized software, including geographic information systems (GISs), vector and raster graphical editors, video effects editing software, and others. So the data processing and conversion techniques are extremely complicated in some cased, when data converted from one software platform to another.In our study, we established investigation of popular desktop GISs functionality application as a full-cycle animated cartography environment. In such a context, we discover approaches to improve methodology of animated maps automated compilation. To prototype a full-cycle animation in GIS environment, we developed a set of animation algorithms implemented in QGIS and documented the algorithms using UML (Unified Modeling Language) diagrams.

In the current paper, we discover a case study of medical service areas zoning automation needed to ensure effective operation of phthisiatric service. The study was conducted for the administrative area of Saint Petersburg city (Russia). Originally, the process of phthisiatric service areas zoning bases upon outdated interpretation of medical maintenance of territories, and assumes splitting of living buildings list compiled for some administrative territory. This leads to appearing of different zoning features (service area geometry and topology, workload imbalance, etc.), which impact the quality and effectiveness of medical service. No unified and(or) open source tools for geographically corrected (in the meaning of accounting of the different spatial factors and variables) medical service areas zoning are available now. Our study is focussed onto closing of this lack basing on geospatial techniques and data management methods. To ensure the automated phthisiological medical service areas zoning we elaborated and tested a set of scripts available to be running in QGIS. Elaborated methodology was documented and considered as a possible for implementation into technological chain of Saint Petersburg phthisiatric service.

Geospatial technology involves the use of maps, satellite imagery, and spatial databases to collect, analyze, and visualize geographic data. Understanding the link between blockchain and geospatial technology is crucial as it opens the door to innovation and progress in both fields. “The use of Blockchain as a system for managing and verifying geospatial data necessitates accuracy, integrity, and trustworthiness”(Pengxiang et al 2022). Improving data integrity, promoting collaboration, and revolutionizing applications in the geospatial domain are all possible through the combination of these two technologies. The World Wide Web (WWW) has expanded connections worldwide, but it also makes networks susceptible to multiple attacks from various anonymous sources. Packets of fixed or variable sizes are used for transmitting data across nodes. Data is encrypted using secure algorithms at the application level, then packetized and sent at lower levels in the OSI architecture. The encrypted data can be obtained by an intruder by organizing the data contents of each packet once they have access to all the packets. Intruders may also attempt to break the secured algorithm used by the sender. This sophisticated approach to Data Security is the content and order of data, ensuring that unauthorized individuals are unable to discern sensitive information during data transmission. The primary objective of this paper is to introduce a novel spatial encryption technique designed to enhance the security of data transfers within networks. This paper, founded on a comprehensive literature review, endeavors to propose a research project focused on exploring futuristic solutions, particularly within the post-quantum era.

Soil erosion is a serious environmental concern in Tagoloan River Basin (TRB), a major watershed in Northern Mindanao, Philippines. It leads to soil loss causing detrimental impacts such as decreased soil productivity, nutrient loss, siltation, and water quality degradation among others. These impacts are better understood by estimating the degree of soil loss in the watershed and visualized in a GIS-based and factor-based approach using the Revised Universal Soil Loss Equation (RUSLE) model. Thematic maps of soil loss were generated with factors for rainfall erosivity (R), soil erodibility (K), topographic (LS) consisting of slope length (L) and slope steepness (S), cover management (C), and support practice (P). Factors were calculated separately and multiplied together to develop combined soil loss maps. Results show that TRB has an actual mean annual soil loss of 153.20 tons/hectare/year with 47.15% of the study area having very high to very severe susceptibility to soil loss. Further, a comparison of the potential (RKLS) and actual (RKLSCP) soil loss maps indicates the significance of cover management and support practices to the resulting mean annual soil loss. The present characterized soil loss level maps and its understanding driving forces of soil erosion for the planning of management practices and mitigating environmental hazards in the watershed.

The issue of flooding in the town of Jasaan in Misamis Oriental province poses a threat to 42% of the population. This is more worrisome during the rainy season, especially in the months of October and December were typhoons are at their strongest and passes in proximity to Mindanao island. Residents have reported significant damage to their properties due to flooding, and typhoons like Super Typhoon Odette in December 2021 have worsened the situation when it brought rain while the dike system was dilapedated. The dike systems in the area have been damaged by these natural disasters, and the lack of repair has made the situation worse. Flood hazard mitigation measures have since been implemented. With the frequent occurrence of flooding in the Cabulig River, a validated model is needed to investigate the behavior of the river during strong and significant rainfall events while taking into consideration the current state of the existing dike systems by coupling the modelling systems HEC-HMS and HEC-RAS. Data from the USGS, DOST-PAGASA, DPWH, and Landsat 8 have been used in this study. The results of the Flood Inundation Analysis showed that most of the flooded areas in Barangay Lower Jasaan and Bobontugan are urban areas which exacerbates during heavy rain events. The results of the simulations of the researchers' proposed dike structures shows that it should be extended towards the upstream direction and a minimum dike height of 2.5m is needed with no discontinuities. A 2.5m high dike on the Bobontugan side should also be placed.

Tropical cyclones and typhoons pose significant threats to coastal ecosystems, particularly mangrove forests. Assessing the extent of mangrove damage and monitoring recovery post-disaster is vital for effective conservation and management strategies. Space-based observations have become increasingly common in monitoring forest damage in tropical regions. However, the predominant methods for change detection rely on satellite optical imagery, which faces challenges in humid tropical zones. Technological advancements have led to the development of detection techniques utilizing synthetic aperture radar (SAR) to address cloud cover issues. The study specifically targets Siargao Island Protected Landscape and Seascape (SIPLAS), the largest contiguous forest in the Philippines, from destruction caused by Super typhoon Rai (Odette). The study utilized SAR time-series data, unsupervised log ratio change detection method and Cumulative Sum (CuSum) analysis via Google Earth Engine (GEE). Analysis of Sentinel-1 GRD images found VV polarization had the highest accuracy at 92% and an F-measure of 0.92. VH polarization had 87.5% accuracy and an F-measure of 0.87, while combining VV and VH and VV/VH achieved the same results of 80.5% overall accuracy with an F-measure of 0.82. With that, results indicated that 53% of mangroves in SIPLAS area were damaged, with 38.05% regenerating by 2022 and 56.75% by 2023, notably after September 16, 2022 detected from CuSum analysis using the VV polarization. These findings substantiate the robustness of the data and methodologies employed in monitoring mangrove resilience and supporting conservation initiatives within the SIPLAS area.

The north eastern region of India is characterized by limited physical connectivity and challenging terrain, that requires innovative approaches for effective developmental planning and monitoring using geospatial data and user-centric applications. This paper focuses on leveraging space-based data and applications to address these challenges and enhance decision-making processes in governance. A unified framework for interoperable spatial data sharing has been developed, ensuring that data is easily discoverable, searchable, and consumable through metadata standards and OGC interoperable data services. A dedicated spatial data repository has been established, offering real-time access to over 1400 geospatial datasets and facilitating better decision-making and geo-analytics among diverse user groups. The repository operates on an open-source platform with integrated map viewers and geo-analytics tools, allowing for interactive exploration and visualization of geospatial data. Additionally, 35 user-centric geospatial applications have been operationalized, all developed using open-source geospatial tools and standards. Capacity building programs have been conducted to increase awareness and maximize the utilization of geospatial data services and applications across the region. The platform has boosted utilization of geospatial data services and applications in the region for overall planning and developmental activities for smart governance and societal benefits.

Disseminating up-to-date information on dengue mosquito habitats aided by modern GIS approach would yield benefits to people being informed on the localized developments of the vector and therefore reduce people’s risk of being affected by the disease. This paper discusses the development of this monitoring and sensing mechanism through a Web-based GIS mapping and visualization. It utilizes the environmental, WHO dengue indices and weather information for a case of a city in the Philippines to visualize hotspot areas for habitats of dengue vectors. The paper would aid the government in deploying key assets and supporting DOH’s 5S drive or Search, Self, Seek, Support and Sustain strategy to curtail the spread of the disease. Through this paper, the author intends to develop a localized web-based platform Aedes mosquito vector. Presented in this study provides the data workflow from field observations and the inputs from environmental and meteorological data which has the potential to help enhance the data dissemination as well as the IEC campaign of local health leaders in reducing the mortality and morbidity of people affected by Dengue mosquitoes. This visualization platform includes an easy to set-up system using the available Web-based GIS which can be updated depending on the availability of field data and meteorological observations.

Tree canopy plays an essential role in the biophysical activities in forest environment. During the past two decades, individual tree delineation using high-resolution imagery data has become a hot topic in forest sensing research. Individual Tree Crown (ITC) segmentation methods aim to generate masks that delineating the boundary of each ITC, which supports various tree parameter extractions. Thanks to the rapid development of deep learning, the ITC segmentation methods achieved remarkable improvement. However, existing research suffers from the limited availability of the datasets, and the lack of evaluation standards as well as task-orientated neural networks. In 2024, the International Society of Photogrammetry and Remote Sensing (ISPRS) launched the first International Contest of ITC Segmentation. The contest aims to reveal the state-of-the-art of the ITC method development using high-resolution images, to clarify the remaining barriers and challenges, and to guide further explorations in the field. This paper overviews the contest and reports the initial finding regarding the impact factors of the method performance.

Land cover classification is critical in various fields, including environmental monitoring, urban planning, and ecological assessment, facilitating informed decision-making processes. Traditional land cover classification methods often involve labor-intensive and time-consuming processes, relying on manual intervention and predefined algorithms. The emergence of deep learning techniques, particularly convolutional neural networks (CNNs), offers a promising solution to automate this process, albeit with complexities in implementation. This study addresses the limitations of existing Geographic Information System (GIS) software and plugins by proposing a novel approach utilizing the Pix2Pix architecture, a type of CNN, for automated land cover classification. The proposed Land Classification Plugin (LCP) integrates seamlessly with QGIS, offering an end-to-end solution for generating classified static maps. The methodology involves preprocessing data, utilizing the Pix2Pix model for image segmentation, and post-processing to produce georeferenced outputs. The development of the LCP involved extensive software and hardware configurations, including essential components like GDAL/OGR, PyTorch, and OpenCV. The plugin's architecture comprises a user-friendly interface for region selection, clipping, and classification aided by the Pix2Pix model. A layout manager feature also allows for the creation of composite maps for enhanced visualization. The accuracy assessment of the LCP demonstrated an overall accuracy of 83.40% across diverse land cover classes, indicating its efficacy in classification tasks. The plugin's capabilities offer significant potential for applications in land management, environmental surveillance, and urban planning, revolutionizing current practices in land cover classification within the realm of GIS software.

Coastal blue carbon ecosystems, such as mangrove forests and seagrass meadows, are important for various environmental functions, serving as habitats, food sources, and carbon sinks. Despite their importance, seagrass ecosystems are experiencing global decline due to numerous stressors, including anthropogenic activities. To address this, effective monitoring and management strategies are crucial. Traditional in situ surveys for seagrass monitoring are labor-intensive and time-consuming. Alternatively, remote sensing technologies, particularly satellite imagery, offer a more efficient means of mapping seagrass distribution over large areas. However, accurately assessing seagrass cover at a local scale remains challenging with satellite data alone. Recently, unmanned aerial system (UAS) technology has emerged as a promising approach for seagrass mapping. With its fine resolution and flexibility for image acquisition, UAS can provide more precise estimates of seagrass cover, complementing satellite data. This integration enhances accuracy assessments and expands data availability for training and validation purposes. Under the BlueCARES Project, a drone training program was developed to equip stakeholders with the skills needed for cost-effective UAS-based seagrass mapping. The drone training program encompasses lectures on UAS basics, flight planning, and image processing. Beyond training, collected data are used to create seagrass maps and assess seagrass density. This initiative not only advances seagrass conservation but also fosters collaboration between government agencies, academia, and local communities to achieve sustainable management of blue carbon ecosystems.