Hasil untuk "Land use"

Florentin Goyens, Florian Feppon

Landing methods have recently emerged in Riemannian matrix optimization as efficient schemes for handling nonlinear equality constraints without resorting to costly retractions. These methods decompose the search direction into tangent and normal components, enabling asymptotic feasibility while maintaining inexpensive updates. In this work, we provide a unifying geometric framework which reveals that, under suitable choices of Riemannian metric, the landing algorithm encompasses several classical optimization methods such as projected and null-space gradient flows, Sequential Quadratic Programming (SQP), and a certain form of the augmented Lagrangian method. In particular, we show that a quadratically convergent landing method essentially reproduces the quadratically convergent SQP method. These connections also allow us to propose a globally convergent landing method using adaptive step sizes. The backtracking line search satisfies an Armijo condition on a merit function, and does not require prior knowledge of Lipschitz constants. Our second key contribution is to analyze landing methods through a geometric parameterization of the metric in terms of fields of oblique projectors and associated metric restrictions. This viewpoint disentangles the roles of orthogonality, tangent and normal metrics, and elucidates how to design the metric to obtain explicit tangent and normal updates. For matrix optimization, this framework not only recovers recent constructions in the literature for problems with orthogonality constraints, but also provides systematic guidelines for designing new metrics that admit closed-form search directions.

Mashrekur Rahman

Satellite foundation models produce dense embeddings whose physical interpretability remains poorly understood, limiting their integration into environmental decision systems. Using 12.1 million samples across the Continental United States (2017--2023), we first present a comprehensive interpretability analysis of Google AlphaEarth's 64-dimensional embeddings against 26 environmental variables spanning climate, vegetation, hydrology, temperature, and terrain. Combining linear, nonlinear, and attention-based methods, we show that individual embedding dimensions map onto specific land surface properties, while the full embedding space reconstructs most environmental variables with high fidelity (12 of 26 variables exceed $R^2 > 0.90$; temperature and elevation approach $R^2 = 0.97$). The strongest dimension-variable relationships converge across all three analytical methods and remain robust under spatial block cross-validation (mean $ΔR^2 = 0.017$) and temporally stable across all seven study years (mean inter-year correlation $r = 0.963$). Building on these validated interpretations, we then developed a Land Surface Intelligence system that implements retrieval-augmented generation over a FAISS-indexed embedding database of 12.1 million vectors, translating natural language environmental queries into satellite-grounded assessments. An LLM-as-Judge evaluation across 360 query--response cycles, using four LLMs in rotating generator, system, and judge roles, achieved weighted scores of $μ= 3.74 \pm 0.77$ (scale 1--5), with grounding ($μ= 3.93$) and coherence ($μ= 4.25$) as the strongest criteria. Our results demonstrate that satellite foundation model embeddings are physically structured representations that can be operationalized for environmental and geospatial intelligence.

R. Houghton

Lizhu Wang, J. Lyons, P. Kanehl et al.

Zilu Meng, Gregory J. Hakim, Wenchang Yang et al.

Deep learning (DL)-based general circulation models (GCMs) are emerging as fast simulators, yet their ability to replicate extreme events outside their training range remains unknown. Here, we evaluate two such models -- the hybrid Neural General Circulation Model (NGCM) and purely data-driven Deep Learning Earth System Model (DL\textit{ESy}M) -- against a conventional high-resolution land-atmosphere model (HiRAM) in simulating land heatwaves and coldwaves. All models are forced with observed sea surface temperatures and sea ice over 1900-2020, focusing on the out-of-sample early-20th-century period (1900-1960). Both DL models generalize successfully to unseen climate conditions, broadly reproducing the frequency and spatial patterns of heatwave and cold wave events during 1900-1960 with skill comparable to HiRAM. An exception is over portions of North Asia and North America, where all models perform poorly during 1940-1960. Due to excessive temperature autocorrelation, DL\textit{ESy}M tends to overestimate heatwave and cold wave frequencies, whereas the physics-DL hybrid NGCM exhibits persistence more similar to HiRAM.

Ivan C. H. Liu, Chung-En Hao, Jing Xie

Echoes of the Land is an interactive installation that transforms seismic dynamics into a multisensory experience through a scientifically grounded spring-block model. Simulating earthquake recurrence and self-organized criticality, the work generates real-time sound and light via motion capture and concatenative granular synthesis. Each block acts as an agent, producing emergent audiovisual cascades that visualize the physics of rupture and threshold behavior. This work exemplifies the amalgamation of scientific knowledge and artistic practice, opening new avenues for novel forms of musical instrument and narrative medium, while inviting further investigation into the intersection of emergent complexity, aesthetics and interactivity.

V. Dale

Dawid Kudas, Agnieszka Wnęk, Ewelina Zając

Soil sealing is a threat to soil and its ecosystem services. One of the main drivers of soil sealing is land degradation resulting from the expansion of urban areas, where it leads to such problems as the growing risk of flooding and local inundations, urban heat islands, or water shortages. The article focuses on analyses and quantification of the general degree of soil sealing in 2012–2018 in eight functional urban areas (FUA) in Poland, taking into account their division into the urban core (UC) and the commuting zone (CZ). We used the high resolution layer imperviousness density (HRL IMD) data to quantify soil sealing as well as data on land cover and land use with different spatial resolutions, i.e. from the European Urban Atlas project (UA) and the National Database of Topographic Objects (BDOT10k) to quantify artificial surfaces. The research determined the spatial differentiation of UCs and CZs in terms of the degree of soil sealing. We further observed higher average growth of sealed land in CZs. Quantitative and spatial analyses determined the spatial patterns of soil sealing in the FUA in Poland. Soil sealing intensified from 2012 to 2018. The process should be expected to continue in the coming years in light of the continuous transformation of vegetated areas into artificial ones. The conclusions should be considered valuable for the implementation of the spatial policy concerning sustainable land use and soil protection in suburban areas.

Stéphane Vannitsem, David Docquier, Sarah Wauthy et al.

The low-frequency variability of the $δ^{18}$O recorded in ice cores (FK17 and TIR18) recently drilled at two different locations in Dronning Maud Land (Antarctica), is investigated using multi-taper spectral method and singular spectrum analysis. Multiple dominant peaks emerge in these records with periods between 3 and 20 years. The two sites show distinct spectral signatures, despite their relative proximity in space (about 100 km apart), suggesting that different processes are involved in generating the variability at these two sites. In order to clarify which processes are acting on $δ^{18}$O at these two locations, the impact of several climate indices as well as sea ice area is investigated using a causal method, known as the Liang-Kleeman rate of information transfer. The analysis of the origin of this low-frequency variability from external sources reveals that El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Southern Annular Mode (SAM), the Dipole Mode Index (DMI) and the sea ice area display important causal influences on $δ^{18}$O at FK17. For TIR18, the main influences are from ENSO, PDO, DMI, the sea ice area, and the Atlantic Multidecadal Oscillation (AMO), revealing the complexity of the interactions in Dronning Maud Land. The two locations share several drivers, but also show local specificities potentially linked to ocean proximity and differences in air mass trajectories. The implication of these findings on the low-frequency variability in the two ice cores is discussed.

Justin Zhao, Timothy Wang, Wael Abid et al.

Low Rank Adaptation (LoRA) has emerged as one of the most widely adopted methods for Parameter Efficient Fine-Tuning (PEFT) of Large Language Models (LLMs). LoRA reduces the number of trainable parameters and memory usage while achieving comparable performance to full fine-tuning. We aim to assess the viability of training and serving LLMs fine-tuned with LoRA in real-world applications. First, we measure the quality of LLMs fine-tuned with quantized low rank adapters across 10 base models and 31 tasks for a total of 310 models. We find that 4-bit LoRA fine-tuned models outperform base models by 34 points and GPT-4 by 10 points on average. Second, we investigate the most effective base models for fine-tuning and assess the correlative and predictive capacities of task complexity heuristics in forecasting the outcomes of fine-tuning. Finally, we evaluate the latency and concurrency capabilities of LoRAX, an open-source Multi-LoRA inference server that facilitates the deployment of multiple LoRA fine-tuned models on a single GPU using shared base model weights and dynamic adapter loading. LoRAX powers LoRA Land, a web application that hosts 25 LoRA fine-tuned Mistral-7B LLMs on a single NVIDIA A100 GPU with 80GB memory. LoRA Land highlights the quality and cost-effectiveness of employing multiple specialized LLMs over a single, general-purpose LLM.

Bryan Habas, Bo Cheng

Inverted landing is a routine behavior among a number of animal fliers. However, mastering this feat poses a considerable challenge for robotic fliers, especially to perform dynamic perching with rapid body rotations (or flips) and landing against gravity. Inverted landing in flies have suggested that optical flow senses are closely linked to the precise triggering and control of body flips that lead to a variety of successful landing behaviors. Building upon this knowledge, we aimed to replicate the flies' landing behaviors in small quadcopters by developing a control policy general to arbitrary ceiling-approach conditions. First, we employed reinforcement learning in simulation to optimize discrete sensory-motor pairs across a broad spectrum of ceiling-approach velocities and directions. Next, we converted the sensory-motor pairs to a two-stage control policy in a continuous augmented-optical flow space. The control policy consists of a first-stage Flip-Trigger Policy, which employs a one-class support vector machine, and a second-stage Flip-Action Policy, implemented as a feed-forward neural network. To transfer the inverted-landing policy to physical systems, we utilized domain randomization and system identification techniques for a zero-shot sim-to-real transfer. As a result, we successfully achieved a range of robust inverted-landing behaviors in small quadcopters, emulating those observed in flies.

Françis Ngolsou, Eya’ane Meva François, Mésodé Nnangé Akweh et al.

Introduction : Les nanotechnologies sont de nos jours une science qui prend une grande importance en raison de leur simplicité, de leur nature écologique et économique. L’objectif de ce travail consistait à évaluer l’effet anti-inflammatoire des nanoparticules obtenues par biosynthèse in situ à partir de la poudre des feuilles de Psychotria calceata. Matériel et méthodes : La synthèse des nanomatériaux s’est faite à partir d’un infusé de la poudre des feuilles de Psychotria calceata auquel a été ajouté une solution de nitrate d’argent. Les nanoparticules obtenues ont été caractérisées après changement de coloration visuel, au spectrophotomètre d’absorption UV-Vis entre 380 et 550nm. Cette caractérisation consistait à observer la formation des nanoparticules à partir de l'apparition de la résonance plasmonique de surface et d’apprécier leur stabilité. La toxicité orale aiguë des nanoparticules a été réalisée sur des rats Wistar selon le protocole décrit par la ligne directrice 423 (2001) de l'Organisation de Coopération et de Développement économique (OCDE). Un modèle d'œdème plantaire de la patte de rat induit par la carraghénine a été utilisé pour évaluer l’activité anti-inflammatoire de ces nanoparticules et des coupes histologiques ont été réalisées sur le foie, la rate, le cœur et les reins. Résultats et Discussion : Le criblage phytochimique de l'extrait aqueux de Psychotria calceata a révélé la présence d'alcaloïdes, phénols, polyphénols, tanins, saponines, flavonoïdes, triterpènes et stéroïdes. Le pic de résonance plasmonique de surface dans le spectre UV-Vis montre des spectres d'absorption compris entre 380 et 500 nm caractéristique de la présence des particules de taille nanométrique. Leur profil toxicologique a montré une DL50 > 2000mg/kg. A la dose de 400µg, les nanoparticules ont montré une diminution significative à p<0,01, trois heures après l'administration des nanoparticules. Conclusion : Les nanoparticules d'argent peuvent agir comme agents réducteurs/inhibiteurs de la libération des médiateurs inflammatoires aigus. Par conséquent, ce travail a clairement démontré que les nanoparticules d'argent de Psychotria calceata pourraient être considérées comme une source potentielle de médicaments anti-inflammatoires.

Aanis Ahmad, Dharmendra Saraswat, Aly El Gamal

Several factors associated with disease diagnosis in plants using deep learning techniques must be considered to develop a robust system for accurate disease management. A considerable number of studies have investigated the potential of deep learning techniques for precision agriculture in the last decade. However, despite the range of applications, several gaps within plant disease research are yet to be addressed to support disease management on farms. Thus, there is a need to establish a knowledge base of existing applications and identify the challenges and opportunities to help advance the development of tools that address farmers' needs. This study presents a comprehensive overview of 70 studies on deep learning applications and the trends associated with their use for disease diagnosis and management in agriculture. The studies were sourced from four indexing services, namely Scopus, IEEE Xplore, Science Direct, and Google Scholar, and 11 main keywords used were Plant Diseases, Precision Agriculture, Unmanned Aerial System (UAS), Imagery Datasets, Image Processing, Machine Learning, Deep Learning, Transfer Learning, Image Classification, Object Detection, and Semantic Segmentation. The review is focused on providing a detailed assessment and considerations for developing deep learning-based tools for plant disease diagnosis in the form of seven key questions pertaining to (i) dataset requirements, availability, and usability, (ii) imaging sensors and data collection platforms, (iii) deep learning techniques, (iv) generalization of deep learning models, (v) disease severity estimation, (vi) deep learning and human accuracy comparison, and (vii) open research topics. These questions can help address existing research gaps by guiding further development and application of tools to support plant disease diagnosis and provide disease management support to farmers.

Rosanna Bonasia, Lorenzo Borselli, Paolo Madonia

The southeast of Mexico City is one of the last areas of environmental importance for the region. However, rapid urban expansion has led to a runoff increase in the presence of intense rainfall. This situation is common to many peri-urban centers close to large cities, where the urbanization of previously green areas has had a direct negative influence on the hydraulic structure. This work proposes a study that combines hydrological analysis for the definition of precipitation scenarios with hydrodynamic simulations based on the current land use. Reconstructed flood scenarios show that the runoffs descending from mountainous areas flow into cemented channels with hydraulic sections and characteristics not adequate to drain specific discharges that can reach 0.90 m<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula>/s and water depths of the order of 2 m, caused by extreme weather phenomena, determining flooding in nearby areas. Runoffs are also intensified by the presence of non-urbanized open spaces in a state of abandonment, whose soil does not favor infiltration and promotes the flooding of residential centers with water levels higher than 1 m. The results indicate an urgent need to adopt actions to reduce flooding and favor infiltration in an area of the city that is also important for aquifer recharge.

Iosto Fodde, Jinglang Feng, Massimiliano Vasile et al.

ESA's Hera mission aims to visit binary asteroid Didymos in late 2026, investigating its physical characteristics and the result of NASA's impact by the DART spacecraft in more detail. Two CubeSats on-board Hera plan to perform a ballistic landing on the secondary of the system, called Dimorphos. For these types of landings the translational state during descent is not controlled, reducing the spacecrafts complexity but also increasing its sensitivity to deployment maneuver errors and dynamical uncertainties. This paper introduces a novel methodology to analyse the effect of these uncertainties on the dynamics of the lander and design a trajectory that is robust against them. This methodology consists of propagating the uncertain state of the lander using the non-intrusive Chebyshev interpolation (NCI) technique, which approximates the uncertain dynamics using a polynomial expansion, and analysing the results using the pseudo-diffusion indicator, derived from the coefficients of the polynomial expansion, which quantifies the rate of growth of the set of possible states of the spacecraft over time. This indicator is used here to constrain the impact velocity and angle to values which allow for successful settling on the surface. This information is then used to optimize the landing trajectory by applying the NCI technique inside the transcription of the problem. The resulting trajectory increases the robustness of the trajectory compared to a conventional method, improving the landing success by 20 percent and significantly reducing the landing footprint.

Feifan Yu, Wenyuan Cong, Xinmin Chen et al.

Autonomous landing of UAVs in high sea states requires the UAV to land exclusively during the ship deck's "rest period," coinciding with minimal movement. Given this scenario, determining the ship's "rest period" based on its movement patterns becomes a fundamental prerequisite for addressing this challenge. This study employs the Long Short-Term Memory (LSTM) neural network to predict the ship's motion across three dimensions: longi-tudinal, transverse, and vertical waves. In the absence of actual ship data under high sea states, this paper employs a composite sine wave model to simulate ship deck motion. Through this approach, a highly accurate model is established, exhibiting promising outcomes within various stochastic sine wave combination models.

Matthew J. Baker, Jonathan Conning

Economists, historians, and social scientists have long debated how open-access areas, frontier regions, and customary landholding regimes came to be enclosed or otherwise transformed into private property. This paper analyzes decentralized enclosure processes using the theory of aggregative games, examining how population density, enclosure costs, potential productivity gains, and the broader physical, institutional, and policy environment jointly determine the property regime. Changes to any of these factors can lead to smooth or abrupt changes in equilibria that can result in inefficiently high, inefficiently low, or efficient levels of enclosure and associated technological transformation. Inefficient outcomes generally fall short of second-best. While policies to strengthen customary governance or compensate displaced stakeholders can realign incentives, addressing one market failure while neglecting others can worsen outcomes. Our analysis provides a unified framework for evaluating mechanisms emphasized in Neoclassical, Neo-institutional, and Marxian interpretations of historical enclosure processes and contemporary land formalization policies.

Usman Nazir, Wadood Islam, Sara Khalid et al.

Land-use monitoring is fundamental for spatial planning, particularly in view of compound impacts of growing global populations and climate change. Despite existing applications of deep learning in land use monitoring, standard convolutional kernels in deep neural networks limit the applications of these networks to the Euclidean domain only. Considering the geodesic nature of the measurement of the earth's surface, remote sensing is one such area that can benefit from non-Euclidean and spherical domains. For this purpose, we designed a novel Graph Neural Network architecture for spatial and spatio-temporal classification using satellite imagery to acquire insights into socio-economic indicators. We propose a hybrid attention method to learn the relative importance of irregular neighbors in remote sensing data. Instead of classifying each pixel, we propose a method based on Simple Linear Iterative Clustering (SLIC) image segmentation and Graph Attention Network. The superpixels obtained from SLIC become the nodes of our Graph Convolution Network (GCN). A region adjacency graph (RAG) is then constructed where each superpixel is connected to every other adjacent superpixel in the image, enabling information to propagate globally. Finally, we propose a Spatially driven Attention Graph Neural Network (SAG-NN) to classify each RAG. We also propose an extension to our SAG-NN for spatio-temporal data. Unlike regular grids of pixels in images, superpixels are irregular in nature and cannot be used to create spatio-temporal graphs. We introduce temporal bias by combining unconnected RAGs from each image into one supergraph. This is achieved by introducing block adjacency matrices resulting in novel Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA). SAG-NN and STAG-NN-BA outperform graph and non-graph baselines on Asia14 and C2D2 datasets efficiently.

Francesco Roscia, Michele Focchi, Andrea Del Prete et al.

Quadruped robots are machines intended for challenging and harsh environments. Despite the progress in locomotion strategy, safely recovering from unexpected falls or planned drops is still an open problem. It is further made more difficult when high horizontal velocities are involved. In this work, we propose an optimization-based reactive Landing Controller that uses only proprioceptive measures for torque-controlled quadruped robots that free-fall on a flat horizontal ground, knowing neither the distance to the landing surface nor the flight time. Based on an estimate of the Center of Mass horizontal velocity, the method uses the Variable Height Springy Inverted Pendulum model for continuously recomputing the feet position while the robot is falling. In this way, the quadruped is ready to attain a successful landing in all directions, even in the presence of significant horizontal velocities. The method is demonstrated to dramatically enlarge the region of horizontal velocities that can be dealt with by a naive approach that keeps the feet still during the airborne stage. To the best of our knowledge, this is the first time that a quadruped robot can successfully recover from falls with horizontal velocities up to 3 m/s in simulation. Experiments prove that the used platform, Go1, can successfully attain a stable standing configuration from falls with various horizontal velocity and different angular perturbations.

Ulices Santa Cruz Leal, Yasser Shoukry

This paper studies the problem of designing a certified vision-based state estimator for autonomous landing systems. In such a system, a neural network (NN) processes images from a camera to estimate the aircraft relative position with respect to the runway. We propose an algorithm to design such NNs with certified properties in terms of their ability to detect runways and provide accurate state estimation. At the heart of our approach is the use of geometric models of perspective cameras to obtain a mathematical model that captures the relation between the aircraft states and the inputs. We show that such geometric models enjoy mixed monotonicity properties that can be used to design state estimators with certifiable error bounds. We show the effectiveness of the proposed approach using an experimental testbed on data collected from event-based cameras.