Hasil untuk "Land use"

Jiaohong Yao, Linfeng Liang, Yao Deng et al.

Marker-based landing is widely used in drone delivery and return-to-base systems for its simplicity and reliability. However, most approaches assume idealized landing site visibility and sensor performance, limiting robustness in complex urban settings. We present a simulation-based evaluation suite on the AirSim platform with systematically varied urban layouts, lighting, and weather to replicate realistic operational diversity. Using onboard camera sensors (RGB for marker detection and depth for obstacle avoidance), we benchmark two heuristic coverage patterns and a reinforcement learning-based agent, analyzing how exploration strategy and scene complexity affect success rate, path efficiency, and robustness. Results underscore the need to evaluate marker-based autonomous landing under diverse, sensor-relevant conditions to guide the development of reliable aerial navigation systems.

Shuang Chen, Jie Wang, Shuai Yuan et al.

The rapid evolution of satellite-borne Earth Observation (EO) systems has revolutionized terrestrial monitoring, yielding petabyte-scale archives. However, the immense computational and storage requirements for global-scale analysis often preclude widespread use, hindering planetary-scale studies. To address these barriers, we present Embedded Seamless Data (ESD), an ultra-lightweight, 30-m global Earth embedding database spanning the 25-year period from 2000 to 2024. By transforming high-dimensional, multi-sensor observations from the Landsat series (5, 7, 8, and 9) and MODIS Terra into information-dense, quantized latent vectors, ESD distills essential geophysical and semantic features into a unified latent space. Utilizing the ESDNet architecture and Finite Scalar Quantization (FSQ), the dataset achieves a transformative ~340-fold reduction in data volume compared to raw archives. This compression allows the entire global land surface for a single year to be encapsulated within approximately 2.4 TB, enabling decadal-scale global analysis on standard local workstations. Rigorous validation demonstrates high reconstructive fidelity (MAE: 0.0130; RMSE: 0.0179; CC: 0.8543). By condensing the annual phenological cycle into 12 temporal steps, the embeddings provide inherent denoising and a semantically organized space that outperforms raw reflectance in land-cover classification, achieving 79.74% accuracy (vs. 76.92% for raw fusion). With robust few-shot learning capabilities and longitudinal consistency, ESD provides a versatile foundation for democratizing planetary-scale research and advancing next-generation geospatial artificial intelligence.

Gizem Dinç

Recreation and leisure activities have become vital components for enhancing social welfare and overall quality of life in contemporary urban and rural environments. Within this context, natural areas play a crucial role in strengthening social interaction, maintaining ecological resilience, and supporting public health. The growing interest in nature-based recreation highlights the need for design strategies that integrate ecological preservation with user-oriented functionality. This study develops a landscape design model explicitly grounded in Nature-Based Solutions (NbS), positioning NbS as the core conceptual and methodological framework of the project. Conducted in the Pınarbaşı Public Garden located in the Şarkikaraağaç district of Isparta, Türkiye, the research demonstrates how NbS principles can be operationalized in ecological landscape design to address environmental, social, and functional needs simultaneously. The design process was structured around a comprehensive analysis of topography, vegetation, hydrology, land use, and social dynamics. The proposed design integrates ecological services such as shading, water management, carbon sequestration, and soil protection with multifunctional recreational facilities including picnic areas, bicycle paths, wooden bridges, and local product stands. Sustainable materials, minimal intervention strategies, and universal accessibility standards were prioritized throughout the design process. The findings demonstrate that NbS-based landscape design enhances ecological continuity, supports local identity, and strengthens the interaction between humans and nature. This case study offers a replicable model for developing resilient recreational landscapes that contribute to environmental sustainability and community well-being.

Xiaoyan Tang, Funan Liu, Xinling Hu et al.

Ecological carrying capacity (ECC) is central to assessing the sustainability of ecosystems, aiming to quantify the limits of natural systems to support human activities while maintaining biodiversity and resource regeneration. To assess ECC, earlier studies typically used the analytic hierarchy process (AHP) method for modeling. In this study, we developed an AHP-EW method based on a combination of AHP and the entropy weight method, which considered important indicators including land use, vegetation, soil, location, topography, climate, and socio-economics, and constructed an ECC evaluation system. The new AHP-EW method was applied to analyze the spatiotemporal ECC patterns in Urumqi from 2000 to 2020. The results showed a general decreasing trend in ECC during the period 2000–2020. Among them, the ECC decreased significantly by 19.05% from 2000 to 2010. After 2010, the rate of decline in ECC slowed to 14.12% due to ecological conservation policies. In addition, Midong District, Dabancheng District, and Urumqi County had worse ECC. Still, in general, the distribution of ECC in each district and county showed a trend of decreasing in areas with low ECC and increasing in areas with high ECC. Cluster analysis showed that ECC improved in ecological reserve areas, while some built-up areas showed a decrease in ECC due to economic development and human activities. Driving factor analysis shows that NDVI, climate change, and land-use conversion are the key factors influencing the change in ECC in Urumqi. This study provides new ideas and technical support for ECC assessment in arid areas, which can help formulate more effective ecological protection strategies and promote the healthy and stable development of regional ecosystems.

Maximillian Fainkich, Kiril Solovey, Anna Clarke

Most skydiving accidents occur during the parafoil-piloting and landing stages and result from human lapses in judgment while piloting the parafoil. Training of novice pilots is protracted due to the lack of functional and easily accessible training simulators. Moreover, work on parafoil trajectory planning suitable for aiding human training remains limited. To bridge this gap, we study the problem of computing safe trajectories for human-piloted parafoil flight and examine how such trajectories fare against human-generated solutions. For the algorithmic part, we adapt the sampling-based motion planner Stable Sparse RRT (SST) by Li et al., to cope with the problem constraints while minimizing the bank angle (control effort) as a proxy for safety. We then compare the computer-generated solutions with data from human-generated parafoil flight, where the algorithm offers a relative cost improvement of 20\%-80\% over the performance of the human pilot. We observe that human pilots tend to, first, close the horizontal distance to the landing area, and then address the vertical gap by spiraling down to the suitable altitude for starting a landing maneuver. The algorithm considered here makes smoother and more gradual descents, arriving at the landing area at the precise altitude necessary for the final approach while maintaining safety constraints. Overall, the study demonstrates the potential of computer-generated guidelines, rather than traditional rules of thumb, which can be integrated into future simulators to train pilots for safer and more cost-effective flights.

Nicholas Kraabel, Jiangtao Liu, Yuchen Bian et al.

Managing natural resources and mitigating risks from floods, droughts, wildfires, and landslides require models that can accurately predict climate-driven land-surface responses. Traditional models often struggle with spatial generalization because they are trained or calibrated on limited observations and can degrade under concept drift. Recently proposed vision foundation models trained on satellite imagery demand massive compute, and they are not designed for dynamic land surface prediction tasks. We introduce StefaLand, a generative spatiotemporal Earth representation learning model centered on learning cross-domain interactions to suppress overfitting. StefaLand demonstrates especially strong spatial generalization on five datasets across four important tasks: streamflow, soil moisture, soil composition and landslides, compared to previous state-of-the-art methods. The domain-inspired design choices include a location-aware masked autoencoder that fuses static and time-series inputs, an attribute-based rather than image-based representation that drastically reduces compute demands, and residual fine-tuning adapters that strengthen knowledge transfer across tasks. StefaLand can be pretrained and finetuned on commonly available academic compute resources, yet consistently outperforms state-of-the-art supervised learning baselines, fine-tuned vision foundation models and commercially available embeddings, highlighting the previously overlooked value of cross-domain interactions and providing assistance to data-poor regions of the world.

Xinglun Chen, Ran Zhang, Huifeng Li

This article is an engineering note, and formal abstract is omitted in accordance with the requirements of the journal. The main idea of this note is as follows. In endoatmospheric landing of reusable rockets, there exist various kinds of disturbances that can induce the trajectory dispersion. The trajectory dispersion propagates with flight time and ultimately determines landing accuracy. Therefore, to achieve high-precision landing, this note proposes a novel online trajectory dispersion control method. Based on a Parameterized Optimal Feedback Guidance Law (POFGL), two key components of the proposed method are designed: online trajectory dispersion prediction and real-time guidance parameter tuning for trajectory dispersion optimization. First, by formalizing a parameterized probabilistic disturbance model, the closed-loop trajectory dispersion under the POFGL is predicted online. Compared with the covariance control guidance method, a more accurate trajectory dispersion prediction is achieved by using generalized Polynomial Chaos (gPC) expansion and pseudospectral collocation methods. Second, to ensure computational efficiency, a gradient descent based real-time guidance parameter tuning law is designed to simultaneously optimize the performance index and meet the landing error dispersion constraint, which significantly reduces the conservativeness of guidance design compared with the robust trajectory optimization method. Numerical simulations indicate that the trajectory dispersion prediction method can achieve the same accuracy as the Monte Carlo method with smaller computational resource; the guidance parameter tuning law can improve the optimal performance index and meet the desired accuracy requirements through directly shaping the trajectory dispersion.

J. B. Marston, Daniel E. Ibarra

When large surface areas of the Earth are altered, radiative forcing due to changes in surface reflectance can drive climate change. Yet to achieve the necessary scale to remove the substantial amounts of carbon dioxide from the atmosphere relevant for ameliorating climate change, enhanced rock weathering (ERW) will need to be applied to very large land areas. Likewise, marine carbon dioxide removal (mCDR) must alter a large fraction of the ocean surface waters to have a significant impact upon climate. We show that surface albedo modification (SAM) associated with ERW or mCDR can easily overwhelm the radiative forcing from the decrease of atmospheric CO2 over years or even decades. A change in albedo as small as parts per thousand has a radiative impact comparable to the removal of 10 tons of carbon per hectare. SAM via ERW can be either cooling or warming. We identify some of the many questions raised by radiative forcing due to these forms of CDR.

Piyu Ke, Philippe Ciais, Yitong Yao et al.

The high growth rate of atmospheric CO2 in 2023 was found to be caused by a severe reduction of the global net land carbon sink. Here we update the global CO2 budget from January 1st to July 1st 2024, during which El Niño drought conditions continued to prevail in the Tropics but ceased by March 2024. We used three dynamic global vegetation models (DGVMs), machine learning emulators of ocean models, three atmospheric inversions driven by observations from the second Orbiting Carbon Observatory (OCO-2) satellite, and near-real-time fossil CO2 emissions estimates. In a one-year period from July 2023 to July 2024 covering the El Niño 2023/24 event, we found a record-high CO2 growth rate of 3.66~$\pm$~0.09 ppm~yr$^{-1}$ ($\pm$~1 standard deviation) since 1979. Yet, the CO2 growth rate anomaly obtained after removing the long term trend is 1.1 ppm~yr$^{-1}$, which is marginally smaller than the July--July growth rate anomalies of the two major previous El Niño events in 1997/98 and 2015/16. The atmospheric CO2 growth rate anomaly was primarily driven by a 2.24 GtC~yr$^{-1}$ reduction in the net land sink including 0.3 GtC~yr$^{-1}$ of fire emissions, partly offset by a 0.38 GtC~yr$^{-1}$ increase in the ocean sink relative to the 2015--2022 July--July mean. The tropics accounted for 97.5\% of the land CO2 flux anomaly, led by the Amazon (50.6\%), central Africa (34\%), and Southeast Asia (8.2\%), with extra-tropical sources in South Africa and southern Brazil during April--July 2024. Our three DGVMs suggest greater tropical CO2 losses in 2023/2024 than during the two previous large El Niño in 1997/98 and 2015/16, whereas inversions indicate losses more comparable to 2015/16. Overall, this update of the low latency budget highlights the impact of recent El Niño droughts in explaining the high CO2 growth rate until July 2024.

Runze Zhang, Ishfaq Aziz, Derek Houtz et al.

High-resolution soil moisture (SM) observations are critical for agricultural monitoring, forestry management, and hazard prediction, yet current satellite passive microwave missions cannot directly provide retrievals at tens-of-meter spatial scales. Unmanned aerial vehicle (UAV) mounted microwave radiometry presents a promising alternative, but most evaluations to date have focused on agricultural settings, with limited exploration across other land covers and few efforts to quantify retrieval uncertainty. This study addresses both gaps by evaluating SM retrievals from a drone-based Portable L-band Radiometer (PoLRa) across shrubland, bare soil, and forest strips in Central Illinois, U.S., using a 10-day field campaign in 2024. Controlled UAV flights at altitudes of 10 m, 20 m, and 30 m were performed to generate brightness temperatures (TB) at spatial resolutions of 7 m, 14 m, and 21 m. SM retrievals were carried out using multiple tau-omega-based algorithms, including the single channel algorithm (SCA), dual channel algorithm (DCA), and multi-temporal dual channel algorithm (MTDCA). A Bayesian inference framework was then applied to provide probabilistic uncertainty characterization for both SM and vegetation optical depth (VOD). Results show that the gridded TB distributions consistently capture dry-wet gradients associated with vegetation density variations, and spatial correlations between polarized observations are largely maintained across scales. Validation against in situ measurements indicates that PoLRa derived SM retrievals from the SCAV and MTDCA algorithms achieve unbiased root-mean-square errors (ubRMSE) generally below 0.04 m3/m3 across different land covers. Bayesian posterior analyses confirm that reference SM values largely fall within the derived uncertainty intervals, with mean uncertainty ranges around 0.02 m3/m3 and 0.11 m3/m3 for SCA and DCA related retrievals.

Sofiane Bouaziz, Adel Hafiane, Raphael Canals et al.

Land Surface Temperature (LST) plays a key role in climate monitoring, urban heat assessment, and land-atmosphere interactions. However, current thermal infrared satellite sensors cannot simultaneously achieve high spatial and temporal resolution. Spatio-temporal fusion (STF) techniques address this limitation by combining complementary satellite data, one with high spatial but low temporal resolution, and another with high temporal but low spatial resolution. Existing STF techniques, from classical models to modern deep learning (DL) architectures, were primarily developed for surface reflectance (SR). Their application to thermal data remains limited and often overlooks LST-specific spatial and temporal variability. This study provides a focused review of DL-based STF methods for LST. We present a formal mathematical definition of the thermal fusion task, propose a refined taxonomy of relevant DL methods, and analyze the modifications required when adapting SR-oriented models to LST. To support reproducibility and benchmarking, we introduce a new dataset comprising 51 Terra MODIS-Landsat LST pairs from 2013 to 2024, and evaluate representative models to explore their behavior on thermal data. The analysis highlights performance gaps, architecture sensitivities, and open research challenges. The dataset and accompanying resources are publicly available at https://github.com/Sofianebouaziz1/STF-LST.

Lucas McCullum, Kareem A. Wahid, Barbara Marquez et al.

The Dice Similarity Coefficient (DSC) is the current de facto standard to determine agreement between a reference segmentation and one generated by manual / auto-contouring approaches. This metric is useful for non-spatially important images; however, radiation therapy requires consideration of nearby Organs-at-Risk (OARs) and their radiosensitivity which are currently unaccounted for with the traditional DSC. In this work, we introduce the OAR-DSC which accounts for nearby OARs and their radiosensitivity when computing the DSC. We illustrate the importance of this through cases where two proposed contours have similar DSC, but lower OAR-DSC when one contour expands closer to the surrounding OARs. This work is important because the OAR-DSC may be used by deep learning auto-contouring algorithms in a radiation therapy specific loss function, thereby progressing on the current disregard for the importance of these differences on the final radiation dose plan generation, delivery, and risks of patient toxicity.

T. Carlson, S. Arthur

Yuying Wu, Yijing Li

ABSTRACTIn recent years, the police intervention strategy “Hot spots policing” has been effective in combating crimes. However, as cities are under the intense pressure of increasing crime and scarce police resources, police patrols are expected to target more accurately at finer geographic units rather than ballpark “hot spot” areas. This study aims to develop an algorithm using geographic information to detect crime patterns at street level, the so-called “hot street”, to further assist the Criminal Investigation Department (CID) in capturing crime change and transitive moments efficiently. The algorithm applies Kernel Density Estimation (KDE) technique onto street networks, rather than traditional areal units, in one case study borough in London; it then maps the detected crime “hot streets” by crime type. It was found that the algorithm could successfully generate “hot street” maps for Law Enforcement Agencies (LEAs), enabling more effective allocation of police patrolling; and bear enough resilience itself for the Strategic Crime Analysis (SCA) team’s sustainable utilization, by either updating the inputs with latest data or modifying the model parameters (i.e. the kernel function, and the range of spillover). Moreover, this study explores contextual characteristics of crime “hot streets” by applying various regression models, in recognition of the best fitted Geographically Weighted Regression (GWR) model, encompassing eight significant contextual factors with their varied effects on crimes at different streets. Having discussed the impact of lockdown on crime rates, it was apparent that the land-use driven mobility change during lockdown was a fundamental reason for changes in crime. Overall, these research findings have provided evidence and practical suggestions for crime prevention to local governors and policy practitioners, through more optimal urban planning (e.g. Low Traffic Neighborhoods), proactive policing (e.g. in the listed top 10 “Hot Streets” of crime), publicizing of laws and regulations, and installations of security infrastructures (e.g. CCTV cameras and traffic signals).

Haechan Mark Bong, Rongge Zhang, Ricardo de Azambuja et al.

This work targets what we consider to be the foundational step for urban airborne robots, a safe landing. Our attention is directed toward what we deem the most crucial aspect of the safe landing perception stack: segmentation. We present a streamlined reactive UAV system that employs visual servoing by harnessing the capabilities of open vocabulary image segmentation. This approach can adapt to various scenarios with minimal adjustments, bypassing the necessity for extensive data accumulation for refining internal models, thanks to its open vocabulary methodology. Given the limitations imposed by local authorities, our primary focus centers on operations originating from altitudes of 100 meters. This choice is deliberate, as numerous preceding works have dealt with altitudes up to 30 meters, aligning with the capabilities of small stereo cameras. Consequently, we leave the remaining 20m to be navigated using conventional 3D path planning methods. Utilizing monocular cameras and image segmentation, our findings demonstrate the system's capability to successfully execute landing maneuvers at altitudes as low as 20 meters. However, this approach is vulnerable to intermittent and occasionally abrupt fluctuations in the segmentation between frames in a video stream. To address this challenge, we enhance the image segmentation output by introducing what we call a dynamic focus: a masking mechanism that self adjusts according to the current landing stage. This dynamic focus guides the control system to avoid regions beyond the drone's safety radius projected onto the ground, thus mitigating the problems with fluctuations. Through the implementation of this supplementary layer, our experiments have reached improvements in the landing success rate of almost tenfold when compared to global segmentation. All the source code is open source and available online (github.com/MISTLab/DOVESEI).

Mohsen Zahmatkesh, Seyyed Ali Emami, Afshin Banazadeh et al.

A robust auto-landing problem of a Truss-braced Wing (TBW) regional jet aircraft with poor stability characteristics is presented in this study employing a Fuzzy Reinforcement Learning scheme. Reinforcement Learning (RL) has seen a recent surge in practical uses in control systems. In contrast to many studies implementing Deep Learning in RL algorithms to generate continuous actions, the methodology of this study is straightforward and avoids complex neural network architectures by applying Fuzzy rules. An innovative, agile civil aircraft is selected not only to meet future aviation community expectations but also to demonstrate the robustness of the suggested method. In order to create a multi-objective RL environment, a Six-degree-of-freedom (6-DoF) simulation is first developed. By transforming the auto-landing problem of the aircraft into a Markov Decision Process (MDP) formulation, the problem is solved by designing a low-level Fuzzy Q-learning (FQL) controller. More specifically, the well-known Q-learning method, which is a discrete RL algorithm, is supplemented by Fuzzy rules to provide continuous actions with no need to complex learning structures. The performance of the proposed system is then evaluated by extensive flight simulations in different flight conditions considering severe wind gusts, measurement noises, actuator faults, and model uncertainties. Besides, the controller effectiveness would be compared with existing competing techniques such as Dynamic Inversion (DI) and Q-learning. The simulation results indicate the superior performance of the proposed control system as a reliable and robust control method to be employed in real applications.

Manikandan Srinivasan P., Dharmakkan Nesakumar, Sumana Nagamani

The experimental study of the heat transfer coefficient of nanofluid plays a significant role in improving the heat transfer rate of the heat exchanger. A natural convection apparatus was used to study heat transfer in the suspension of Al2O3 nanoparticles in a water-ethylene glycol mixture base fluid. The effects of the heat input, the nanoparticle volume fraction, and the base fluid concentration on the heat transfer coefficient were studied using a 23 full factorial design matrix (16 experimental runs) and the MINITAB Design software. The levels for the heat input, nanoparticle volume fraction, and base fluid concentration were 10 and 100 W, 0.1 and 1 vol.%, and 30 and 50 vol.%, respectively. The residual, contour, 3D surface plots, and Pareto chart were drawn from the experimental results. The observed heat transfer coefficient showed the highest enhancement with the high level of the nanoparticle volume fraction and a moderate enhancement with the high level of heat input, and a slight enhancement with the base fluid concentration.

Daniel Assefa Tofu, Kebede Wolka, Teshale Woldeamanuel

Abstract Energy is a key factor in the economic development. Currently, however, millions of people across the world suffer from energy poverty, having little or no access to energy for cooking, lighting, heating, cooling, or using information and communication technologies. Objective of this study was to investigate the domestic energy sources for households and the impact of biomass use as a source of energy on the environment and food insecurity in the drought-affected northern highlands of Ethiopia. A total of 398 household heads were interviewed using a structured questionnaire, whereas 16 focus group discussions and 12 key informant interviews were conducted. Descriptive data analysis techniques were used to analyze quantitative data while content analysis methods were used to analyze qualitative data. The use of traditional biomass fuels such as firewood, charcoal, crop residue, animal dung, and biomass residue that can be combusted were prevalent in the area, which aggravated the degradation of agricultural lands. As commented by the majority of respondents, the move towards the adoption of modern energy sources was not common due to finance (98%), access (97%), durability (97%) and lack of awareness (93%). The findings showed that land degradation has been severe to the extent that no grain yield can be collected from crop production. As a result, people were exposed to both chronic and transitory food insecurity, and hence the majority of people make their living on food aid. In food-insecure areas, relying on biomass energy could increase land degradation or retard the speed of land restoration, which adversely affects agricultural production and food security. Investing in alternative energy technologies can improve the environment, food security, and people’s health.

Hongjie Jia, Huiyuan Wang, Yan Cao et al.

Abstract In modern society, system integration that enables multiple subsystems to function as one is emerging in various fields like industry, commerce, and infrastructure. Although it has been proved that integration value could be tapped to the maximum with controllable cost by optimising the integration schemes in certain fields, there is still a lack of a general method for modelling and analysing the process of system integration. To address this need, this paper proposes an analysis framework of system integration. The concepts of integration object, integration strategy, integration time, integration cost and integration value are introduced to describe the integration process. Further, three optimisation models of the local optimisation (OPT1), phase optimisation (OPT2) and integration optimisation (OPT3) are constructed. The proposed framework can also supervise and compare the performance of intermediate processes of different integration schemes. Two case studies in the commerce and energy fields are analysed to illustrate the function of the proposed framework.

Yong Piao, Dongkun Lee, Sangjin Park et al.

Forest fires are one of the most frequently occurring natural hazards, causing substantial economic loss and destruction of forest cover. As the Gangwon-do region in Korea has abundant forest resources and ecological diversity as Korea's largest forest area, spatial data on forest fire susceptibility of the region are urgently required. In this study, a forest fire susceptibility map (FFSM) of Gangwon-do was constructed using Google Earth Engine (GEE) and three machine learning algorithms: Classification and Regression Trees (CART), Random Forest (RF), and Boosted Regression Trees (BRT). The factors related to climate, topography, hydrology, and human activity were constructed. To verify the accuracy, the area under the receiver operating characteristic curve (AUC) was used. The AUC values were 0.846 (BRT), 0.835 (RF), 0.751 (CART). Factor importance analysis was performed to identify the important factors of the occurrence of forest fires in Gangwon-do. The results show that the most important factor in the Gangwon-do region is slope. A slope of approximately 17° (moderately steep) has a considerable impact on the occurrence of forest fires. Human activity and interference are the other important factors that affect forest fires. The established FFSM can support future efforts on forest resource protection and environmental management planning in Gangwon-do.