Accurate Land Research Articles

Implementation of Deep Learning Technique for Accurate Land Cover Classification

Detecting and modelling Land Cover are crucial for managing natural resources, assessing environmental impacts, and preserving ecological connectivity. Convolutional Neural Networks (CNNs) have become pivotal in advancing remote sensing applications, particularly in land cover classification. Therefore, this study introduces a CNN classifier for the prediction land cover efficiently. The pre processing is carried out based on Principle Component Analysis (PCA), which reduces the dimensionality of the input data while retaining essential information. This step aims to enhance computational efficiency and improve the CNN’s ability to extract relevant features during training. Following pre processing, a comprehensive training phase involves optimizing the CNN architecture using a labelled dataset. This process allows the network to learn intricate spatial patterns and spectral characteristics crucial for accurate land cover classification. Evaluation of the trained model occurs in the testing phase, where performance metrics such as accuracy, precision, recall, and F1 score are computed using an independent validation dataset. Results obtained from the python software demonstrate that the CNN PCA framework’s capability to achieve high classification accuracy across diverse land cover types.

AI and machine learning tools in plantation mapping: potentials of high-resolution satellite data

Abstract. Plantation mapping plays a vital role in agriculture, forestry, and land management. The integration of Artificial intelligence and Machine learning techniques with high-resolution satellite data has revolutionized the accuracy and efficiency of plantation mapping. Utilizing AI and machine learning tools for plantation mapping offers a transformative approach to efficient and accurate land management. These technologies enable automated analysis of satellite imagery and other geospatial data, facilitating rapid and precise identification of plantations, crop health assessment, and yield predictions. The integration of AI enhances the mapping process, providing valuable insights for sustainable agriculture, resource optimization, and environmental monitoring. The application of these advanced tools in plantation mapping represents a significant leap towards data-driven and environmentally conscious land management practices. It presents a promising advancement in agricultural practices. By leveraging these technologies for automated analysis of satellite imagery and geospatial data, accurate and timely mapping of plantations becomes feasible. The use of AI and ML tools in Plantation mapping, challenges in integration, the possible solutions and its future prospects are reviewed in this paper not only to enhance efficiency but also to offer insights into crop health, aiding in precision agriculture and resource optimization.

LAND VALUE ASSESSMENT METHODS AND THEIR INFLUENCE ON THE MARKET PRICE OF LAND PLOTS

The paper analyzes the land market, which is part of the wider property market sector, focusing on the many factors that determine its dynamics and complexity. It was determined that the main drivers of land value are geographical location, economic conditions, legislative regulation, political stability, infrastructure and development prospects of the territory. The location of land plots in urbanized or strategically important locations significantly increases their value due to high demand, while plots in remote or less developed areas have a lower value. The article examines the impact of the economic climate, where during periods of economic growth the demand for land increases, leading to higher prices, while economic downturns can lead to lower prices. Land laws, tax policies, and land use regulations are key regulatory factors that have a significant impact on market value. Further prospects for the development of the area, such as plans for the construction of new residential complexes or commercial centers, can also significantly increase the value of land plots. It is noted that according to the International Valuation Standards, the market value is considered as the amount of money for which the property could be transferred as a result of a commercial agreement between knowledgeable and independent parties. The article emphasizes the importance of an integrated approach to the analysis of the land market, which includes the assessment of all potential factors affecting pricing, including the size of the plot and its specific purpose, which determine its market attractiveness and value. The use of combined evaluation methods, such as comparative, profitable and cost, allows to obtain a more accurate assessment of the value of land plots, which is critical for investors, developers and policy decisions. The results of the study can be used by governments to evaluate and revise land legislation, which will contribute to more effective management of land resources. The importance of accurate land valuation for the implementation of successful land reforms that can help ensure the equitable use of land resources.

Simulation Model Design of VHF Omni-Directional Range (VOR) Based on Microcontroller

The process of giving directional information to assist airplanes in making an accurate landing at the airport of destination is known as aviation navigation. The VHF Omnidirectional Range is a crucial navigation tool for guiding planes to the airport (VOR). Since it costs a lot of money to learn how to fly an airplane, a VOR simulator was developed that can mimic the movement of an aircraft passing through the VOR. The simulation that was produced is a prototype that sends and receives signals to the aircraft utilizing an antenna and microcontroller as supporting hardware. The VOR/DME flight navigation system is constructed in this study using mathematical modeling; a formulation representing the essential features of the system is expressed as a set of connected variables. The ESP32 module that powers the system serves as a DME by sending out Bluetooth radio signals. where one ESP32 module serves as a DME object (moving DME) and three ESP32 modules serve as DME stations (ground DME). Information on the distance between the DME station and the DME object will be communicated using the MQTT protocol, and this data will be processed using the trilateration method to predict the location and movement of the DME object. The measurement accuracy at DME station 1, DME station 2, and DME station 3 are 99.52%, 99.92%, and 99.98% respectively. The enhanced capabilities to estimate the position of objects observed from different directions or omni-directionally on a two-dimensional scale are made possible by the performance results of combining the performance of three ESP32 devices as Distance Measurement Equipment (DME).

The Specific Application and Meaning Expression of Folk Art in Urban Landscape Design Under the Optimized Interactive Genetic Algorithm

With the rapid and stable growth of China's economy and the rapid development of urban construction, urban complexes have sprung up in major cities. The lag in urban ecological environmental protection, maintenance and construction has brought great pressure to the needs of today's urban residents in terms of life, safety, and sense of belonging. In the major cities with many high-rise buildings in contemporary China, land resources are becoming more and more scarce, and the urban ecological environment is in urgent need of recycling, and due to the blind imitation of Western culture and design mode and the neglect of my country's traditional regional culture, the urban landscape lacks interaction, resonance, and sense of belonging with citizens, and the phenomenon of landscape similarity emerges in various cities, focusing on the landscape space of urban complexes. There are also these problems. Urban residents urgently need a third space that can adjust their physical, mental, and spiritual needs. How to design an urban complex landscape that meets the aesthetic needs and humanistic needs of contemporary cities and has regional characteristics has become the first important task of my research. Folk art is an artistic treasure created by the working people in their production and life. Folk art is the embodiment of cultural regionality and the foundation of national culture. It awakens people's awareness of the importance of local culture, awakens people's sense of belonging, and is closer to the local public life. Today, the living soil and social and humanistic environment of folk art are in the process of urbanization in our country, and there is a trend of gradual disappearance of lifestyle changes. How to make the contemporary urban complex landscape an organic soil for the survival, expression, application, and development of folk art is an important task in contemporary urban landscape design. Based on optimization, related concepts such as symbols, folk art symbols, urban complexes, urban complex landscape design, etc. have been sorted out. The relevant experimental results show that the construction land accuracy of the logistic regression model based on genetic algorithm has increased from 78.0% to 85.3%. kappa increased from 74.5% to 81.2%. Research shows that the logistic regression parameter optimization method based on genetic algorithm has better simulation effect than the conventional logistic regression method and is more suitable for the situation of unbalanced data distribution and many data features in the simulation of large-scale urban land dynamic changes.

Control Authority of a Single-Surface Parafoil with Bleed-Air Spoilers

Bleed-air control of ram-air parafoils is a lightweight and cost-effective mechanism that enables significant landing accuracy improvements compared to traditional trailing edge brake control due to the addition of direct glide slope control. Single-surface parafoil canopies have been shown to be lightweight and possess similar flight behavior as their ram-air counterparts. The design of bleed-air spoilers is more challenging for single-surface canopies, which lack the internal ram-air that provides a high pressure differential at any chordwise location. This paper explores bleed-air spoilers on single-surface parafoils with a focus on turn rate and glide slope control capabilities. Through a flight test campaign, it was shown that bleed-air spoilers on a single-surface parafoil provide both turn rate control from asymmetric vent opening and glide slope change from symmetric vent opening. Vents located far forward at the 10% chord location yield substantial control response with a maximum turn rate response of 38 deg/s and a maximum glide slope change of 58%. Vents located farther aft at 30, 50, and 70% chords demonstrated diminished control authority. Varying the spanwise locations of the spoilers revealed that the outermost vents had negligible lateral and longitudinal control authority.

Estimating and explaining regional land value distribution using attention-enhanced deep generative models

Accurate land valuation is crucial in sustainable urban development, influencing pivotal decisions on resource allocation and land-use strategies. Most existing studies, primarily using point-based modeling approaches, face challenges on granularity, generalizability, and spatial effect capturing, limiting their effectiveness in regional land valuation with high granularity. This study therefore proposes the LVGAN (i.e., land value generative adversarial networks) framework for regional land value estimation. The LVGAN model redefines land valuation as an image generation task, employing deep generative techniques combined with attention mechanisms to forecast high-resolution relative value distributions for informed decision-making. Applied to a case study of New York City (NYC), the LVGAN model outperforms typical deep generative methods, with MAE (Mean Absolute Error) and MSE (Mean Squared Error) averagely reduced by 36.58 % and 59.28 %, respectively. The model exhibits varied performance across five NYC boroughs and diverse urban contexts, excelling in Manhattan with limited value variability, and in areas characterized by residential zoning and high density. It identifies influential factors such as road network, built density, and land use in determining NYC land valuation. By enhancing data-driven decision-making at early design stages, the LVGAN model can promote stakeholder engagement and strategic planning for sustainable and well-structured urban environments.

BLEI: Research on a Novel Remote Sensing Bare Land Extraction Index

Bare land, as a significant land cover type on the Earth’s surface, plays a crucial role in supporting land-use planning, urban management, and ecological environmental research through the investigation of its spatial distribution. However, due to the diversity of land-cover types on the Earth’s surface and the spectral complexity exhibited by bare land under the influence of environmental factors, it is prone to confusion with urban and other land features. In order to extract bare land rapidly and efficiently, this study introduces a novel bare land extraction index called the Bare Land Extraction Index (BLEI). Then, considering both Ganzi Tibetan Autonomous Prefecture and Urumqi, China as the study areas, we compared BLEI with three presented indices: the Bare-soil Index (BI), Dry Bare Soil Index (DBSI), and Bare Soil Index (BSI). The results show that BLEI exhibits excellent efficacy in distinguishing bare land and urban areas. It gets the most outstanding accuracy in bare land identification and mapping, with overall accuracy (OA), kappa coefficient, and F1-score of 98.91%, 0.97, and 97.89%, respectively. Furthermore, BLEI is also effective in distinguishing bare land from sandy soil, which can not only improve the mapping accuracy of bare land in soil-deserted areas but also provide technological support for soil research and land-use planning.

Towards accurate spatial prediction of Glossina pallidipes relative densities at country-scale in Kenya

Vector-borne diseases, like those transmitted by tsetse flies, pose a significant global public health threat. Reducing vector populations is a promising strategy for disease control, especially in the case of tsetse-transmitted African trypanosomiasis. However, the cost-effective implementation of large-scale vector surveillance and control measures face challenges due to the lack of spatially explicit and reliable maps identifying vector hotspots. In this study, we assessed the accuracy of predicting Glossina pallidipes relative densities across Kenya by linking constrained in-situ tsetse catch data from 660 traps across three Kenyan regions with readily available gridded satellite information (human population, land cover, soil properties, elevation, precipitation, and land surface temperature) using a classical random forest algorithm. To enhance predictive performance, we employed two feature elimination techniques specifically designed for machine learning algorithms, i.e., Recursive Feature Elimination (RFE) and Variable Selection Using Random Forests (VSURF). For each set of retained variables, we trained a Random Forest model using a spatial cross-validation technique. Our findings showed that tsetse fly relative densities decreased with mean annual precipitation, and soil moisture, and conversely increased with higher tree cover. Based on the cross-validated R2, 41% of the spatial variability in relative densities of tsetse flies could be explained. For spatial extrapolation, only the set of predictors retained by VSURF closely matched known tsetse fly distributions in Kenya. This more accurate performance of VSURF may be attributed to its approach of assessing variables for both importance and their contribution to reducing prediction error. Our study demonstrates the potential of using a random forest method to upscale tsetse relative abundance predictions to the national level. However, the reliability of the current extrapolated map remains uncertain. We recommend: 1) increasing tsetse fly sampling efforts, particularly in the data-limited northern and eastern regions of Kenya, and 2) developing a more precise and accurate land cover map with classes that directly associate with known habitat characteristics of the target tsetse species.

Comparison and Evaluation of Five Global Land Cover Products on the Tibetan Plateau

The Tibetan Plateau (TP) region contains maximal alpine grassland ecology at the mid-latitudes. This region is also recognized as an ecologically fragile and sensitive area under the effects of global warming. Regional climate modeling and ecosystem research depend on accurate land cover (LC) information. In order to obtain accurate LC information over the TP, the reliability and precision of five moderate/high-resolution LC products (MCD12Q1, C3S-LC, GlobeLand30, GLC_FCS30, and ESA2020 in 2020) were analyzed and evaluated in this study. The different LC products were compared with each other in terms of areal/spatial consistency and assessed with four reference sample datasets (Geo-Wiki, GLCVSS, GOFC-GOLD, and USGS) using the confusion matrix method for accuracy evaluation over the TP. Based on the paired comparison of these five LC datasets, all five LC products show that grass is the major land cover type on the TP, but the range of grass coverage identified by the different products varies noticeably, from 43.35% to 65.49%. The fully consistent spatial regions account for 43.72% of the entire region of the TP, while, in the transition area between grass and bare soil, there is still a large area of medium-to-low consistency. In addition, a comparison of LC datasets using integrated reference datasets shows that the overall accuracies of MCD12Q1, C3S-LC, GlobeLand30, GLC_FCS30, and ESA2020 are 54.29%, 49.32%, 53.03%, 53.73%, and 60.11%, respectively. The producer accuracy of the five products is highest for grass, while glaciers have the most reliable and accurate characteristics among all LC products for users. These findings provide valuable insights for the selection of rational and appropriate LC datasets for studying land-atmosphere interactions and promoting ecological preservation in the TP.

Correcting land surface temperature from thermal imager by considering heterogeneous emissivity

It is fundamental to obtain accurate land surface temperature (LST) to study surface energy process. Infrared thermal imagers are commonly used for deriving LST on the basis of radiance measurements. However, when deriving LST from brightness temperature of a blackbody in thermal imagers, thermal imagers only allow setting a fixed land surface emissivity (LSE). This causes uncertainty in retrieving thermal infrared (TIR) temperature from heterogeneous surfaces with varied LSE, such as those covered in vegetation. Corrections can be made using the Normalized Vegetation Index (NDVI). However, commercial thermal imagers provide only red (R), green (G), and blue (B) bands without a near infrared band so NDVI cannot be calculated on the same instrument and is commonly not available. We propose an alternative method to estimate LSE using RGB-based vegetation index. Thereafter the estimated LSE was used to correct the TIR temperature derived from the fixed LSE. An experiment was conducted to validate the proposed correcting method. The results show that 1) the corrected LST values were closer to the ground truth, with a mean absolute error (MAE) of 0.41 ± 0.34 °C, whereas the MAE was 0.75 ± 0.56 °C for the uncorrected LST; 2) the more heterogeneous the surface, the greater the difference between the corrected and uncorrected LST values, indicating the necessity of LST correction when applying thermal imagers over heterogeneous surface.

Application of Sentinel-2A Images for Land Cover Classification Using NDVI in Jember Regency

The advancement of remote sensing technology has led to the development of sophisticated image processing methods that yield highly accurate land cover classification information, minimizing misinterpretations. The Normalized Difference Vegetation Index (NDVI) is a widely utilized method in remote sensing for measuring green vegetation. A significant portion of the Jember Regency area is covered by vegetation. This study aimed to identify various land cover types in the Jember Regency area, quantify the area for each classification, and establish the NDVI value ranges for each type of cover. Sentinel-2 was employed as the primary data source, and the NDVI method was utilized for land cover classification in the Jember Regency. The region exhibited diverse land cover types. Data from Sentinel-2A captured in June and October 2019 were chosen due to their accessibility, open-source nature, and adequate spectral, spatial, and temporal resolution. The classification in this study encompassed five classes: water bodies, settlements, dry fields, irrigated paddy fields, and forests. Error analysis was conducted using a confusion matrix with the Overall and Kappa algorithms. The accuracy results for June indicated a Kappa Accuracy of 37.7% and Overall Accuracy of 54.5%. In October, the Kappa Accuracy increased to 39.9%, and the Overall Accuracy reached 56.5%. In conclusion, the NDVI method did not meet the criteria for accurately interpreting land cover classification.

Design of air-ground cooperative landing platform and autonomous landing strategy

This paper studies innovative design for landing platform design and autonomous landing strategies for unmanned aerial vehicles (UAVs). First, a self-leveling landing platform on a mobile unmanned ground vehicle (UGV) is designed, which can be maintained even when the UGV moves on uneven terrain. Then, with the cooperation of the UAV and the UGV, they are controlled to reach the same velocity in the horizontal direction, which helps avoid the UAV’s rollover caused by horizontal velocity difference. Meanwhile, a buffering strategy is designed to reduce the vertical velocity difference, which helps avoid a hard landing. Finally, a combined landing code is designed to improve the accuracy and success rate of UAV landing. Experiments are carried out to demonstrate the effectiveness of the proposed design of a self-leveling platform and autonomous landing strategies.

Disk‐Integrated Earth’s Outgoing Longwave Radiation Viewed From a Moon‐Based Platform: Model Simulations

AbstractA Moon‐based sensor can observe the Earth as a single point and achieve disk‐integrated measurements of outgoing longwave radiation (OLR), which significantly differs from low orbital, geostationary, and Sun–Earth L1 point platforms. In this study, a scheme of determining the disk‐integrated Earth’s OLR based on a Moon‐based platform is proposed. The observational solid angle was theoretically derived based on the Earth’s ellipsoid model and the disk‐integrated observational anisotropic factor was estimated to eliminate the effects of the Earth’s radiant anisotropy. The simulated disk‐integrated Earth's OLR obtained from a Moon‐based platform varies periodically, due to changes in the observation geometry and Earth's scene distribution within the observed Earth’s disk. Clouds, meteorological parameters, and the land cover distribution notably affect the disk‐integrated Earth’s OLR. By analyzing the disk‐integrated Earth’s OLR from a Moon‐based platform, significant variabilities were investigated. Additionally, the Earth’s shape and radiant anisotropy that affecting the disk‐integrated Earth’s OLR were estimated. In conclusion, a more realistic Earth’s shape, the latest version of the angular distribution model (ADM), and accurate land cover and meteorological datasets are needed when determining the disk‐integrated Earth’s OLR. It is expected the unique variability captured by this platform and its ability to complement traditional satellite data make it a valuable tool for studying Earth’s radiation budget and energy cycle, and contributing to diagnostic of the climate General Circulation Models (GCM) performance.

Vegetation Type Mapping in Southern Patagonia and Its Relationship with Ecosystem Services, Soil Carbon Stock, and Biodiversity

Vegetation Type (VT) mapping using Optical Earth observation data is essential for the management and conservation of natural resources, as well as for the evaluation of the supply of provisioning ecosystem services (ESs), the maintenance of ecosystem functions, and the conservation of biodiversity in anthropized environments. The main objective of the present work was to determine the spatial patterns of VTs related to climatic, topographic, and spectral variables across Santa Cruz province (Southern Patagonia, Argentina) in order to improve our understanding of land use cover at the regional scale. Also, we examined the spatial relationship between VTs and potential biodiversity (PB), ESs, and soil organic content (SOC) across our study region. We sampled 59,285 sites sorted into 19 major categories of land cover with a reliable discrimination level from field measurements. We selected 31 potential predictive environmental dataset covariates, which represent key factors for the spatial distribution of land cover such as climate (four), topography (three), and spectral (24) factors. All covariate maps were generated or uploaded to the Google Earth Engine cloud-based computing platform for subsequent modeling. A total of 270,292 sampling points were used for validation of the obtained classification map. The main land cover area estimates extracted from the map at the regional level identified about 142,085 km2 of grasslands (representing 58.1% of the total area), 38,355 km2 of Mata Negra Matorral thicket (15.7%), and about 25,189 km2 of bare soil (10.3%). From validation, the Overall Accuracy and the Kappa coefficient values for the classification map were 90.40% and 0.87, respectively. Pure and mixed forests presented the maximum SOC (11.3–11.8 kg m−2), followed by peatlands (10.6 kg m−2) and deciduous Nothofagus forests (10.5 kg m−2). The potential biodiversity was higher in some shrublands (64.1% in Mata Verde shrublands and 63.7% in mixed shrublands) and was comparable to those values found for open deciduous forests (Nothofagus antarctica forest with 60.4%). The provision of ESs presented maximum values at pure evergreen forests (56.7%) and minimum values at some shrubland types (Mata Negra Matorral thicket and mixed shrubland) and steppe grasslands (29.7–30.9%). This study has provided an accurate land cover and VT map that provides crucial information for ecological studies, biodiversity conservation, vegetation management and restoration, and regional strategic decision-making.

Event-Based Guidance and Incremental Control with Application to Fixed-wing Unmanned Aerial Vehicle Perched Landing Maneuvers

Considering the nonlinearity and unknown dynamics of fixed-wing unmanned aerial vehicles in perched landing maneuvers, an event-based online guidance and incremental control scheme is proposed. The guidance trajectory for perched landing must be dynamically feasible therefore an event-based trapezoidal collocation point optimization method is proposed. Introduction of the triggering mechanism for the rational use of computing resources to improve PL accuracy. Furthermore, a filter-based incremental nonlinear dynamic inverse (F-INDI) control with state transformation is proposed to achieve robust trajectory tracking under high angle of attack (AOA). The F-INDI uses low-pass filters to obtain incremental dynamics of the system, which simplifies the design process. The state transformation strategy is to convert the flight-path angle, AOA and velocity into two composite dynamics, which avoids the sign reversal problem of control gain under high AOA. The stability analysis shows that the original states can be controlled only by controlling the composite state. Simulation results show that the proposed scheme achieves high perched landing accuracy and a reliable trajectory tracking control.

Cognitive land cover mapping: A three-layer deep learning architecture for remote sensing data classification

In many application domains, remotely sensed (RS) data are essential for disaster monitoring, climate forecasting, and remote surveillance. It perceives and gathers environmental, sociological, and transitional data on its own, from a distance, in increasing amounts of spatial and temporal details. While urbanization has been continued to be a worldwide phenomenon, the urban management and planning are needed to satisfy the needs of the Earth system. The operations associated with land management, urban planning, and transportation on Earth have been connected to digital maps and geographic databases. Knowledge of land cover is also significant for managing, conserving, and formulating environmental and urban development policies. Therefore, highly accurate Land Use Land Cover (LULC) assessment and monitoring are of the utmost significance for empirical studies and organizational accomplishment. In this study, we leverage remotely sensed data to formulate a sophisticated deep learning system for comprehensive Earth observation. The core of our approach involves the development of a three-layered Convolution-Convolution-Maxpooling Convolutional Neural Network (CCMCNN) model, preceded by a meticulous pre-processing stage. In the pre-processing stage, images extracted from the NWPU-RESISC dataset undergo resizing, division, and augmentation procedures before being fed into the three-layered CCMCNN architecture for the purpose of classifying them into five different LULC categories. The effectiveness of our proposed model undergoes thorough validation through a comparative examination against various pre-trained deep learning models outlined in existing literature. Presenting performance metrics, our model attains a remarkable accuracy rate of 98.4%. This advancement holds significant promise in the realm of Earth surveillance, offering a valuable tool for enhancing situational awareness and environmental monitoring.

The reptile optimized deep learning model for land cover classification of the uppal earth region in telangana state using satellite image fusion

This study addresses challenges in land use and cover identification using remote sensing (RS) imagery, focusing on the Uppal region. By leveraging deep learning models, particularly an optimized ResNext-50 architecture, we aim to enhance efficiency and accuracy in classifying land features. Our approach integrates Landsat-8 and hyper-spectral satellite data, utilizing preprocessing techniques like dark subtraction, stacking, merging, and spectral enhancement. Principal Component Analysis (PCA) is applied to streamline high-dimensional feature sets obtained from pre-processed spectral data. We further employ hybrid NSCT-FDCT fusion for integrating Landsat-8 and hyperspectral images. The resulting fused image is fed into our classification process, utilizing the modified ResNext50 (Deep Learning Architecture) model with Reptile Search Optimization for weight link optimization. Notably, our proposed method achieves impressive outcomes: 97% accuracy, 96% sensitivity, 99% specificity, 3% error, 97% precision, and a 95% Matthew Correlation Coefficient. This demonstrates the efficacy of our approach in predicting diverse land covers within the Uppal region, showcasing the potential of Landsat-8 and Hyper-spectral data for accurate land use and cover identification.

Calculate the ignition height of the vertical landing phase online for the reusable rocket

For the vertical landing phase of the reusable rocket, in order to improve the landing accuracy with consideration of multiple uncertainties, a novel strategy to calculate the ignition height online is proposed based on polynomial guidance law (PGL), particle swarm optimization (PSO), and deep reinforcement learning (DRL). Firstly, a deep neural network (DNN) is designed to describe the relationship between the state of the reusable rocket and the ignition height. To accomplish the guidance task of the vertical landing phase, PGL is modified by introducing the estimated aerodynamic acceleration. Through simulation, the output range of the DNN is estimated by the modified PSO. Then, the reward function is shaped and the parameters of the DNN are trained on a training set of simulation scenarios by the DRL algorithm. Finally, to demonstrate the effectiveness of the proposed strategy, the trained DNN is used to calculate the ignition height of 1500 unlearned simulation scenarios online. The numerical simulation results show that the proposed strategy has higher landing accuracy and lower fuel consumption than the offline strategy of fixed ignition height based on the modified PSO.

Cost-effective disaster-induced land cover analysis: a semi-automatic methodology Using machine learning and satellite imagery

ABSTRACT This study introduces a cost-effective and automated methodology to swiftly and accurately gauge disaster-induced land cover changes, harnessing satellite imagery and machine learning. Using open-source platforms like Google Earth Engine, the approach streamlines training sample generation and assesses diverse machine learning algorithms on pre- and post-disaster Landsat images. The innovation lies in its semi-automatic training sample generation methodology, reducing time and manual effort for accurate land cover mapping and addressing the challenge of crafting training samples in disaster settings. The methodology was tested on the 2010 Chilean earthquake and tsunami, revealing a 45% increase in sediment cover and a 15% reduction in water cover, underlining vast landscape alterations. SVM stood out for its consistent accuracy. To validate the methodological robustness and broaden its applicability, we replicated the procedures using diverse land cover collection, including ESA WorldCover 10 m and Dynamic World. This comprehensive validation encompassed varied catastrophic events, extending the methodology’s relevance beyond its original scope. The consistent findings across different scenarios underscored the method’s robustness and underscored its potential for broader utilization. This streamlined, cost-effective approach suits multiple land cover products, setting the stage for constant Earth feature monitoring. In light of escalating natural disasters, the synergy of machine learning and geospatial technology spotlighted here offers promising avenues for improved disaster preparedness and response.