Precise Map Research Articles

Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data

ABSTRACTAutomating mineral delineation and rock type analysis using remote sensing imaging data is a critical application of machine learning. Traditional machine learning methods often struggle with accuracy and precise map generation. This study aims to enhance performance through a refined deep learning model. In this work, we present a deep learning pipeline to map the mineral deposits in the study area. Initially, we apply a deep convolutional neural network (CNN) to a specialized mineral dataset to map mineral deposits within the study area. Subsequently, we build a hybrid model combining deep CNN layers with a support vector machine (SVM). This merger significantly improves classification accuracy from an initial 92.7% to 95.3%. In our approach, CNN layers function as feature extractors while the SVM serves as the classification model. Moreover, we conduct an evaluation of the SVM using polynomial kernels of degrees 3, 6, 9, and 12. The results indicate that the SVM with a degree of 12 achieved the highest classification accuracy, followed by degrees 9, 6, and 3. Experimental results demonstrate the effectiveness of our proposed method for classifying remote sensing imaging data, showcasing its potential for advancing mineral delineation and rock type analysis.

Machine Learning and Remote Sensing Applications for Assessing Land Use and Land Cover Changes for Under-monitored Basin

Urban sprawling caused by industrial and economic growth has significantly affected land use and land cover (LULC). Using satellite imagery for real-time examination in Kuantan has become exceedingly expensive due to the scarcity and obsolescence of real-time LULC data. With the advent of remote sensing and geographical information systems, LULC change assessment is feasible. A quantitative assessment of image classification schemes (supervised classification using maximum likelihood and deep learning classification using random forest) was examined using 2022 Sentinel-2 satellite imagery to measure its performance. Kappa coefficient and overall accuracy were used to determine the classification accuracy. Then, 32 years of LULC changes in Kuantan were investigated using Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 based on the best classifier. Random forest classification outperformed maximum likelihood classification with an overall accuracy of 85% compared to 92.8%. The findings also revealed that urbanisation is the main factor contributing to land changes in Kuantan, with a 32% increase in the build-up region and 32% in forest degradation. Despite the subtle and extremely dynamic connection between ecosystems, resources, and settlement, these LULC changes can be depicted using satellite imagery. With the precision of using a suitable classification scheme based on comprehensive, accurate and precise LULC maps can be generated, capturing the essence of spatial dynamics, especially in under-monitored basins. This study provides an overview of the current situation of LULC changes in Kuantan, along with the driving factors that can help the authorities promote sustainable development goals.

Optimized Factor Graph for Tightly-Coupled LiDAR/IMU Localization in Underground Parking Garages

Abstract. To address the challenge of intelligent vehicle localization in underground parking structures due to the loss of GNSS signals, this paper introduces a method to address this issue by developing a novel localization framework known as GF-LIO, which denotes a tightly-coupled fusion of LiDAR and IMU data, innovatively combining the Interactive Extended State Kalman Filter (IESKF) with a factor graph to enhance the localization process and solve the problem of GNSS signal loss in underground parking lots. The GF-LIO model commences with a strategic feature selection process, facilitated by a greedy algorithm that prioritizes environmental cues within the point cloud data. This method effectively filters out redundant features, thereby enhancing the saliency of retained features and subsequently improving the robustness of the localization process. Following feature selection, the model integrates LiDAR and IMU measurements utilizing the IESKF algorithm, ensuring a cohesive fusion of sensor data and bolstering attitude estimation accuracy. The culmination of the GF-LIO framework involves factor graph optimization, a sophisticated technique that synthesizes LiDAR odometry, IMU pre-integration factors, and loop closure detection factors. This optimization step enhances the overall precision and consistency of the localization process, resulting in superior performance compared to existing methodologies. Experimental evaluations conducted within underground parking environments corroborate the efficacy of the GF-LIO model. Comparative analyses against established approaches such as A-LOAM, LeGO-LOAM, LIO-LOAM, and FAST-LIO demonstrate a notable performance improvement exceeding 12.53%. The proposed model adeptly integrates domain-specific environmental characteristics with multi-sensor data, thereby facilitating precise localization and map construction tasks for intelligent vehicle navigating within the intricate confines of subterranean parking structures.

Inversion of Crop Water Content Using Multispectral Data and Machine Learning Algorithms in the North China Plain

(1) Background: Accurate inversion of crop water content is key to making an intelligent irrigation decision. However, little effort has been devoted to accurately estimating the crop water content of winter wheat in the North China Plain. (2) Method: The crop water content of winter wheat was measured at jointing, flowering and grain-filling stages, respectively. UAV-based multispectral remote sensing images were used to calculate thirteen vegetation indices, including SAVI, EVI, R-M, NDRE, OSAVI, GOSAVI, REOSAVI, GBNDVI, NDVI, RVI, DVI, GNDVI, and TVI. Five machine learning (ML) algorithms (i.e., MLR, RF, PLSR, ElasticNet, and ridge regression) were adopted to estimate the crop water content of winter wheat at the three growth stages. The benchmark datasets, which include CWC as well as vegetation indices calculated based on spectral indices, were adopted to validate the performance of the ML models. (3) Results: The correlation coefficients ranged from 0.64 to 0.82 at different growth stages. The optimal vegetation indices were GNDVI for the jointing stage, NDRE for the flowering and the grain-filling stage, respectively. Among the five machine learning methods, random forest (RF) showed the best performance across the three growth stages, with its coefficient of determination (R2) of 0.80, or an increase by 20.1% than those of other models. In addition, the RMSE and RPD of the RF model at the flowering stage were 3.00% and 2.01, which significantly outperformed other models and growth stages. (4) Conclusion: This study may provide theoretical support and technical guidance for monitoring current water status in wheat crops, which is useful to develop a precise irrigation prescription map for local farmers. (5) Limitation: The main limitation of this study is that the sample size is relatively small and may not fully reflect the characteristics of the target groups. At the same time, subjectivity and bias may exist in the data collection, which may have a certain impact on the accuracy of the results. Future studies could consider expanding sample sizes and improving data collection methods to overcome these limitations.

Study of the patterns of ice lake variation and the factors influencing these changes in the western Nyingchi area

Abstract The current ice lake dataset in the western region of Nyingchi requires further improvement. Due to the intricate distribution of ice lakes and imprecise boundary delineation, research tends to overlook small-scale ice lakes in this area. Moreover, most related studies have focused solely on variations in ice lake areas within key regions, such as the Himalayas, with little attention given to changes occurring in southeastern Tibet. The frequency of ice and snow disasters in the study area has been steadily increasing over the years. Therefore, this study utilizes Landsat satellite images and employs visual interpretation methods to generate more precise and comprehensive maps depicting the distribution of ice lakes in the western region of Nyingchi Province for the years 1994, 2010, 2018, and 2022. Additionally, changes in scale and spatial patterns of different types of ice lakes were investigated. Between 1994 and 2022, the ice lake area in the study area significantly increased by 22.5%, reaching a total of 35.8 ± 3.0 km2. This expansion was primarily driven by glacier-fed lakes, which experienced a remarkable growth rate of 30.8%. In contrast, the non-glacier-fed lakes experienced an increase by only 15.6%. Notably, ice lakes at higher elevations exhibited a peak in expansion, with those above 5143.0 m experiencing the most substantial growth rate of 44.8%. The long-term expansion rate of ice lakes is investigated through the measurement of changes in their boundaries, with the aim to understand the factors contributing to their growth. These findings indicate the rapid expansion of the ice lake near the glacier, with an annual growth rate of 1.3% per annum. Specifically, the glacial-fed section exhibited an expansion rate of 1.1% per annum, while the nonglacial-fed section experienced a growth rate of 0.6% per annum. The seasonal variability in marine glaciers is the primary factor influencing the expansion of ice lakes in this region, with temperature and precipitation serving as the principal driving forces impacting the transformation of these lakes. The data provided by the research results will facilitate a comprehensive understanding of the dynamics and mechanisms governing the ice lake in western Nyingchi, thereby contributing to an enhanced scientific comprehension of potential disaster risks associated with this ice lake.

Efficient 3D robotic mapping and navigation method in complex construction environments

AbstractRecent advancements in construction robotics have significantly transformed the construction industry by delivering safer and more efficient solutions for handling complex and hazardous tasks. Despite these innovations, ensuring safe robotic navigation in intricate indoor construction environments, such as attics, remains a significant challenge. This study introduces a robust 3‐dimensional (3D) robotic mapping and navigation method specifically tailored for these environments. Utilizing light detection and ranging, simultaneous localization and mapping, and neural networks, this method generates precise 3D maps. It also combines grid‐based pathfinding with deep reinforcement learning to enhance navigation and obstacle avoidance in dynamic and complex construction settings. An evaluation conducted in a simulated attic environment—characterized by various truss structures and continuously changing obstacles—affirms the method's efficacy. Compared to established benchmarks, this method not only achieves over 95% mapping accuracy but also improves navigation accuracy by 10% and boosts both efficiency and safety margins by over 30%.

Remote sensing and magnetic characterization of the Au mineralization and its structural implications: Meatiq dome, Eastern Desert, Egypt

This article examines hydrothermally altered ophiolitic ultramafic rocks (HAOU), specifically Listvenite, which are thrust over the Meatiq dome in the context of gold mineralization. These rocks represent gneissic complexes located in Egypt's eastern desert. The analyses presented herein are essential for understanding the distribution of sheared serpentinite on and beneath the surface and the underlying domal structure. This study offers critical insights into the distribution of serpentinite at Meatiq. It combines remote sensing, aerial and ground magnetic data with petrological, geochemical, and geological analyses to create precise geological maps of potential subsurface igneous structures commonly linked to gold mineralization. Remote sensing is used to test for rock differentiation; intensive field geological investigations were conducted along several traverses. Petrographic and geochemical analysis of selected samples confirmed Au content in some localities. Additionally, tomographic inversion of the collected magnetic land profiles has unveiled previously unidentified subsurface distributions of magnetic susceptibilities, which are essential for explaining the observed surface magnetic anomalies and for understanding the subsurface arrangement of various rock units. Results show that the HAOU rocks have a lower magnetic susceptibility signature relative to the adjacent serpentinites, the serpentinite from South Meatiq shows relatively high gold content, and the gold content decreases with carbonation and alteration of the serpentinite into talc-carbonate, as detected geochemically. The procedure followed in the present study can be regionally applied to studying HAOU rocks of similar geologic conditions. The novelty of this study, beyond the introduction of a novel workflow, lies in the revelation that the rocks forming the Meatiq dome are in thrust contact with steeply dipping suprastructure units.

A Novel Characterization Methodology for Vapor-Injected Compressors: A Comparative Analysis with Existing Black-Box Models

In regions characterized by high temperature gradients, vapor compression systems often necessitate operation at very high-pressure ratios resulting in a reduction in system capacity. Economized vapor injection compressors are used to avoid these issues, yet a precise predictive map for various compressor technologies with minimal data and relatively better performance remains unclear. This paper establishes a black-box compressor model to accurately predict compressor power, injection mass ratio, and evaporator mass flow rate in compressors with a single vapor injection port. This model is compared against three legacy models from literature and the ANN model, for reference. All five models are evaluated based on their ability to predict the aforementioned metrics. The proposed black-box model can predict the relevant metrics all within 5% Mean Absolute Percentage Error (MAPE). Additionally, a refrigerant sensitivity analysis is performed with the black-box models. The model is trained using data from R410A and then used the coefficients to predict the performance of the same compressor when using R454B, and vice versa. It can estimate the evaporator mass flow rate with an accuracy within 3%, the power within 2%, and the injection mass ratio with MAPE less than 3%.

Building detection in VHR remote sensing images using a novel dual attention residual-based U-Net (DAttResU-Net): An application to generating building change maps

In today's era, increasing access to very high-resolution remote sensing images (VHR-RSIs) has enhanced building detection and change assessment capabilities. These applications provide accurate urban mapping, facilitate effective land management, and support disaster assessment by delivering detailed insights into building structures and their temporal changes. This study uses a two-stage process to present a pioneering approach for generating precise building maps (BMs) and subsequent building change maps (BCMs) from VHR-RSIs. The primary question addressed by the research is how to enhance the U-Net architecture to improve its sensitivity to both high-level semantic features (HLSF) and low-level spatial features (LLSF) in the building detection task. For this purpose, in the initial stage of the method, a novel deep learning model called dual attention residual-based U-Net (DAttResU-Net) is introduced. This model incorporates two significant modifications to the conventional U-Net, enhancing its capacity to yield bi-temporal BMs. Firstly, each standard convolutional block (CB) is replaced with an optimized CB incorporating a channel-spatial attention module attuned to the building objects' crucial HLSF. Secondly, an additional attention module is integrated into the encoder-decoder path of the model, heightening the sensitivity of U-Net to vital LLSF of buildings while disregarding extraneous background spatial information during the fusion of HLSF and LLSF. In the subsequent stage, the bi-temporal BMs generated by the DAttResU-Net are subjected to a box-based class-object change detection methodology to produce accurate BCMs. The effectiveness of the proposed architecture is rigorously evaluated against state-of-the-art models in both BM and BCM generation contexts, utilizing the well-established WHU dataset for experimentation. The experimental results indicated that the DAttResU-Net model, boasting an average of PFN/ PFP value of 2.33/1.34 (%) surpasses the performance of the state-of-the-art models in generating bi-temporal BMs. Furthermore, the building change detection outcomes demonstrated the proficient role of the bi-temporal BMs predicted by the proposed model in leading to the most optimal BCMs, exhibiting average PFN/ PFP value of 2.63/8.93 (%), outperforming comparative networks. Finally, we concluded that the proposed DAttResU-Net architecture is a highly promising and applicable model for producing reliable BMs and BCMs.

Improving rapid flood impact assessment: An enhanced multi-sensor approach including a new flood mapping method based on Sentinel-2 data

Rapid flood impact assessment methods need complete and accurate flood maps to provide reliable information for disaster risk management, in particular for emergency response and recovery and reconstruction plans. With the aim of improving the rapid assessment of flood impacts, this work presents a new impact assessment method characterized by an enhanced satellite multi-sensor approach for flood mapping, which improves the characterization of the hazard. This includes a novel flood mapping method based on the new multi-temporal Modified Normalized Difference Water Index (MNDWI) that uses multi-temporal statistics computed on time-series of Sentinel-2 multi-spectral satellite images. The multi-temporal aspect of the MNDWI improves characterization of land cover over time and enhances the temporary flooded areas, which can be extracted through a thresholding technique, allowing the delineation of more precise and complete flood maps. The methodology, if implemented in cloud-based environments such as Google Earth Engine (GEE), is computationally light and robust, allowing the derivation of flood maps in matters of minutes, also for large areas.The flood mapping and impact assessment method has been applied to the seasonal flood occurred in South Sudan in 2020, using Sentinel-1, Sentinel-2 and PlanetScope satellite imagery. Flood impacts were assessed considering damages to buildings, roads, and cropland. The multi-sensor approach estimated an impact of 57.4 million USD (considering a middle-bound scenario), higher than what estimated by using Sentinel-1 data only, and Sentinel-2 data only (respectively 24% and 78% of the estimation resulting from the multi-sensor approach). This work highlights the effectiveness and importance of considering multi-source satellite data for flood mapping in a context of disaster risk management, to better inform disaster response, recovery and reconstruction plans.

A Comparative Analysis of Remote Sensing Estimation of Aboveground Biomass in Boreal Forests Using Machine Learning Modeling and Environmental Data

It is crucial to have precise and current maps of aboveground biomass (AGB) in boreal forests to accurately track global carbon levels and develop effective plans for addressing climate change. Remote sensing as a cost-effective tool offers the potential to update AGB maps for boreal forests in real time. This study evaluates different machine learning algorithms, namely Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), for predicting AGB in boreal forests. Conducted in the Qilian Mountains, northwest China, the study integrated field measurements, space-borne LiDAR, optical remote sensing, and environmental data to develop a training dataset. Among 34 variables, 22 were selected for AGB estimation modeling. Our findings revealed that the LightGBM AGB model had the highest level of accuracy (R2 = 0.84, RMSE = 15.32 Mg/ha), outperforming the XGBoost, RF, and SVR AGB models. Notably, the LightGBM AGB model effectively addressed issues of underestimation and overestimation. We also observed that the disparity in accuracy among the models widens with increasing altitude. Remarkably, the LightGBM AGB model consistently demonstrates optimal performance across all elevation gradients, with residuals generally below 25 Mg/ha for low-value overestimation and below −38 Mg/ha for high-value underestimation. The model developed in this study presents a viable and alternative approach for enhancing AGB estimation accuracy in boreal forests based on remote sensing technology.

B1-MRF: Large dynamic range MRF-based absolute mapping in the human body at 7T.

This study aims to map the transmit magnetic field ( ) in the human body at 7T using MR fingerprinting (MRF), with a focus on achieving high accuracy and precision across a large dynamic range, particularly at low flip angles (FAs). A FLASH-based MRF sequence (B1-MRF) with high sensitivity was developed. Phantom and in vivo abdominal imaging were performed at 7T, and the results were compared with established reference methods, including a slow but precise preparation-based method (PEX), saturated TurboFLASH (satTFL), actual flip angle imaging (AFI) and Bloch-Siegert shift (BSS). The MRF signal curve was highly sensitive to , while T1 sensitivity was comparatively low. The phantom experiment showed good agreement of to PEX for a T1 range of 204-1691 ms evaluated at FAs from 0° to 70°. Compared to the references, a dynamic range increase larger than a factor of two was determined experimentally. In vivo liver scans showed a strong correlation between B1-MRF, satTFL, and RPE-AFI in a low body mass index (BMI) subject (18.1 kg/m2). However, in larger BMI subjects (≥25.5 kg/m2), inconsistencies were observed in low regions for satTFL and RPE-AFI, while B1-MRF still provided consistent results in these regions. B1-MRF provides accurate and precise maps over a wide range of FAs, surpassing the capabilities of existing methods in the FA range < 60°. Its enhanced sensitivity at low FAs is advantageous for various applications requiring precise estimates, potentially advancing the frontiers of ultra-high field (UHF) body imaging at 7T and beyond.

Optimization-based approach for high-fidelity phase retrieval from sparse interferometric data: implications for industry and biological research

Optical profilometers provide a non-contact, non-destructive method for swiftly profiling 3D surfaces. White light interferometers, often used for this purpose, employ a 5-phase shifting technique for precise phase maps. However, capturing multiple frames introduces mechanical movement, which impedes imaging of dynamic objects. White light’s low spatial-temporal coherence mitigates speckles and spurious fringes while offering high axial resolution. Creating a high fringe density interferogram with low-coherence light is challenging. Introducing a tilt angle in the interferometer can increase the fringe density, which is still insufficient for phase map retrieval using the single-shot Fourier transform method. We propose an adaptive optimization framework to recover phase maps from single low fringe density interferograms. This method iteratively extracts reference beam information, eliminating mechanical movement and enhancing system stability while reducing costs and system bulkiness. The simulation and experimental results on a step-phase object (etched on silicon) and biological MG63 osteosarcoma cells validate the efficacy of a single-shot optimization scheme. For comparison, the phase maps of the same objects were obtained using the single-shot Fourier transform and multi-shot 5-phase shifted methods. The single-shot optimization technique shows efficient performance, yielding phase maps with reasonable accuracy, potentially replacing the 5-phase shifting technique in industrial and biological diagnostics.

Glacier dynamic study of the Terre de Lambert glacier in Greenland based on Landsat 8 and Sentinel-1 data

With global temperatures on the rise, the trend of glacial melting is becoming increasingly evident. This study focuses on the northeastern region of Greenland as the research subject, utilizing remote sensing technology and geographic information technology to study glacial dynamics. Initially, the research processes Landsat 8 data using QGIS to classify the widespread snow and ice cover, facilitating the observation and analysis of its temporal variations. The study then narrows its focus to Terre de Lambert for further analysis. Subsequently, using satellite data from Sentinel-1 and employing SNAP, the research conducts an in-depth analysis of glacier flow speed in specific regions. Through intricate computational models and algorithms, precise glacier flow direction and speed maps are generated. By combining historical data, the study compares and analyzes the glacier flow speed between 2018 and from June 20th to July 14th, 2023. Results indicate that the glacier flow speed in 2023 during the same time frame was faster. Throughout the research process, limitations in current data acquisition and processing methods were identified. Thus, the paper suggests improvements to data acquisition and processing tools to enhance the efficiency of glacier flow speed studies. Theres a fervent hope for the emergence of more convenient, streamlined, and customizable temporal dimension tools for glacier flow speed data in the future. The research findings aim to draw global citizens attention to the issue of glacial melting and inspire collective actions to protect our planet.

A novel method for aero-engine map calibration using adaptation factor surface

High-fidelity component-level models (CLMs) are essential for aero-engine performance modeling, monitoring, and diagnosis, relying heavily on precise component maps. Inaccuracies in these maps can cause significant deviations between predicted and actual measurements. This study introduces a novel wide-range performance adaptation method (WPAM) utilizing adaptation factor surfaces, enabling precise adjustment of speed line operating points. The adaptation factors for the component maps are calculated based on both steady-state and transient measurements using the Levenberg-Marquardt method, with transient adaptation calculations converted into a steady-state solution. Adaptation factor surfaces are constructed using the moving least squares method to modify component maps in both spool speed and β-value directions. Validated on two engines with distinct maps, WPAM significantly enhances CLM accuracy under steady-state and transient conditions, considering multidimensional engine and CLM map discrepancies. It is applicable to degraded and variable geometry engines, exhibiting robustness in handling noisy measurement data.

Exploring multi-pollution variability in the urban environment: geospatial AI-driven modeling of air and noise

ABSTRACT This study addresses the critical need for comprehensive multi-pollution modeling in urban environments by employing Geospatial Artificial Intelligence (GeoAI) techniques. Specifically, it aims to elucidate the variability of air and noise pollution levels in Tehran, Iran, and develop precise multi-pollution susceptibility maps. A spatial database was compiled, encompassing annual average data (2019–2022) of the Air Quality Index (AQI) and Equivalent Continuous Sound Level (Leq), along with 19 influential factors related to both pollutants. The study utilized the CatBoost machine learning algorithm, enhanced with Grey Wolf Optimizer (GWO) and Harris Hawks Optimization (HHO) algorithms, to model and predict pollution patterns. Key quantitative achievements include a remarkable 15% reduction in Root Mean Square Error (RMSE) for air pollution and a 12% reduction in noise pollution when using the CatBoost-HHO configuration compared to the standalone CatBoost algorithm. Validation of the multi-pollution maps utilizing the Area Under the Curve (AUC) analysis showed high accuracy, with the CatBoost-HHO achieving AUC values of 0.943 for air pollution and 0.936 for noise pollution, outperforming CatBoost-GWO and standalone CatBoost models. These results underscore the efficacy of our approach in accurately identifying and mapping multi-pollution hotspots, contributing valuable insights for urban environmental management and public health preservation.

Assessment of the Accuracy of Various Machine Learning Algorithms for Classifying Urban Areas through Google Earth Engine: A Case Study of Kabul City, Afghanistan

Accurate identification of urban land use and land cover (LULC) is important for successful urban planning and management. Although previous studies have explored the capabilities of machine learning (ML) algorithms for mapping urban LULC, identifying the best algorithm for extracting specific LULC classes in different time periods and locations remains a challenge. In this research, three machine learning algorithms were employed on a cloud-based system to categorize urban land use of Kabul city through satellite images from Landsat-8 and Sentinel-2 taken in 2023. The most advanced method of generating accurate and informative LULC maps from various satellite data and presenting accurate outcomes is the machine learning algorithm in Google Earth Engine (GEE). The objective of the research was to assess the precision and efficiency of various machine learning techniques, such as random forest (RF), support vector machine (SVM), and classification and regression tree (CART), in producing dependable LULC maps for urban regions by analyzing optical satellite images of sentinel and Landsat taken in 2023. The urban area was divided into five classes: built-up area, vegetation, bare-land, soil, and water bodies. The accuracy and validation of all three algorithms were evaluated. The RF classifier showed the highest overall accuracy of 93.99% and 94.42% for Landsat-8 and Sentinel-2, respectively, while SVM and CART had lower overall accuracies of 87.02%, 81.12%, and 91.52%, 87.77%, with Landsat-8 and Sentinel-2, respectively. The results of the present study revealed that in this classification and comparison, RF performed better than SVM and CART for classifying urban territory for Landsat-8 and Sentinel-2 using GEE. Furthermore, the study highlights the importance of comparing the performance of different algorithms before selecting one and suggests that using multiple methods simultaneously can lead to the most precise map.

Two-stage fusion strategy and residual shrinkage capsule network for SAR image change detection

ABSTRACT Acquiring precise change map for synthetic aperture radar (SAR) images poses challenges due to the absence of labelled datasets and the presence of speckle noise interference. This paper presents a two-stage fusion strategy coupled with capsule network classification detection method. Initially, to enhance the structure of change areas within the difference image (DI), we propose an energy ratio (ER) algorithm based on the mean ratio (MR). Subsequently, employing a two-stage fusion strategy integrating multiplication and discrete wavelet transform (DWT), we fuse the DIs generated by the three algorithms to effectively complement change information across distinct DIs. In addition, a saliency detection algorithm is introduced to mitigate noise interference and balance the number of samples. Hierarchical fuzzy c-means clustering (HFCM) is then used to pre-classify the salient fusion difference image (SFDI) and derive pseudo-labels. Finally, we apply the residual shrinkage capsule network (RSCN) to reclassify pre-classification results and obtain the change detection outcome, capable of extracting the multi-scale refinement features and adaptively learning discriminant information from changed and unchanged regions. Experimental evaluations on various SAR image datasets demonstrate the exceptional detection performance of the proposed method.

A multi‐focus image fusion network deployed in smart city target detection

AbstractIn the global monitoring of smart cities, the demands of global object detection systems based on cloud and fog computing in intelligent systems can be satisfied by photographs with globally recognized properties. Nevertheless, conventional techniques are constrained by the imaging depth of field and can produce artefacts or indistinct borders, which can be disastrous for accurately detecting the object. In light of this, this paper proposes an artificial intelligence‐based gradient learning network that gathers and enhances domain information at different sizes in order to produce globally focused fusion results. Gradient features, which provide a lot of boundary information, can eliminate the problem of border artefacts and blur in multi‐focus fusion. The multiple‐receptive module (MRM) facilitates effective information sharing and enables the capture of object properties at different scales. In addition, with the assistance of the global enhancement module (GEM), the network can effectively combine the scale features and gradient data from various receptive fields and reinforce the features to provide precise decision maps. Numerous experiments have demonstrated that our approach outperforms the seven most sophisticated algorithms currently in use.

Real-time map rendering and interaction: a stylized hierarchical symbol model

ABSTRACT Digital maps, serving as pivotal instruments in the realm of geographic information services, adeptly encapsulate spatial data through refined graphics and symbols. Widely used across various domains such as smart devices, in-vehicle navigation systems, unmanned aerial vehicles (UAV), electronic gaming, and virtual reality, these maps face the ongoing challenge of delivering real-time, precise, and efficacious map rendering and interaction experiences. In response to this challenge, this study introduces a Stylized Hierarchical Symbol structure (SHS) and a Geographic Feature Color-weighted Rendering technique (GFCR). The approach is further complemented by incorporating Adaptive Hierarchy-Weighted caching (AHie), Catalan Number-based Caching access (CatNCa), and Grid-based Dynamic caching loading (GriD). Experimental results demonstrate the method's effectiveness in addressing issues related to timeliness, accuracy, and logical correctness in mobile map environments, enhancing the real-time interaction experience of mobile digital maps. Notably, this method imposes modest hardware and storage requirements, making it applicable to a wide range of scenarios including natural resource planning, intricate road landscapes, route navigation challenges, and emerging fields such as autonomous driving, virtual reality, and the metaverse.