Hasil untuk "Land use"

Geethen Singh, Jasper A Slingsby, Tamara B Robinson et al.

Reliable classification of Earth Observation data depends on consistent, up-to-date reference labels. However, collecting new labelled data at each time step remains expensive and logistically difficult, especially in dynamic or remote ecological systems. As a response to this challenge, we demonstrate that a model with access to reference data solely from time step t0 can perform competitively on both t0 and a future time step t1, outperforming models trained separately on time-specific reference data (the gold standard). This finding suggests that effective temporal generalization can be achieved without requiring manual updates to reference labels beyond the initial time step t0. Drawing on concepts from change detection and semi-supervised learning (SSL), the most performant approach, "Common Ground", uses a semi-supervised framework that leverages temporally stable regions-areas with little to no change in spectral or semantic characteristics between time steps-as a source of implicit supervision for dynamic regions. We evaluate this strategy across multiple classifiers, sensors (Landsat-8, Sentinel-2 satellite multispectral and airborne imaging spectroscopy), and ecological use cases. For invasive tree species mapping, we observed a 21-40% improvement in classification accuracy using Common Ground compared to naive temporal transfer, where models trained at a single time step are directly applied to a future time step. We also observe a 10 -16% higher accuracy for the introduced approach compared to a gold-standard approach. In contrast, when broad land cover categories were mapped across Europe, we observed a more modest 2% increase in accuracy compared to both the naive and gold-standard approaches. These results underscore the effectiveness of combining stable reference screening with SSL for scalable and label-efficient multi-temporal remote sensing classification.

Valerio Brunacci, Davide Plozza, Alessio De Angelis et al.

We present a complete infrastructure-less magneto-inductive (MI) localization system enabling a lightweight UAV to autonomously hover, track, and land with centimeter precision on a mobile quadruped robot acting as a dynamic docking pad. This work advances the vision of heterogeneous robot collaboration, where ultra-lightweight flying robots serve as mobile perception agents for ground-based Unmanned Ground Vehicles (UGVs). By extending the sensing horizon and providing complementary viewpoints, the UAVs enhance exploration efficiency and improve the quality of data collection in large-scale, unknown environments. The proposed system aims to complements traditional localization modalities with a compact, embedded, and infrastructure-less magnetic sensing approach, providing accurate short-range relative positioning to bridge the gap between coarse navigation and precise UAV docking. A single lightweight receive coil and a fully embedded estimation pipeline on the UAV deliver 20 Hz relative pose estimates in the UGV's frame, achieving a 3D position root-mean-square error (RMSE) of 5 cm. The system uses real-time estimation and a warm-started solver to estimate the 3D position, which is then fused with inertial and optical-flow measurements in the onboard extended Kalman filter. Real-world experiments validate the effectiveness of the framework, demonstrating significant improvements in UAV--UGV teaming in infrastructure-less scenarios compared to state-of-the-art methods, requiring no external anchors or global positioning. In dynamic scenarios, the UAV tracks and docks with a moving UGV while maintaining a 7.2 cm RMSE and achieving successful autonomous landings.

Julio de la Torre-Vanegas, Miguel Soriano-Garcia, Israel Becerra et al.

Landing safely in crowded urban environments remains an essential yet challenging endeavor for Unmanned Aerial Vehicles (UAVs), especially in emergency situations. In this work, we propose a risk-aware approach that harnesses semantic segmentation to continuously evaluate potential hazards in the drone's field of view. By using a specialized deep neural network to assign pixel-level risk values and applying an algorithm based on risk maps, our method adaptively identifies a stable Safe Landing Zone (SLZ) despite moving critical obstacles such as vehicles, people, etc., and other visual challenges like shifting illumination. A control system then guides the UAV toward this low-risk region, employing altitude-dependent safety thresholds and temporal landing point stabilization to ensure robust descent trajectories. Experimental validation in diverse urban environments demonstrates the effectiveness of our approach, achieving over 90% landing success rates in very challenging real scenarios, showing significant improvements in various risk metrics. Our findings suggest that risk-oriented vision methods can effectively help reduce the risk of accidents in emergency landing situations, particularly in complex, unstructured, urban scenarios, densely populated with moving risky obstacles, while potentiating the true capabilities of UAVs in complex urban operations.

Amber Batool, Faryal Batool, Roohan Ahmed Khan et al.

Quadcopters are versatile aerial robots gaining popularity in numerous critical applications. However, their operational effectiveness is constrained by limited battery life and restricted flight range. To address these challenges, autonomous drone landing on stationary or mobile charging and battery-swapping stations has become an essential capability. In this study, we present NMPC-Lander, a novel control architecture that integrates Nonlinear Model Predictive Control (NMPC) with Control Barrier Functions (CBF) to achieve precise and safe autonomous landing on both static and dynamic platforms. Our approach employs NMPC for accurate trajectory tracking and landing, while simultaneously incorporating CBF to ensure collision avoidance with static obstacles. Experimental evaluations on the real hardware demonstrate high precision in landing scenarios, with an average final position error of 9.0 cm and 11 cm for stationary and mobile platforms, respectively. Notably, NMPC-Lander outperforms the B-spline combined with the A* planning method by nearly threefold in terms of position tracking, underscoring its superior robustness and practical effectiveness.

Suraj Kumar, Aditya Rallapalli, Bharat Kumar GVP

Typical lunar landing missions involve multiple phases of braking to achieve soft-landing. The propulsion system configuration for these missions consists of throttleable engines. This configuration involves complex interconnected hydraulic, mechanical, and pneumatic components each exhibiting non-linear dynamic characteristics. Accurate modelling of the propulsion dynamics is essential for analyzing closed-loop guidance and control schemes during descent. This paper presents a learning-based system identification approach for modelling of throttleable engine dynamics using data obtained from high-fidelity propulsion model. The developed model is validated with experimental results and used for closed-loop guidance and control simulations.

Ardaneswari Dyah Pitaloka Citraresmi, Sri Gunani Partiwi, Ratna Sari Dewi

The creative industry has experienced rapid expansion in emerging economies, substantially contributing to employment and economic growth. However, despite this expansion, understanding how multiple workforce-related factors jointly influence creative performance remains limited. This study’s main contribution is to offer an integrated perspective on how workforce resilience, sustainability, and digital readiness collectively shape the creative output of Micro, Small, and Medium Enterprises (MSMEs). We used a mixed-methods design to collect data through surveys and in-depth interviews with owners and employees to capture insights on adaptability, well-being, and digital competencies. Results derived from Partial Least Squares Structural Equation Modeling (PLS-SEM) reveal that resilient and sustainable workforces positively affect creative performance, with digital readiness as a crucial mediator. This study highlights the importance of digital adoption strategies and workforce preparedness in an evolving industry landscape. Importance-Performance Map Analysis further identifies psychosocial risk management, employee well-being, and workplace safety as high-priority yet underdeveloped areas requiring immediate attention. By clearly articulating how an integrated approach to resilience, sustainability, and digital readiness advances theoretical and practical discourse, this work provides actionable insights for policymakers and MSMEs practitioners seeking to enhance innovation and maintain competitiveness in the face of ongoing digital disruption.

Mijung Kim, Nimrah Ahmed, Kadriye Akdemir et al.

Abstract Students’ reasoning and decision making on complex socioscientific issues are critical for developing scientific literacy for 21st century citizenship. By incorporating a scenario-based approach, this study aims to understand the complexity of students’ decision making on environmental issues: forests and land use. To help students grasp the context of these issues, we developed scenarios reflecting their experiences and understanding of forests within local communities. Through scenario-based surveys, students in Grade 5–6 science classrooms were encouraged to explore diverse stakeholders’ perspectives and articulate their decisions regarding the scenarios. Additionally, students in focus groups participated in semi-structured discussions and interviews. The data collected from the surveys and students’ dialogues were thematically analyzed. The study found that students prioritized environmental concerns, demonstrated skepticism toward politicians’ perspectives, and emphasized righteousness in their decision making. These findings suggest that a holistic approach is essential to engage students’ diverse perspectives in socioscientific and environmental problem solving. However, this also highlights the ongoing challenge of disciplinary boundaries within school curricula and pedagogical practices in science classrooms.

Yanyan Lyu, Yong Wang, Xiaoling Shen

Global climate change poses a serious threat to <i>Torreya grandis</i>, a rare and economically important tree species, making the accurate mapping of its spatial distribution essential for forest resource management. However, extracting forest-growing areas remains challenging due to the limited spatial and temporal resolution of remote sensing data and the insufficient classification capability of traditional algorithms for complex land cover types. This study utilized monthly Sentinel-2 imagery from 2023 to extract multitemporal spectral bands, vegetation indices, and texture features. Following minimum redundancy maximum relevance (mRMR) feature selection, a spatial–spectral fused attention network (SSFAN) was developed to extract the distribution of <i>T. grandis</i> in the Kuaiji Mountain area and to analyze the influence of topographic factors. Compared with traditional deep learning models such as 2D-CNN, 3D-CNN, and HybridSN, the SSFAN model achieved superior performance, with an overall accuracy of 99.1% and a Kappa coefficient of 0.961. The results indicate that <i>T. grandis</i> is primarily distributed on the western, southern, and southwestern slopes, with higher occurrence at elevations above 500–600 m and on slopes steeper than 20°. The SSFAN model effectively integrates spectral–spatial information and leverages a self-attention mechanism to enhance classification accuracy. Furthermore, this study highlights the joint influence of natural factors and human land-use decisions on the distribution pattern of <i>T. grandis.</i> These findings aid precision planting and resource management while advancing methods for identifying tree species.

Fernando Guedan Pecker, Cristian Ramirez Atencia

This research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously, a novel approach not previously explored. The study presents an innovative genetic algorithm-based method for minimizing pollution from fuel combustion during aircraft take-off and landing at airports. This algorithm uniquely integrates the optimization of both landing gates and take-off/landing runways, considering the correlation between engine operation time and pollutant levels. The approach employs advanced constraint handling techniques to manage the intricate time and resource limitations inherent in airport operations. Additionally, the study conducts a thorough sensitivity analysis of the model, with a particular emphasis on the mutation factor and the type of penalty function, to fine-tune the optimization process. This dual-focus optimization strategy represents a significant advancement in reducing environmental impact in the aviation sector, establishing a new standard for comprehensive and efficient airport operation management.

Kun Wang, Zheng Chen, Jun Li

In this paper, we consider a trajectory planning problem arising from a lunar vertical landing with minimum fuel consumption. The vertical landing requirement is written as a final steering angle constraint, and a nonnegative regularization term is proposed to modify the cost functional. In this way, the final steering angle constraint will be inherently satisfied according to Pontryagin's Minimum Principle. As a result, the modified optimal steering angle has to be determined by solving a transcendental equation. To this end, a transforming procedure is employed, which allows for finding the desired optimal steering angle by a simple bisection method. Consequently, the modified optimal control problem can be solved by the indirect shooting method. Finally, some numerical examples are presented to demonstrate and verify the developments of the paper.

Philip Metzger

In the companion paper ("Erosion rate of lunar soil under a landing rocket, part 1: identifying the rate-limiting physics", this issue) an equation was developed for the rate that lunar soil erodes under the exhaust of a landing rocket. That equation has only one parameter that is not calibrated from first principles, so here it is calibrated by the blowing soil's optical density curve during an Apollo landing. An excellent fit is obtained, helping validate the equation. However, when extrapolating the erosion rate all the way to touchdown on the lunar surface, a soil model is needed to handle the increased resistance to erosion as the deeper, more compacted soil is exposed. Relying on models derived from Apollo measurements and from Lunar Reconnaissance Orbiter (LRO) Diviner thermal inertia measurements, only one additional soil parameter is unknown: the scale of increasing cohesive energy with soil compaction. Treating this as an additional fitting parameter results in some degeneracy in the solutions, but the depth of erosion scour in the post-landing imagery provides an additional constraint on the solution. The results show that about 4 to 10 times more soil was blown in each Apollo landing than previously believed, so the potential for sandblasting damage is worse than prior estimates. This also shows that, with further development, instruments to measure the soil erosion during lunar landings can constrain the soil column's density profile complementary to the thermal inertia measurements, providing insight into the landing site's geology.

Zhuang Jia, Wenkai Lu, Meng Zhang et al.

Ground-roll wave is a common coherent noise in land field seismic data. This Rayleigh-type surface wave usually has low frequency, low apparent velocity, and high amplitude, therefore obscures the reflection events of seismic shot gathers. Commonly used techniques focus on the differences of ground-roll and reflection in transformed domain such as $f-k$ domain, wavelet domain, or curvelet domain. These approaches use a series of fixed atoms or bases to transform the data in time-space domain into transformed domain to separate different waveforms, thus tend to suffer from the complexity for a delicate design of the parameters of the transform domain filter. To deal with these problems, a novel way is proposed to separate ground-roll from reflections using convolutional neural network (CNN) model based method to learn to extract the features of ground-roll and reflections automatically based on training data. In the proposed method, low-pass filtered seismic data which is contaminated by ground-roll wave is used as input of CNN, and then outputs both ground-roll component and low-frequency part of reflection component simultaneously. Discriminative loss is applied together with similarity loss in the training process to enhance the similarity to their train labels as well as the difference between the two outputs. Experiments are conducted on both synthetic and real data, showing that CNN based method can separate ground roll from reflections effectively, and has generalization ability to a certain extent.

Mauricio Lima, Katherine Deck, Oliver R. A. Dunbar et al.

Machine learning is playing an increasing role in hydrology, supplementing or replacing physics-based models. One notable example is the use of recurrent neural networks (RNNs) for forecasting streamflow given observed precipitation and geographic characteristics. Training of such a model over the continental United States (CONUS) has demonstrated that a single set of model parameters can be used across independent catchments, and that RNNs can outperform physics-based models. In this work, we take a next step and study the performance of RNNs for river routing in land surface models (LSMs). Instead of observed precipitation, the LSM-RNN uses instantaneous runoff calculated from physics-based models as an input. We train the model with data from river basins spanning the globe and test it using historical streamflow measurements. The model demonstrates skill at generalization across basins (predicting streamflow in catchments not used in training) and across time (predicting streamflow during years not used in training). We compare the predictions from the LSM-RNN to an existing physics-based model calibrated with a similar dataset and find that the LSM-RNN outperforms the physics-based model: a gain in median NSE from 0.56 to 0.64 (time-split experiment) and from 0.30 to 0.34 (basin-split experiment). Our results show that RNNs are effective for global streamflow prediction from runoff inputs and motivate the development of complete routing models that can capture nested sub-basis connections.

Jorge da Paixão Marques Filho, Antônio José Teixeira Guerra, Carla Bernadete Madureira Cruz et al.

Soil erosion is a global issue—with gully erosion recognized as one of the most important forms of land degradation. The purpose of this study is to compare and contrast the outcomes of four machine learning models, Classification and Regression (CART), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM), used for mapping susceptibility to soil gully erosion. The controlling factors of gully erosion in the Piraí Drainage Basin, Paraíba do Sul Middle Valley were analysed by image interpretation in Google Earth and gully erosion samples (n = 159) were used for modelling and spatial prediction. The XGBoost and RF models achieved identical results for the area under the receiver operating characteristic curve (AUROC = 88.50%), followed by the SVM and CART models, respectively (AUROC = 86.17%; AUROC = 85.11%). In all models analysed, the importance of the main controlling factors predominated among Lineaments, Land Use and Cover, Slope, Elevation and Rainfall, highlighting the need to understand the landscape. The XGBoost model, considering a smaller number of false negatives in spatial prediction, was considered the most appropriate, compared to the Random Forest model. It is noteworthy that the XGBoost model made it possible to validate the hypothesis of the study area, for susceptibility to gully erosion and identifying that 9.47% of the Piraí Drainage Basin is susceptible to gully erosion. Furthermore, replicable methodologies are evidenced by their rapid applicability at different scales.

Mofijul Hoq Masum, Mohammad Faridul Alam, Md. Shariful Alam

Corporate governance is one of the key factors in corporate performance for the economy. In particular, for a transition economy, which is on the way of developing economies from the least developing economy, the relevant attributes of corporate governance are a vital issue. This study explores the most important board and ownership attributes that affect corporate performance in a transitional economy. A static panel fixed effects model is used to identify the most significant board and ownership attributes that affect corporate performance. It is found that board independence, board size, inclusion of women on the board, foreign shareholding and institutional shareholding significantly influence corporate performance, whereas executive shareholding has an adverse impact on corporate performance in the context of a transition economy. There is a paradoxical finding representing that although the foreign shareholdings significantly influenced the corporate performance in the transitional economy the inclusion of foreign members on the board has no significant impact on corporate performance. In addition, the government shareholding has no significant role in earning profit. These diversified findings implied that not all corporate governance attributes have the same effect on corporate performance. Based on the outcomes of this study, the regulatory body of the transitional economy can design its corporate governance policy.

Christopher J. Banks, Katherine Simpson, Nicholas Hanley et al.

Financial incentives encourage the plantation of new woodland to increase habitat, biodiversity, carbon sequestration, as a contribution to meeting climate change and biodiversity conservation targets. Whilst these are largely positive effects, it is worth considering that this expansion of woodland can lead to increased presence of wildlife species in proximity to agricultural holdings that may pose an enhanced risk of disease transmission between wildlife and livestock. Wildlife and the provision of a reservoir for infectious disease is particularly important in the transmission dynamics of bovine tuberculosis, the case studied here. In this paper we develop an economic model for predicting changes in land use resulting from subsidies for woodland planting. We use this to assess the consequent impact on wild deer populations in the newly created woodland areas, and thus the emergent infectious disease risk arising from the proximity of new and existing wild deer populations and existing cattle holdings. We consider an area in the South-West of Scotland, having existing woodland, deer populations, and extensive and diverse cattle farm holdings. In this area we find that, with a varying level of subsidy and plausible new woodland creation scenarios, the contact risk between areas of wild deer and cattle increases between 26% and 35% over the risk present with a zero subsidy. This provides a foundation for extending to larger regions and for examining potential risk mitigation strategies, for example the targeting of subsidy in low disease risk areas, or provisioning for buffer zones between woodland and agricultural holdings.

Hang-Sik Park, Hee-Yoo Kang, Myung-Chul Kim et al.

Ayush Gupta, Ahmed Baza, Ekaterina Dorzhieva et al.

The paper focuses on a heterogeneous swarm of drones to achieve a dynamic landing of formation on a moving robot. This challenging task was not yet achieved by scientists. The key technology is that instead of facilitating each agent of the swarm of drones with computer vision that considerably increases the payload and shortens the flight time, we propose to install only one camera on the leader drone. The follower drones receive the commands from the leader UAV and maintain a collision-free trajectory with the artificial potential field. The experimental results revealed a high accuracy of the swarm landing on a static mobile platform (RMSE of 4.48 cm). RMSE of swarm landing on the mobile platform moving with the maximum velocities of 1.0 m/s and 1.5 m/s equals 8.76 cm and 8.98 cm, respectively. The proposed SwarmHive technology will allow the time-saving landing of the swarm for further drone recharging. This will make it possible to achieve self-sustainable operation of a multi-agent robotic system for such scenarios as rescue operations, inspection and maintenance, autonomous warehouse inventory, cargo delivery, and etc.

Metehan Yalçın, Ahmet Alp Kındıroğlu, Furkan Burak Bağcı et al.

Transfer Learning methods are widely used in satellite image segmentation problems and improve performance upon classical supervised learning methods. In this study, we present a semantic segmentation method that allows us to make land cover maps by using transfer learning methods. We compare models trained in low-resolution images with insufficient data for the targeted region or zoom level. In order to boost performance on target data we experiment with models trained with unsupervised, semi-supervised and supervised transfer learning approaches, including satellite images from public datasets and other unlabeled sources. According to experimental results, transfer learning improves segmentation performance 3.4% MIoU (Mean Intersection over Union) in rural regions and 12.9% MIoU in urban regions. We observed that transfer learning is more effective when two datasets share a comparable zoom level and are labeled with identical rules; otherwise, semi-supervised learning is more effective by using the data as unlabeled. In addition, experiments showed that HRNet outperformed building segmentation approaches in multi-class segmentation.

Ryan Stamp

Living-off-the-Land is an evasion technique used by attackers where native binaries are abused to achieve malicious intent. Since these binaries are often legitimate system files, detecting such abuse is difficult and often missed by modern anti-virus software. This paper proposes a novel abuse detection algorithm using raw command strings. First, natural language processing techniques such as regular expressions and one-hot encoding are utilized for encoding the command strings as numerical token vectors. Next, supervised learning techniques are employed to learn the malicious patterns in the token vectors and ultimately predict the command's label. Finally, the model is evaluated using statistics from the training phase and in a virtual environment to compare its effectiveness at detecting new commands to existing anti-virus products such as Windows Defender.