Search Results (22)

This paper investigates land-use as the cornerstone of spatial planning in rapidly urbanising contexts, focusing on the critical gaps at the mesoscale between centralised vision and local implementation. By exploring Java’s complex desakota landscapes, this study employs an innovative GIS-based land-use cluster analysis using multidimensional parameters—including slope, population density, agricultural land, forest cover, and surface water—to categorise land-use patterns. The resulting mesoscale clusters reveal cohesive functional territories that transcend traditional political boundaries, articulating distinctive ‘mixtures’ of urbanity within Java’s rural-urban continuum. This approach not only captures socio-environmental dynamics across administrative silos but also establishes a new strategic framework for regional planning challenges. By advancing boundary-making beyond mere political convention to reflect on-the-ground ecological and functional coherence, this framework responds to the urgent global challenge of reconciling accelerating suburban and regional development pressures with the preservation of local communities, agricultural systems, and natural landscapes. Full article

As the “Ninth Grotto-Heaven” in Daoist tradition, Linwu Cave has served as a symbolic bridge between the human and immortal realms since the Tang Dynasty. During the Ming Dynasty, painters of the Wu School in Suzhou reimagined Linwu Cave through landscape paintings, transforming it into a visual emblem that merged Daoist cosmology with the ancient Chinese literati ideal of reclusion. This article adopts an interdisciplinary approach, combining art history and religious studies, to analyze Linwu Cave-themed paintings by Wu School artists such as Shen Zhou, Wen Zhengming, and Tang Yin. The study examines how these painters reinterpreted the Daoist concept of “grotto-heavens and blessed lands” into “habitable spaces” through a process of “de-ritualization”. This strategy involved the use of imagery such as the alchemical metaphors of stalactites and the qi (vital energy) symbolism of auspicious clouds on sacred mountains, which diminished Daoist ritualistic elements while amplifying the literati’s idealized vision of reclusion. Drawing on local historical records and field investigations, the research further reveals how the transformation of Linwu Cave into a cultural landmark reflected the Ming Dynasty scholar-officials’ cultural strategies. Through art, these individuals articulated the tension between their aspirations for official success and their longing for a secluded life, set against the backdrop of a rigid civil service examination system and intense political rivalries. By employing the theoretical framework of “Sacred Space”, this study argues that literati painting functioned not only as an aesthetic expression but also as a dynamic medium for religious and philosophical ideas. This perspective offers new insights into the interpretation of Daoist art and its broader cultural significance. Full article

Unmanned Surface Vehicles (USVs) are commonly used as mobile docking stations for Unmanned Aerial Vehicles (UAVs) to ensure sustained operational capabilities. Conventional vision-based techniques based on horizontally-placed fiducial markers for autonomous landing are not only susceptible to interference from lighting and shadows but are also restricted by the limited Field of View (FOV) of the visual system. This study proposes a method that integrates an improved minimum snap trajectory planning algorithm with an event-triggered vision-based technique to achieve autonomous landing on a small USV. The trajectory planning algorithm ensures trajectory smoothness and controls deviations from the target flight path, enabling the UAV to approach the USV despite the visual system’s limited FOV. To avoid direct contact between the UAV and the fiducial marker while mitigating the interference from lighting and shadows on the marker, a landing platform with a vertically placed fiducial marker is designed to separate the UAV landing area from the fiducial marker detection region. Additionally, an event-triggered mechanism is used to limit excessive yaw angle adjustment of the UAV to improve its autonomous landing efficiency and stability. Experiments conducted in both terrestrial and river environments demonstrate that the UAV can successfully perform autonomous landing on a small USV in both stationary and moving scenarios. Full article

Visual navigation, characterized by its autonomous capabilities, cost effectiveness, and robust resistance to interference, serves as the foundation for vision-based autonomous landing systems. These systems rely heavily on runway instance segmentation, which accurately divides runway areas and provides precise information for unmanned aerial vehicle (UAV) navigation. However, current research primarily focuses on runway detection but lacks relevant runway instance segmentation datasets. To address this research gap, we created the Runway Landing Dataset (RLD), a benchmark dataset that focuses on runway instance segmentation mainly based on X-Plane. To overcome the challenges of large-scale changes and input image angle differences in runway instance segmentation tasks, we propose a vision-based autonomous landing segmentation network (VALNet) that uses band-pass filters, where a Context Enhancement Module (CEM) guides the model to learn adaptive “band” information through heatmaps, while an Orientation Adaptation Module (OAM) of a triple-channel architecture to fully utilize rotation information enhances the model’s ability to capture input image rotation transformations. Extensive experiments on RLD demonstrate that the new method has significantly improved performance. The visualization results further confirm the effectiveness and interpretability of VALNet in the face of large-scale changes and angle differences. This research not only advances the development of runway instance segmentation but also highlights the potential application value of VALNet in vision-based autonomous landing systems. Additionally, RLD is publicly available. Full article

The successful 2020 launch and 2021 landing of the National Aeronautics and Space Administration’s (NASA) Perseverance Mars rover initiated the first phase of the NASA and European Space Agency (ESA) Mars Sample Return (MSR) campaign. The goal of the MSR campaign is to collect scientifically interesting samples from the Martian surface and return them to Earth for further study in terrestrial laboratories. The MSR campaign consists of three major spacecraft components to accomplish this objective: the Perseverance Mars rover, the Sample Retrieval Lander (SRL) and the Earth Return Orbiter (ERO). Onboard the ERO spacecraft is the Capture, Containment and Return System (CCRS). CCRS will capture, process and return to Earth the samples that have been collected after they are launched into Mars orbit by the Mars Ascent Vehicle (MAV), which is delivered to Mars onboard the SRL. To facilitate the processing of the orbiting sample (OS) via the CCRS, we have designed and developed a vision system to determine the OS capture orientation. The vision system is composed of two cameras sensitive to the visible portion of the electromagnetic spectrum and two illumination modules constructed from broadband light emitting diodes (LED). Vision system laboratory tests and physics-based optical simulations predict CCRS ground processing will be able to correctly identify the OS post-capture orientation using only a single vision system image that is transmitted to Earth from Mars orbit. Full article

Due to their low cost, interference resistance, and concealment of vision sensors, vision-based landing systems have received a lot of research attention. However, vision sensors are only used as auxiliary components in visual landing systems because of their limited accuracy. To solve the problem of the inaccurate position estimation of vision-only sensors during landing, a novel data closed-loop pose-estimation algorithm with an implicit neural map is proposed. First, we propose a method with which to estimate the UAV pose based on the runway’s line features, using a flexible coarse-to-fine runway-line-detection method. Then, we propose a mapping and localization method based on the neural radiance field (NeRF), which provides continuous representation and can correct the initial estimated pose well. Finally, we develop a closed-loop data annotation system based on a high-fidelity implicit map, which can significantly improve annotation efficiency. The experimental results show that our proposed algorithm performs well in various scenarios and achieves state-of-the-art accuracy in pose estimation. Full article

The advancement of autonomous technology in Unmanned Aerial Vehicles (UAVs) has piloted a new era in aviation. While UAVs were initially utilized only for the military, rescue, and disaster response, they are now being utilized for domestic and civilian purposes as well. In order to deal with its expanded applications and to increase autonomy, the ability for UAVs to perform autonomous landing will be a crucial component. Autonomous landing capability is greatly dependent on computer vision, which offers several advantages such as low cost, self-sufficiency, strong anti-interference capability, and accurate localization when combined with an Inertial Navigation System (INS). Another significant benefit of this technology is its compatibility with LiDAR technology, Digital Elevation Models (DEM), and the ability to seamlessly integrate these components. The landing area for UAVs can vary, ranging from static to dynamic or complex, depending on their environment. By comprehending these characteristics and the behavior of UAVs, this paper serves as a valuable reference for autonomous landing guided by computer vision and provides promising preliminary results with static image photogrammetry. Full article

The selection of the electric motor for the propulsion system in electric vehicles is a crucial step, as it determines the final performance of the vehicle. The design of the propulsion system of an electric vehicle, although similar in principle to that of a conventional endothermic engine, requires a change in vision. Indeed, the main problem in an electric vehicle is its range, which depends not only on the weight of the vehicle but also on the type of powertrain, type of transmission and engine, several factors that are difficult to assess at an early stage. In some cases, during the preliminary design phase of the propulsion system, one simply estimates the maximum power required by the vehicle, neglecting the calculation of the range. This evaluation is postponed to later stages, causing increased complexity and interaction during the propulsion system evaluation process. In this study, vehicle autonomy is taken into account from the outset with the aim to reduce this iteration. This paper proposes a preliminary electric motor selection method for land vehicles, highlighting the importance of smoothing the sampled data of driving cycles. A method for obtaining approximate efficiency maps of the electric motor is also illustrated, and it is shown how the total gear ratio affects vehicle energy consumption. Ultimately, this work makes a contribution to the design of more efficient and high-performance electric vehicles. This topic is more oriented to helping automotive manufactures choose in a fast and structured way electric motors for their vehicles. Full article

This paper describes the deployment, integration, and demonstration of a Smartphone Video Guidance Sensor (SVGS) as a novel technology for autonomous 6-DOF proximity maneuvers and precision landing of a quadcopter drone. The proposed approach uses a vision-based photogrammetric position and attitude sensor (SVGS) to estimate the position of a landing target after video capture. A visual inertial odometry sensor (VIO) is used to provide position estimates of the UAV in a ground coordinate system during flight on a GPS-denied environment. The integration of both SVGS and VIO sensors enables the accurate updating of position setpoints during landing, providing improved performance compared with VIO-only landing, as shown in landing experiments. The proposed technique also shows significant operational advantages compared with state-of-the-art sensors for indoor landing, such as those based on augmented reality (AR) markers. Full article

The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms’ ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology’s performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario. Full article

The transformation of dietary structure brought about by economic development in populous countries is expected to trigger an increase in grain demand, which will put enormous pressure on the grain supply in these nations and even globally. We simulated nine demand scenarios for 2020–2050 based on China’s dataset for 15 kinds of grains from 1961–2018. The results show that the maximum difference between the predicted grain demand is 323.8 Mt, equal to the total grain consumption of approximately 600 million Chinese people in one year. To capture which demand scenarios will be met when grain productivity gradually improves within reasonable ranges, we present three projections from the production side. In particular, Projection 1 (P1), which maintains productivity at the current level, only fulfills the projected demand for Scenarios 1-LL, 2-LM, 4-ML, and 7-HL and falls short of the maximum value (Scenario 9-HH) by 117 Mt, which requires an additional 250,000 ha of arable land resources to fill the gap. After raising the preset value of grain yield, the productivity of Projection 2 in turn satisfies the demand scenario 5-MM. When both set variables (grain yields and arable area) increase simultaneously, the output of Projection 3 increases by 15.3% over P1. However, it still lags behind the demand of 68 million tons in Scenario 9-HH, thus implying uncertainty in China’s vision of meeting the goal of 95% grain self-sufficiency. Rather than pursuing a single outcome, we discuss multiple possibilities for China’s future grain balance and emphasize the adjusting and compensating role of grain trade and storage in the whole system. Ultimately, this paper calls for a better understanding of the supply–demand gap therein and its future trends to support national grain security as well as global sustainable food policies. Full article

The paper presents the concept of a component of an aircraft’s automatic flight control system, controlling the airplane when in longitudinal motion (i.e., pitch angle, sink rate, airspeed channels) during automatic landing, from a final approach until a touchdown. It is composed of two key parts: a vision system and an automatic landing system. The first part exploits dedicated image-processing algorithms to identify the number of red and white PAPI lights appearing on an onboard video camera. Its output data—information about an aircraft’s position on a vertical profile of a landing trajectory—is used as one of the crucial inputs to the automatic landing system (the second part), which uses them to control the landing. The control algorithms implemented by the automatic landing system are based on the fuzzy logic expert system and were developed to imitate the pilot’s control actions during landing an aircraft. These two parts were teamed together as a component of a laboratory rig, first as pure software algorithms only, then as real hardware modules with downloaded algorithms. In two test campaigns (software in the loop and hardware in the loop) they controlled an aircraft model in a simulation environment. Selected results, presenting both control efficiency and flight precision, are given in the final section of the paper. Full article

Unmanned Aerial Vehicles have advanced rapidly in the last two decades with the advances in microelectromechanical systems (MEMS) technology. It is crucial, however, to design better power supply technologies. In the last decade, lithium polymer and lithium-ion batteries have mainly been used to power multirotor UAVs. Even though batteries have been improved and are constantly being improved, they provide fairly low energy density, which limits multirotors’ UAV flight endurance. This problem is addressed and is being partially solved by using docking stations which provide an aircraft to land safely, charge (or change) the batteries and to take-off as well as being safely stored. This paper focuses on the work carried out in the last decade. Different docking stations are presented with a focus on their movement abilities. Rapid advances in computer vision systems gave birth to precise landing systems. These algorithms are the main reason that docking stations became a viable solution. The authors concluded that the docking station solution to short ranges is a viable option, and numerous extensive studies have been carried out that offer different solutions, but only some types, mainly fixed stations with storage systems, have been implemented and are being used today. This can be seen from the commercially available list of docking stations at the end of this paper. Nevertheless, it is important to be aware of the technologies being developed and implemented, which can offer solutions to a vast number of different problems. Full article

Five years after its establishment (2016), the LTER-Greece network outlines its vision, aims, objectives and its achievements through a series of case studies. The network consists of eight observatories, focusing on innovative research topics, aiming to be both cooperative and complementary, while currently being in the process of expanding. LTER-Greece acknowledges the complexity of ecosystems and the fact that effective management of natural resources may only be achieved by addressing every sector of a nexus system in order to understand inter-dependencies, thus accounting for solutions that promote resilience. Hence, LTER-Greece focuses on the holistic study of the water-environment-ecosystem-food-energy-society nexus, in order to face environmental and socio-ecological challenges at local and global scales, particularly climate change, biodiversity loss, pollution, natural disasters and unsustainable water and land management. Framed around five research pillars, monitoring and research targets nine research hypotheses related to climate change, environmental management, socio-ecology and economics, biodiversity and environmental process dynamics. As environmental monitoring and related research and conservation in Greece face critical shortcomings, LTER-Greece envisages confronting these gaps and contributing with interdisciplinary solutions to the current and upcoming complex environmental challenges. Full article

The techniques for conducting forest inventories have been established over centuries of land management and conservation. In recent decades, however, compelling new tools and methodologies in remote sensing, computer vision, and data science have offered innovative pathways for enhancing the effectiveness and comprehension of these sampling designs. Now with the aid of Unmanned Aerial Systems (UAS) and advanced image processing techniques, we have never been closer to mapping forests at field-based inventory scales. Our research, conducted in New Hampshire on complex mixed-species forests, used natural color UAS imagery for estimating individual tree diameters (diameter at breast height (dbh)) as well as stand level estimates of Basal Area per Hectare (BA/ha), Quadratic Mean Diameter (QMD), Trees per Hectare (TPH), and a Stand Density Index (SDI) using digital photogrammetry. To strengthen our understanding of these forests, we also assessed the proficiency of the UAS to map the presence of large trees (i.e., >40 cm in diameter). We assessed the proficiency of UAS digital photogrammetry for identifying large trees in two ways: (1) using the UAS estimated dbh and the 40 cm size threshold and (2) using a random forest supervised classification and a combination of spectral, textural, and geometric features. Our UAS-based estimates of tree diameter reported an average error of 19.7% to 33.7%. At the stand level, BA/ha and QMD were overestimated by 42.18% and 62.09%, respectively, while TPH and SDI were underestimated by 45.58% and 3.34%. When considering only stands larger than 9 ha however, the overestimation of BA/ha at the stand level dropped to 14.629%. The overall classification of large trees, using the random forest supervised classification achieved an overall accuracy of 85%. The efficiency and effectiveness of these methods offer local land managers the opportunity to better understand their forested ecosystems. Future research into individual tree crown detection and delineation, especially for co-dominant or suppressed trees, will further support these efforts. Full article