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Impact craters are crucial for our understanding of planetary resources, geological ages, and the history of evolution. We designed a novel pseudo-spectral spatial feature extraction and enhanced fusion (PSEF) method with the YOLO network to address the problems encountered during the detection of the numerous and densely distributed meter-sized impact craters on the lunar surface. The illumination incidence edge features, isotropic edge features, and eigen frequency features are extracted by Sobel filtering, LoG filtering, and frequency domain bandpass filtering, respectively. Then, the PSEF images are created by pseudo-spectral spatial techniques to preserve additional details from the original DOM data. Moreover, we conducted experiments using the DES method to optimize the post-processing parameters of the models, thereby determining the parameter ranges for practical deployment. Compared with the Basal model, the PSEF model exhibited superior performance, as indicated by multiple measurement metrics, including the precision, recall, F1-score, mAP, and robustness, etc. Additionally, a statistical analysis of the error metrics of the predicted bounding boxes shows that the PSEF model performance is excellent in predicting the size, shape, and location of impact craters. These advancements offer a more accurate and consistent method to detect the meter-sized craters on planetary surfaces, providing crucial support for the exploration and study of celestial bodies in our solar system. [ABSTRACT FROM AUTHOR]

The introduction of unmanned aerial vehicles (UAVs) and their deployment as tools for terrain information gathering has prompted discussions regarding the quality of the resulting products and the potential for their extended applications in geomatics. This study specifically focuses on evaluating the utilization and capabilities of UAV photogrammetry in generating topographic maps, with a primary emphasis on assessing the accuracy of these maps. An area of approximately 2.65 km2 of the New Administrative Capital in Cairo, Egypt, was captured using a DJI M300 RTK drone. Agisoft Metashape and Pix4Dmapper software were used to process digital images to produce orthophotos and digital surface models. The precision of direct geo-referencing was evaluated by utilizing common tie points extracted by the user and the onboard GPS camera positions recorded during flight. Horizontal and vertical RMSEs were measured at 0.069 m and 0.593 m for Pix4Dmapper and 0.062 m and 0.548 m for Agisoft Metashape, respectively. Notably, direct geo-referencing exhibited lower accuracy, particularly in the vertical dimension. In the 7-GCP scenario, when combined with indirect geo-referencing, both software packages achieved the highest accuracy, with radial RMSEs of 0.023 m and 0.054 m and vertical RMSEs of 0.038 m and 0.052 m, respectively, corresponding to a pixel size of 1.7–2.2 times the ground sampling distance (GSD). In summary, this study affirms the efficacy of utilizing UAV photogrammetry in creating digital maps, orthophotos, contour lines, digital terrain models, digital surface models, and line maps, all aligning with globally recognized standards. [ABSTRACT FROM AUTHOR]

Structure-from-Motion (SfM) Photogrammetry-based unmanned aerial vehicles (UAV) are considered one of the most effective non-destructive techniques to detect features in archaeology. However, relying on only standalone-based photogrammetric approaches is not always valid in revealing new archaeological features and monuments. This study aims to verify the efficiency of the integrationbased methodologies of SfM photogrammetry products to improve the detection of new archaeological features in the non-excavated regions of the archaeological site of Ur, south of Iraq. The methodology proposes an automatic workflow to extract 3D topographic models such as Digital Terrain Model (DTM) by Agisoft Metashape (MS) software and orthomosaic images as standard products for UAVphotogrammetry. These have delivered the following Structure-from-Motion and Multi-View Stereo (SfM-MVS) algorithms using an optimized flight planning, data capturing, and data processing pipeline. Stand-alone raster images derived from photogrammetry, such as (slope images, hill shade rasters, etc.), have limitations in archaeology compared to hybrid raster images in detecting covered and unrevealed features. The methodology focuses on fusing multiple rasters extracted from DTM using the QGIS software package analysis tools to detect features that may never be detected from single standalone image rasters. This is because combining multiple rasters of different parameters may highlight new potential features such as paths, temples, etc. The results findings revealed that the fusion methodology of raster images obtained from UAV-SfM Photogrammetry through its standard products and using high precision settings could successfully help to identify many new archaeological features in the undiscovered site in Ur, such as walls and houses, which may date back to the late periods of Ur. The results concluded that this enhances the role of photogrammetry and remote sensing techniques as nondestructive excavation tools to provide a rich database for archaeologists for further analysis and interpretation of undiscovered archaeological sites without destruction. Many new archaeological features were also revealed, and a general digital map of the site was prepared. It also prompts a review of the settlement theories in previous studies in the ancient city of UR and the documentation of excavated archaeological ruins. [ABSTRACT FROM AUTHOR]

Many studies have incorporated concept maps into digital games to enable learners to make connections between subject concepts in the game. However, most learners do not associate spontaneously with the thematic concepts in the game but need to be facilitated by effective scaffolding mechanisms to reconceptualize the learning process and content. Therefore, this study proposed a knowledge check-based concept map-guided gaming strategy for chemistry learning for secondary-level learners. Specifically, the knowledge check-based concept map-guided gaming strategy was employed as a learning strategy while playing digital games. The knowledge check mechanismwas developed based on the notation of two-tier testing. Moreover, the learning environment was a self-developed chemical classification-based digital game, in which learners learned through concept mapping guided gaming tasks with factual and conceptual questions to confirm that they can actually learn content and conceptual understanding in the game. To understand the effects of the proposed strategy, a quasi-experimental design was adopted to measure compare two groups' learning achievement, problem-solving awareness, critical thinking tendency, and germane cognitive load of seventh-grade students using the knowledge check-based concept map-guided gaming strategy and the conventional concept map-guided gaming strategy. The results of the study showed that learners' learning achievement, problem-solving awareness, critical thinking tendency, and germane cognitive load in chemistry learning were effectively improved by the knowledge check-based concept map-guided gaming strategy. Furthermore, this study implies that teachers can adjust their teaching scaffolds and provide strategies according to learners' learning behaviors in digital game-based learning. [ABSTRACT FROM AUTHOR]

Map matching is a technology that aligns users' GPS position sequence with the road network on a digital map. Under low-sampling-rate conditions, existing interactive voting-based map matching algorithm leads to mismatching and low matching efficiency. Considering such problems, this paper proposes an improved interactive voting-based map matching algorithm considering path correlation by optimizing the observation probability and transition probability formulas to improve spatiotemporal analysis. Average speed and sampling time are used to estimate the path length and analyze the correlation between the estimated path and the actual path in order to reduce mismatching and improve the accuracy of matching. Utilizing three constraint conditions to filter erroneous candidate road segments improves accuracy and reduces matching time. The experimental results show that under various road conditions, the improved algorithm outperforms the compared algorithms. The matching accuracy can be maintained at over 90%, and the matching time is reduced by about 1ms compared to the comparison algorithms. [ABSTRACT FROM AUTHOR]

Improving the quality of life for people with vision impairments has been an important goal in the research and design of assistive devices for several decades. This paper seeks to further that goal by introducing a novel assistive technology platform that leverages real-time 3D spatial audio to promote safe and efficient navigation for people who are blind or visually impaired (PVI). The presented platform, EchoSee, uses modern 3D scanning technology on a mobile device to construct a live, digital 3D map of a user's environment as they move about their surroundings. Spatialized, virtual audio sources (i.e., virtual speakers) are dynamically placed within the digital 3D scan of the world, providing the navigator with a real-time 3D stereo audio "soundscape." The digital 3D map, and its resultant soundscape, are continuously updated as the user moves about their environment. The generated soundscape is played back through headphones connected to the navigator's device. This paper details (1) the underlying technical components and how they were integrated to produce the mobile application that generates a dynamic soundscape on a consumer mobile device, (2) a methodology for analyzing navigation performance with the application, (3) the design and execution of a user study investigating the effectiveness of the presented system, and (4) a discussion of the results of that study along with a proposed future study and possible improvements. Altogether, this paper presents a novel software platform aimed at assisting individuals with vision impairments to navigate and understand spaces safely, efficiently, and independently and the results of a feasibility study analyzing the viability of the approach. [ABSTRACT FROM AUTHOR]

Technology implementation, particularly the use of Unmanned Aerial Vehicles (UAVs) and photogrammetry, is being employed in road works for regional and road planning. The current study deploys aerial photographs and data processing along with software, like Agisoft Metashape, PCI Geomatica, Global Mapper, and Autocad Civil 3D as an efficient and effective way to generate digital maps and perform geometric road reviews. The accuracy test of CE90 performed for horizontal accuracy was 0.003 m and the LE90 carried out for vertical accuracy was 0.006 m. This accuracy level is valuable for road planning, ensuring that the data utilized for decision-making are reliable and precise. The study focused on Wori Street spanning from Pandu to Kima Atas Street Manado, covering the section from Sta. 0+000 to Sta. 5+225, which is a collector road with a designated speed of 50 km/h. Among the 16 bends analyzed, 11 met highways’ standards for the collector road class, certifying compliance with safety guidelines. Furthermore, the existing road slope conforms to standard requirements, remaining below 8%. This adherence to safety criteria is vital for the design and operation of safe roads. [ABSTRACT FROM AUTHOR]

The groundwater salinization process complexity and the lack of data on its controlling factors are the main challenges for accurate predictions and mapping of aquifer salinity. For this purpose, effective machine learning (ML) methodologies are employed for effective modeling and mapping of groundwater salinity (GWS) in the Mio-Pliocene aquifer in the Sidi Okba region, Algeria, based on limited dataset of electrical conductivity (EC) measurements and readily available digital elevation model (DEM) derivatives. The dataset was randomly split into training (70%) and testing (30%) sets, and three wrapper selection methods, recursive feature elimination (RFE), forward feature selection (FFS), and backward feature selection (BFS) are applied to train the data. The resulting combinations are used as inputs for five ML models, namely random forest (RF), hybrid neuro-fuzzy inference system (HyFIS), K-nearest neighbors (KNN), cubist regression model (CRM), and support vector machine (SVM). The best-performing model is identified and applied to predict and map GWS across the entire study area. It is highlighted that the applied methods yield input variation combinations as critical factors that are often overlocked by many researchers, which substantially impacts the models' accuracy. Among different alternatives the RF model emerged as the most effective for predicting and mapping GWS in the study area, which led to the high performance in both the training (RMSE = 1.016, R = 0.854, and MAE = 0.759) and testing (RMSE = 1.069, R = 0.831, and MAE = 0.921) phases. The generated digital map highlighted the alarming situation regarding excessive GWS levels in the study area, particularly in zones of low elevations and far from the Foum Elgherza dam and Elbiraz wadi. Overall, this study represents a significant advancement over previous approaches, offering enhanced predictive performance for GWS with the minimum number of input variables. [ABSTRACT FROM AUTHOR]

Farmland boundary data, an important basic data for the operation of agricultural automation equipment, has been widely studied by scholars from all over the world. However, the common methods of farmland boundary acquisition through sensors such as LiDAR and vision cameras combined with complex algorithms suffer from problems such as serious data drift, difficulty in eliminating noise, and inaccurate plot boundary data. In order to solve this problem, this study proposes a method for estimating the orientation dimensions of farmland based on the seeding trajectory. The method firstly calculates the curvature of the discrete data of the seeding trajectory; secondly, we innovatively use a low-pass filter and integral clustering to filter the curvature values and distinguish between straight lines and curves; and finally, the straight-line portion located at the edge of the seeding trajectory is fitted with a univariate linear fit to calculate the estimation of the farmland size orientation. As verified by the field experiments, the minimum linear error of the vertices is only 0.12m, the average error is 0.315m, and the overlapping rate of the plot estimation is 98.36% compared with the real boundary of the plot. Compared with LiDAR mapping, the average linear error of the vertices' position is reduced by 50.2%, and the plot estimation overlap rate is increased by 2.21%. The experimental results show that this method has the advantage of high accuracy, fast calculation speed, and small calculation volume, which provides a simple and accurate method for constructing farmland maps, provides the digital data support for the operation of agricultural automation equipment, and has significance for farm digital mapping. [ABSTRACT FROM AUTHOR]

The characterization and introduction of agroecology is separated into several zones based on the physical conditions of the environment. This study aimed to obtain land suitability for paddy, corn, and soybean‐based information and potential of the local resource, which is a digital map of agroecological zones (AEZs) and crop requirements in Jombang district. The strategy is that land suitability zones for rice, corn, and soybeans can be extrapolated to other areas that have the similar agroecology zones in the Jombang region or other regions in Indonesia. This research includes the following 3 series of activities: (1) data gathering and administration, (2) field activities, and (3) data processing. Information on climate characteristics, plant growth, and needs were obtained from field studies and literature studies and then the information was overlaid using a land use expert system with the ALES (automated land evaluation system) program to determine the ZAE class. The results of this study were that AEZ in Jombang district is dominated by Zone IV with a slope of <8% (66.69%), consisting of paddy, corn, soybeans, and sugarcane commodities. Zone III with a slope of 8–15% (1.77%) consists of mango, banana, and corn commodities. Zone II with a slope of 15–30% (3.38%) consists of durian and mango commodities. The zone above (30–40% slope) is an area with natural vegetation (4.79%). Suitable land (S1) for rice is 37,062 Ha (33.09%) and for corn is 14,698 Ha (13.12%), and quite suitable land (S2) for soyabean, is divided into S2nr (nutrient retention), an area of 10.99%, and S2rc, nr (root media and nutrient retention), and an area of 18.33%. Land suitability class determines the level of land management and technology required to obtain optimal crop yields. Land suitability for paddy, corn, and soybeans based on ZAE in Jombang District can be extrapolated to other areas that have similarities. [ABSTRACT FROM AUTHOR]

Mapping is now thoroughly digital at all stages of production and maps are widely used in digital form. This digital turn has transformed the nature of mapping and maps. Maps need no longer be static representations, but rather constitute spatial media, providing an interactive, dynamic means for creating, discussing, and sharing spatial information and mediating spatial practices. This has included the development of 3D mapping, including nascent digital twins and digital deep maps. In this short paper, we reflect on our attempts to produce a 3D city information model for Dublin that acts as a basic digital twin, which we have also used to explore deep mapping, as well as map projecting data onto a printed 3D map model of the city. We consider what digital twins and deep maps mean for how we understand the nature of mapping, arguing that they produce a dyadic intertwining of map and territory; a literal, material expression of post‐representational, ontogenetic conceptions of mapping. [ABSTRACT FROM AUTHOR]

Programmable quantum simulators may one day outperform classical computers at certain tasks. But at present, the range of viable applications with noisy intermediate-scale quantum (NISQ) devices remains limited by gate errors and the number of high-quality qubits. Here, we develop an approach that places digital NISQ hardware as a versatile platform for simulating multi-dimensional condensed matter systems. Our method encodes a high-dimensional lattice in terms of many-body interactions on a reduced-dimension model, thereby taking full advantage of the exponentially large Hilbert space of the host quantum system. With circuit optimization and error mitigation techniques, we measured on IBM superconducting quantum processors the topological state dynamics and protected mid-gap spectra of higher-order topological lattices, in up to four dimensions, with high accuracy. Our projected resource requirements scale favorably with system size and lattice dimensionality compared to classical computation, suggesting a possible route to useful quantum advantage in the longer term. Previous quantum simulations of higher-order topological phases have utilized synthetic dimensions. Here the authors simulate high-order topological phases on lattices with spatial dimension up to four on a superconducting quantum computer using a mapping to a many-body Hamiltonian in a reduced dimension. [ABSTRACT FROM AUTHOR]

Mobile maps are an important tool for mastering modern digital life. In this paper, we outline our perspective on the challenges and opportunities associated with designing adaptive mobile maps that are useful, usable, and accessible to a wide range of users in different contexts. If we claim for adaptive mobile maps to be successful, we need to expand our understanding of map use context, including the physical and digital spaces, user behavior, and individual differences. We identify key challenges, such as the scarcity of knowledge about mobile map use behavior, the need for effective adaptation methods and strategies, user acceptance of adaptive maps, and issues related to control, privacy, trust, and transparency. We finally suggest research opportunities, such as studying mobile map usage, employing AI-based adaptation methods, leveraging the power of visual communication through maps, and ensuring user acceptance through user control and privacy. [ABSTRACT FROM AUTHOR]

In this paper, we geocode the residences of nineteenth-century Brompton residents to the level of the building in order to argue that literary sociability and propinquity – leading to face-to-face interaction with writers, editors, artists, actors, and publishers – may have played a more important role in the formation of women's literary careers than scholars have yet recognized. Taking the popular novelist and poet Dinah Craik as our case study, we argue that the walkability as well as the informal and inexpensive literary sociability of the area made Brompton a fertile ground for the early careers of nineteenth-century women writers. Following Alan Liu's work in Critical Infrastructure studies, we attempt through mapping to reanimate connections that have been lost to time, establishing the importance of Brompton as a literary, artistic, and intellectual neighbourhood in the nineteenth century, and of propinquity in supporting the careers of women writers. Expanding on Sarah Bilston's recent work on the suburbs as fertile grounds for the careers of Victorian women writers, we add a spatial dimension to Robert Darnton's well-known print network, placing the private home, a site of literary sociability, as a central node in public print networks. [ABSTRACT FROM AUTHOR]

The precise tie-points (TPs) on synthetic aperture radar (SAR) images are a critical cornerstone in the global digital elevation model (DEM) and digital ortho map (DOM) production process. While there are abundant studies on SAR TPs matching, improvement opportunities persist in large areas. The correspondences have pixel-level errors during geocoding, which result in misalignment between global products. Consequently, this paper proposed a robust method for SAR images TPs matching, which consists of three key steps: (1) interest point extraction based on the dynamic Harris area entropy (DHAE) grid; (2) adaptive determination of template size; (3) normalized cross correlation (NCC) template matching. DHAE is a regional texture information grid based on the SAR-Harris map, and it is achieved through dynamic block division. Generating the DHAE grid over SAR images enables the extraction of interest points that have regional feature representation and distribution uniformity. A variable-size matching template is adaptively determined based on DHAE to enhance template quality while maintaining computational efficiency. Subsequently, the NCC algorithm is employed to find subpixel-precise correspondences. The proposed method is applied on TPs matching in 57 Terra-SAR images, which cover a large geographical area. Furthermore, the overlapping area is partitioned into five segments according to different coverage types. The experimental results demonstrate that the proposed method outperforms other template matching methods. For all coverage types, the proposed method exhibits high-precision sub-pixel results that reach up to 38.64% in terms of the relative positioning error (RPE), particularly in texture-weak and large areas. [ABSTRACT FROM AUTHOR]

The soil supports many ecosystem services (ES) essential to human well‐being. Rapid developments in digital soil mapping (DSM) allow the mapping of soil types and soil properties with improved resolution and accuracy. However, the potential of DSM to improve the assessment and mapping of ES is not fully exploited. To better understand this potential, we synthesized the peer‐reviewed literature. We examined what empirical studies reveal about the role of soil properties in the assessment of four major ES provided by the forest: (I) timber production, (II) soil carbon storage, (III) regulation of water flow and provision of clean water, and (IV) the soil as a habitat for organisms. Results revealed that soil properties are strongly related to the provision of ES. Therefore, using DSM could greatly improve the assessment of the ES provided by forests. Several variables were related to specific ES regardless of region or ecosystem types, but others were found to be situation‐specific (climate and soil type) and need to be considered at the proper scale or within a proper land classification framework. DSM products have the potential to greatly improve the assessment of ES by turning qualitative relationships between soil and ES to quantitative ones. This could also lead to the discovery of new soil–ES relationships. For this potential to be realized, progress should be made in mapping the most crucial soil parameters with greater precision and in promoting the use of soil parameters in ES assessment. Core Ideas: Soil properties are major determinants of forest ecosystem services (ES).Advances in digital soil mapping are generating better spatial estimates of soil properties.Soil properties are rarely used to evaluate ES.A literature review identifies how soil property maps could help improve the assessment of four ES.The ES considered are timber production, soil carbon storage, water quality/quantity, and habitat. [ABSTRACT FROM AUTHOR]

This study focuses on the Memorial Chapel, a historical site located inside the Tempio Evangelico Valdese in Florence. In 1843, the first Anglican church in Florence, known as Holy Trinity Church, was built by D. Giraldi. Around 1892, G. F. Bodley began the reconstruction of a new building of neo-Gothic style at the same site, which was completed in 1904. This new church had a space dedicated to memory called Memorial Chapel. In 1967, the monumental complex was acquired by the Waldensians, now known as the Tempio Evangelico Valdese. This interdisciplinary investigation aimed to study the most damaged painted walls of the chapel. For this purpose, samples of decorative plaster mortars were collected from various points, after carrying out a digital mapping of the degraded areas. Mineralogical, petrographic, optical, chemical, and microchemical analyses were performed. This study made it possible to highlight the composition and the characteristics of the different layers of the plaster mortars, permitting us also to identify the types of pigments used over time in the paintings; furthermore, it was possible to reconstruct the degradation phenomena on the walls and the events that caused them, providing valuable insight for targeted restoration efforts. [ABSTRACT FROM AUTHOR]

Path loss is important for the unmanned aerial vehicle (UAV) placement, trajectory optimization, and power allocation in UAV-aided communications. By considering both the factors of path delay and reflection angle (RA) in the non-line-of-sight (NLoS) paths, a new machine learning-based delay–angle-joint path loss prediction method for UAV mmWave channels is proposed. The one-input back propagation based neural network (BPNN) and two-input BPNN are built to predict the path power for line-of-sight (LoS) case and NLoS case, respectively. Meanwhile, a data acquisition method is developed to obtain massive data set for training the BPNN. According to the geometric information of digital map, a calculation method for path delay and RA is also proposed to drive the BPNN. The proposed method is simulated and analyzed based on ray-tracing (RT) simulated data under a typical urban scenario at 28 GHz. The 2D relationship of power–delay for the LoS case and 3D relationship of power–delay–RA for the NLoS case are obtained through the trained BPNN, which are well consistent with the validation set of RT data and outperform the traditional methods, i.e., 3GPP model and exponential model. Moreover, it's found that the path power rapidly decreases when RA is 65 ∘ – 75 ∘ under the simulation scenario, which could be an important reference for transceiver placement and route planning to reduce the impact of NLoS paths in the UAV channel. [ABSTRACT FROM AUTHOR]

This study explores religious diversity in Romania and how the trends of indices of religious fractionalization and polarization manifest at the county level in a post-communist period. The county level was selected as the relevant level for analysis due to its more clearly visible spatial dynamics with regard to religions. Recent studies have revealed that the erosion of diversity is an important aspect for any country and needs to be considered as an important aspect of democracy. This paper highlights how digital mapping of religious polarization and fractionalization in Romania has been generally maintained, although the regions of Transylvania, Banat, Dobrogea and Western Moldova remain areas in which there are different religions. Other than the general aspect of religious diversity depending on ethnic trends, there has been a rise of Pentecostals and Baptists at the county level. This aspect has further implications for our study, which calls for creating new institutions for dialogue between majority Orthodox communities and Protestants, not only so that potential conflicts do not appear between Orthodox and emerging religious communities but also to ensure objective, cultural, religious coherence. Our maps revealed the distribution patterns and the temporal evolution of religious denominations for the 1992–2011 period. The major line of originality in our research rests in the findings that after conducting a bivariate spatial autocorrelation analysis of religious fractionalization and polarization, we were able to identify two distinct clusters with spatial overlap over historical provinces. [ABSTRACT FROM AUTHOR]

Based on digital line graphic and true digital orthophoto map, a large amount of sample data that meets the requirements of deep learning can be automatically generated. However, there are often some erroneous information, which increases the difficulty of training neural network models and limits the improvement of ground feature extraction accuracy. A sample data automatic cleaning method based on selection weight iteration was proposed to address this issue. Firstly, a deep neural network model for data cleaning was constructed, and a network training method based on selection weight iteration was proposed. The method broke the assumption that all samples had the same weight for the calculation of loss function during network model training. The prediction accuracy of the samples during the data cleaning network model training process was used as the weight of the samples to be brought into the network training, and the sample weights were continuously updated through iterative training. Finally, samples with low weights were eliminated to achieve automatic data cleaning and sample database refinement. Training and accuracy comparison experiments were conducted on five classic semantic segmentation network models using a sample database before and after data cleaning. The results show that, the model trained using the sample database after data cleaning improves the average accuracy of building extraction by 2.36%, road extraction by 3.48%, and water extraction by 1.88%. This experiment proves that the data cleaning method proposed in this paper can effectively improve the accuracy of the network model in extracting ground features. [ABSTRACT FROM AUTHOR]

This paper presents a valley path planning algorithm based on the Hybrid A* algorithm. This algorithm is aimed at finding the valley path for aircraft considering dynamics constraints and terrain limitations. The preliminaries involve the establishment of a 3D workspace based on digital elevation map (DEM) data and addressing methods of valley detection. Following this comprehensive groundwork, the Hybrid A*-based algorithm, employed to determine the valley path within the 3D workspace while accommodating dynamic constraints and terrain limitations, is then introduced. In the experimental test, to validate the effectiveness of the algorithm proposed in this paper, we tested the performance of the proposed algorithm and other three baseline algorithms based on four optimization objectives in three workspaces. The simulated results indicate that the algorithm proposed in this paper can effectively find the valley path while considering dynamic constraints and terrain limitations. [ABSTRACT FROM AUTHOR]

This research presents new proposed algorithms for generating digital signatures. The proposed algorithms based on coupling chaotic maps with Schnorr and Elgamal schemes to obtain the private key. These maps, including 2D henon, 2D tinkerbell, and Tent Sine System, generate a sequence of random iterations, each one is then converted into 256-bit integer to be fit as the private key. The key space is increased (2256) compared to the traditional Schnorr and Elgamal (2160) and there become a wide range of digital signatures corresponding to the random iterations. The key space development increases the security level of the signature scheme which in turn makes it difficult for any adversary to hack the scheme. Also, the results proved that our new algorithm takes less signing and verification time compared to other proposed algorithms. It was proved that our proposed algorithms don't require large number of keys for signing or verification. It is just one private key and another public. [ABSTRACT FROM AUTHOR]

Located in the vast Algerian Sahara, Bechar has experienced over the years uncontrolled urban growth. It has evolved from a simple oasis and ksar (traditional settlement) to a city with a contrasting center and segregated outskirts. This growth has led to significant urban fragmentation that affected residents' quality of life. This research uses space syntax theory to analyze Bechar's urban configurations and their impact on functionality and sustainability. The study examines integration, connectivity, control, and intelligibility through axial maps and syntactic measures. Findings reveal that areas with high integration show better accessibility and reduced vehicle reliance, while segregated areas face accessibility challenges and limited services. Global integration values range from −1 to 1.192, averaging at 0.767, which indicates different accessibility among neighborhoods. These insights are crucial to identify areas where sustainable urban planning can mitigate adverse effects and enhance city efficiency. We created an axial map using DepthmapX v0.8.0 software and a modified digital QGIS map to collect data and measure Bechar's urban properties. The methodology we used translates social behaviors into spatial representations, generating interpretative models for social and spatial phenomena. This article provides valuable information on the infrastructure and service needs, guiding strategic resource allocation. The proposed urban design strategies include urban canopies and optimized building ratios to reduce energy consumption and promote sustainability. To conclude, we recommend practical solutions to enhance the sustainability and functioning of urban areas similar to Bechar. [ABSTRACT FROM AUTHOR]

Data Element Mapping and Analysis (DEMA) represents a new and systematic methodology for the standardized capture, mapping, and analysis of data threads essential for comprehending digital systems and their architecture. This research studies the synergies between DEMA and Systems Modeling Language (SysML). The study aims to enable the systematic creation of digital artifacts using SysML. The results show that DEMA can serve as a complementary tool, enhancing the creation of SysML models by improving knowledge capture and verification processes. The prototype DEMA to SysML framework presented in this work provides the foundation for a systematic methodology to enable the systematic creation of models using SysML. [ABSTRACT FROM AUTHOR]

This review focuses on digital soil organic carbon (SOC) mapping at regional or national scales in spatial resolutions up to 1 km using open data remote sensing sources, emphasizing its importance in achieving United Nations' Sustainable Development Goals (SDGs) related to hunger, climate action, and land conservation. The literature review was performed according to scientific studies indexed in the Web of Science Core Collection database since 2000. The analysis reveals a steady rise in total digital soil mapping studies since 2000, with digital SOC mapping studies accounting for over 20% of these studies in 2023, among which SDGs 2 (Zero Hunger) and 13 (Climate Action) were the most represented. Notably, countries like the United States, China, Germany, and Iran lead in digital SOC mapping research. The shift towards machine and deep learning methods in digital SOC mapping has surged post-2010, necessitating environmental covariates like topography, climate, and spectral data, which are cornerstones of machine and deep learning prediction methods. It was noted that the available climate data primarily restrict the spatial resolution of digital SOC mapping to 1 km, which typically requires downscaling to harmonize with topography (up to 30 m) and multispectral data (up to 10–30 m). Future directions include the integration of diverse remote sensing data sources, the development of advanced algorithms leveraging machine learning, and the utilization of high-resolution remote sensing for more precise SOC mapping. [ABSTRACT FROM AUTHOR]

We present a nonadiabatic classical-trajectory approach that offers the best of both worlds between fewest-switches surface hopping (FSSH) and quasiclassical mapping dynamics. This mapping approach to surface hopping (MASH) propagates the nuclei on the active adiabatic potential-energy surface, such as in FSSH. However, unlike in FSSH, transitions between active surfaces are deterministic and occur when the electronic mapping variables evolve between specified regions of the electronic phase space. This guarantees internal consistency between the active surface and the electronic degrees of freedom throughout the dynamics. MASH is rigorously derivable from exact quantum mechanics as a limit of the quantum–classical Liouville equation (QCLE), leading to a unique prescription for momentum rescaling and frustrated hops. Hence, a quantum-jump procedure can, in principle, be used to systematically converge the accuracy of the results to that of the QCLE. This jump procedure also provides a rigorous framework for deriving approximate decoherence corrections similar to those proposed for FSSH. We apply MASH to simulate the nonadiabatic dynamics in various model systems and show that it consistently produces more accurate results than FSSH at a comparable computational cost. [ABSTRACT FROM AUTHOR]

Quantum simulation of chemical Hamiltonians enables the efficient calculation of chemical properties. Mapping is one of the essential steps in simulating fermionic systems on quantum computers. In this work, a unified framework of transformations mapping fermionic systems to qubit systems is presented and many existing transformations—such as Jordan–Wigner, Bravyi–Kitaev, and parity transformations—are included in this framework. Based on this framework, the Multilayer Segmented Parity (MSP) transformation is proposed. The MSP transformation is a general mapping with an adjustable parameter vector, which can be viewed as a generalization of the above-mentioned mappings. Furthermore, the MSP transformation can adjust flexibly when dealing with different systems. Applying these mappings to the electronic structure Hamiltonians of various molecules, the MSP transformation is found to perform better on a number of Pauli operators and gates needed in the circuit of Hamiltonian simulation. The MSP transformation will reduce the qubit gate requirement for Hamiltonian simulation on noisy intermediate-scale quantum devices, and it will provide a much wider choice of mappings for researchers. [ABSTRACT FROM AUTHOR]

High-resolution and high-accuracy mapping products of the Lunar South Pole (LSP) will play a vital role in future lunar exploration missions. Existing lunar global mapping products cannot meet the needs of engineering tasks, such as landing site selection and rover trajectory planning, at the LSP. The Lunar Reconnaissance Orbiter (LRO)'s narrow-angle camera (NAC) can acquire submeter images and has returned a large amount of data covering the LSP. In this study, we combine stereo-photogrammetry and photoclinometry to generate high-resolution digital orthophoto maps (DOMs) and digital elevation models (DEMs) using LRO NAC images for a candidate landing site at the LSP. The special illumination and landscape characteristics of the LSP make the derivation of high-accuracy mapping products from orbiter images extremely difficult. We proposed an easy-to-implement shadow recognition and contrast stretching method based on the histograms of the LRO NAC images, which is beneficial for photogrammetric and photoclinometry processing. In order to automatically generate tie points, we designed an image matching method considering LRO NAC images' features of long strips and large data volumes. The terrain and smoothness constraints were introduced into the cost function of photoclinometry adjustment, excluding pixels in shadow areas. We used 61 LRO NAC images to generate mapping products covering an area of 400 km2. The spatial resolution of the generated DOMs was 1 m/pixel, and the grid spacing of the derived DEMs was 1 m (close to the spatial resolution of the original images). The generated DOMs achieved a relative accuracy of better than 1 pixel. The geometric accuracy of the DEM derived from photoclinometry was consistent with the lunar orbiter laser altimeter (LOLA) DEM with a root mean square error of 0.97 m and an average error of 0.17 m. [ABSTRACT FROM AUTHOR]

Digital audio–tactile maps (DATMs) on touchscreen devices provide valuable opportunities for people who are visually impaired (PVIs) to explore the spatial environment for engaging in travel activities. Existing solutions for DATMs usually require extensive training for the PVIs to understand the feedback mechanism. Due to the shortage of human resources for training specialists, as well as PVIs' desire for frequent practice to maintain their usage skills, it has become challenging to widely adopt DATMs in real life. This paper discusses the use of large language models (LLMs) to provide a verbal evaluation of the PVIs' perception, which is crucial for the independent practice of DATM usage. A smartphone-based prototype providing DATMs of simple floor plans was developed for a preliminary investigation. The evaluation results have proven that the interaction with the LLM could help the participants better understand the DATMs' content and could vividly replicate them by drawings. [ABSTRACT FROM AUTHOR]

AbstractMaps are spatial representations of environments and are thus inherently geographical. As such, attaining map skills necessitates fostering students’ cartographic- and language-related abilities when using maps, which represent a characteristic role in geography education. However, to our knowledge, there is no empirical research on the cartographic- and language-related abilities required when using maps in geography education classrooms. To gain more profound insights into the linkages between cartographic concept knowledge and language skills regarding maps, a qualitative study among students is conducted. Upper secondary students drew a sketch map using a digital pen of a familiar route in the school area, while simultaneously being recorded thinking aloud. Students’ representations of the cartographic concept in their sketch maps and their cartography-specific/-unspecific language use in think-aloud protocols were analyzed qualitatively. Despite the high heterogeneity within the sample, results show linkages between the cartographic concept representation and cartography-specific language use among the participants. In addition, students who use cartography-specific language at a medium or high level paraphrased less and represented cartographic concepts more appropriately. [ABSTRACT FROM AUTHOR]

The United Nations called on the international community to move forward together. Through the Sustainable Development Goals (SDG), a plan was set until 2030. The use of maps, mapping technology and the skills of cartographers help synthesize and visualize complex data. Maps allow insights into trends and comparisons between different areas and over different time periods. Mapping helps in better understanding and implications of each sustainable development goal. For this purpose, the paper presents map projections with a special emphasis on mapping for a sustainable world. Map projection is not only the transformation of geospatial data, but of any data related to space. Presented is a modern classification of map projections that differs from the usual division into cylindrical, conical and azimuthal. For maps of the world adapted to the goals of sustainable development, the Winkel Triple projection is proposed. Different map projections bring with them significantly different distortions. The energy consumption for displaying a digital map varies greatly when using different projections. This is the area of a new branch of cartography - green cartography. In the article, we take a critical look at the research of digital maps and their carbon footprint and warn against maps made in map projections that should not be researched in the future. The article is intended for everyone who is interested in cartography as an auxiliary profession in sustainable development, but also for cartographers who do not think critically enough about map projections. [ABSTRACT FROM AUTHOR]

European Union (EU) diplomatic representation in third countries is performed by both the Member States and by the EU Delegation. This hybrid system of representation functions through EU coordination. As social media have become important channels of state representation, coordination also takes place in the domain of digital diplomacy. This article analyzes how the EU Member State embassies and the EU Delegation coordinate EU representation through online and offline interactions. It investigates the practices of coordination and maps routines of digital sociability. The United States' capital Washington, DC provides a context of both strong bilateral relations and a history of shared EU interests. The study draws on observations on Twitter (later renamed X) between 2019 and 2021 and reflections from diplomats who engage in the coordination of EU representation, collected through an online survey and interviews. By examining the reciprocity between online and offline interactions, the study illuminates how relationships are cultivated, a sense of collective belonging is fostered, and social order is negotiated. The findings enhance our understanding of how digital diplomacy is deeply embedded within diplomatic contexts and their distinctive practices. They contribute to advancing knowledge about the interplay of digital diplomacy, multilateral representation, and the dynamics that shape diplomatic engagements. [ABSTRACT FROM AUTHOR]

Five-axis contouring errors left on the parts contributed by feed drive servos, and intra- and inter-axis geometric errors are predicted along the five-axis tool paths. Quasi-static geometric errors are measured and modeled in the machine's workspace. Servo tracking errors of the three translational and two rotary drives are measured during machining or predicted from the CNC model. All errors are projected to the tooltip using a generalized screw theory-based forward kinematic model. The proposed model is validated experimentally on a CNC machining center for two test parts. • Developed a predictive model combining volumetric and servo-induced errors in 5-axis machining. • Utilized screw theory for adaptable modeling across different five-axis machine configurations. • Measured 41 geometric error parameters with laser interferometer and ballbar. • Included error parameter decoupling and decontamination procedures. • Achieved an 87 % accuracy in predicting dimensional errors on machined parts. [ABSTRACT FROM AUTHOR]

Aggregation, as an operation of cartographic generalization, provides an effective means of abstracting the configuration of building features by combining them according to the scale reduction of the 2D map. Automating this design process effectively helps professional cartographers design both paper and digital maps, but finding the best aggregation result from the numerous combinations of building features has been a challenge. This paper presents a novel approach to assist cartographers in interactively designing the aggregation of building features in scale‐aware map visualization. Our contribution is to provide an appropriate set of candidates for the cartographer to choose from among a limited number of possible combinations of building features. This is achieved by collecting locally optimal solutions that emerge in the course of aggregation operations, formulated as a label cost optimization problem. Users can also explore better aggregation results by interactively adjusting the design parameters to update the set of possible combinations, along with an operator to force the combination of manually selected building features. Each cluster of aggregated building features is tightly enclosed by a concave hull, which is later adaptively simplified to abstract its boundary shapes. Experimental design examples and evaluations by expert cartographers demonstrate the feasibility of the proposed approach to interactive aggregation. [ABSTRACT FROM AUTHOR]

Chang'e-6 (CE-6) is the first sample-return mission from the lunar farside and will be launched in May of 2024. The landing area is in the south of the Apollo basin inside the South Pole Aitken basin. Statistics and analyses of impact craters in the landing area are essential to support safe landing and geologic studies. In particular, the crater size–frequency distribution information of the landing area is critical to understanding the provenance of the CE-6 lunar samples to be returned and can be used to verify and refine the lunar chronology model by combining with the radioisotope ages of the relevant samples. In this research, a digital orthophoto map (DOM) mosaic with resolution of 3 m/pixel of the CE-6 landing area was generated from the 743 Narrow Angle Camera of the Lunar Reconnaissance Orbiter Camera. Based on the DOM, craters were extracted by an automated method and checked manually. A total of 770,731 craters were extracted in the whole area of 246 km × 135 km, 511,484 craters of which were within the mare area. Systematic analyses of the crater distribution, completeness, spatial density, and depth-to-diameter ratio were conducted. Geologic model age estimation was carried out in the mare area that was divided into three geologic units according to the TiO2 abundance. The result showed that the east part of the mare had the oldest model age of μ 3.27 − 0.045 + 0.036 Ga, and the middle part of the mare had the youngest model age of μ 2.49 − 0.073 + 0.072 Ga. The crater catalogue and the surface model age analysis results were used to support topographic and geologic analyses of the pre-selected landing area of the CE-6 mission before the launch and will contribute to further scientific researches after the lunar samples are returned to Earth. [ABSTRACT FROM AUTHOR]

Traditionally, it has been considered that the Ptolemaic or classical maps from the four editions of Ptolemy's Geography published in Rome (1478, 1490, 1507, and 1508) are apparently indistinguishable at first glance because they have been printed from the same plates. This poses a problem for antiquarians, collectors, and curators who wish to accurately date their copies. Recently, two very comprehensive articles have been published on the different paper watermarks associated with each edition, which would allow for the correct identification of each one. However, there are occasions when the maps do not display watermarks. This article aims to provide some keys to distinguish between the incunabula editions (1478, 1490) and those of 1507–1508 in cases of the absence of watermarks. In this process of detecting differences, we have used digitized images of the maps. The results show small modifications in the copper plates made between the editions of 1490 and 1507/08. Our findings reveal that seven previously unknown reworked maps have been identified. [ABSTRACT FROM AUTHOR]

Large-scale digital soil maps are essential for rational and sustainable land management as well as accurate fertilizer application. This study focuses on digital mapping of soil properties, namely soil organic carbon (SOC), pH, nitrogen (N), phosphorus (P), and potassium (K) in Chernozem topsoil and subsoil. The study was conducted on two arable fields in the Cis-Ural forest-steppe zone of the Republic of Bashkortostan (Russia). The random forest algorithm in combination with terrain attributes and Sentinel-2A satellite data was applied for spatial prediction of soil properties. The root-mean-square error (RMSE) and coefficient of determination (R2) were used to determine the model performance. According to the Pearson correlation, a significant positive relationship between SOC and N content was found at all sites and depths (R = 0.76–0.92). A cross-validation revealed that SOC (R2 = 0.22–0.62, RMSE = 0.35–0.89%) and N (R2 = 0.16–0.60, RMSE = 21.11–36.6 mg kg−1) were best predicted among other properties at all depths using remote sensing data, whereas the performance of predictive models decreased with depth. However, a relationship between the content of some soil properties and their spatial distribution at study depths was observed, which can be used as an additional explanatory variable. We suppose that digital mapping of soil properties at the arable field scale should not be limited to topographic and remote sensing variables. Based on this information, the use of auxiliary variables, such as collocated soil information in combination with relief and remote sensing data can be effective in more accurately estimating the spatial distribution of properties across arable fields at different depths. Overall, this study provides valuable insights into spatial modelling of the vertical distribution of soil properties, highlighting the significance of remote sensing data at the arable field scale. The findings can be valuable for land managers, agronomists, and policymakers seeking sustainable land management practices and efficient fertilizer application, as well as for developing further mapping procedures for arable fields. [ABSTRACT FROM AUTHOR]

Based on the analysis of the problems in material distribution routes, we propose the idea of integrating the intelligent connected vehicle system with material distribution, and construct an intelligent connected vehicle material distribution route model based on k-center spatial cellular clustering and an improved cockroach optimization algorithm. Firstly, we set the research scope to include the distribution center, the distribution points and the geographical environment. A cellular spatial model of distribution points is constructed to quantify and visualize the neighborhood relationship between the distribution centers and distribution points. On this basis, we construct an intelligent connected vehicle material distribution route model based on the improved cockroach optimization algorithm, and the optimal material distribution center is determined by searching for the corresponding optimal distribution route of each distribution center. In the experiment, we use the concept of symmetry to design routes that start from the initial points. The route passes through the distribution point, and finally reaches the destination. In this mode, the experiment generates symmetrically round-trip routes and generates different distribution time schedules. Case studies and comparative experiments show that the proposed algorithm has a total distance cost 1.2 km lower than the distance cost generated by the Baidu Map method and 2.7 km lower than the distance cost generated by the 360 Map method. In terms of the total time cost of the proposed algorithm, it is 0.06 h lower than the time cost generated by the Baidu Map method and 0.135 h lower than the time cost generated by the 360 Map method. Compared with the commonly used Dijkstra algorithm and the A* algorithm for route optimization, our proposed algorithm also generates a lower cost than the two other types of optimization algorithms. In the case study, the distance generated by the proposed algorithm is 1.8 km lower than that of the Dijkstra algorithm, and the total time cost is 0.09 h lower than that of the Dijkstra algorithm. The distance generated by the proposed algorithm is 1.6 km lower than that of the A* algorithm, and the total time cost is 0.08 h lower than that of the A* algorithm. Meanwhile, the proposed algorithm has a lower time complexity than the two commonly used optimization algorithms. Therefore, our proposed algorithm can find the distribution route with the lowest transportation cost. Compared to the commonly used electronic maps and the optimization algorithms for distribution route planning, our proposed algorithm can output distribution routes with lower costs under the same distribution sequence, and reduce the transportation costs for intelligent connected vehicle material distribution systems to the maximum extent. [ABSTRACT FROM AUTHOR]

First-order basin morphology is important for the functioning of the whole hydrologic system. This study examines the geomorphology of three areas in the tectonically active Siwalik Hills, Nepal, using morphometric variables. Twenty variables for 11,200 first-order basins were obtained from maps and Digital Elevation Models (DEMs) of three areas (the Machheli, Banganga and Binai river basins). Principal component analysis of each area condensed these into 6 major components, which accounted for 78–82% of the total variance in each data set. Rotated principal components were remarkably consistent between the three areas. The average slope, area, elevation range, valley angle, shape index, hypsometric integral, basin asymmetry factor and drainage density were identified as the eight most important morphometric variables defining these components. Ordinal grouping and k-means clustering led to recognition of eight basin types, distinguished predominantly by elevation range, average slope, area and valley angle. Shallow and steep basins are predominant in all three areas. The spatial distribution of first-order basin types exhibits a distinct pattern of geomorphic regions. The study demonstrated that a first-order basin is a suitable unit to demonstrate the morphological characteristics of a geologically complex area, and the study approach can be applied more broadly. [ABSTRACT FROM AUTHOR]

Background. Violation of the principles of direct and negative feedback regulation of endocrine functions leads to nodular goiter. The structure and quantitative changes of thyroid gland can be assessed more informatively and accurately by polarization biomedical optics. The purpose was to substantiate the possibility of using polarization biomedical optic methods in the diagnosis of nodular thyroid goiter based on the application of an integrative pathophysiological approach of the principle of direct and negative feedback regulation of endocrine functions. Materials and methods. Two groups of patients were studied: control group 1 — healthy donors (n = 51), study group 2 — patients with nodular goiter (n = 51), who underwent a thyroid fine needle biopsy for diagnostic purposes. The fundamental idea of polarization biomedical optics is based on two analytical approaches: statistical and topographic (fractal). Instrumental laser methods were used such as polarization, interference, digital holographic, as well as computer and analytical methods: statistical and multifractal. The statistical parameters of polarization azimuth maps, polarization azimuth of phase and multifractal spectra of digital microscopic images of native histological sections of thyroid biopsy in patients with nodular goiter were determined quantitatively with calculation of the mean, dispersion, asymmetry and kurtosis. The significance of differences compared to the controls taken as 100 % was evaluated using the Student’s parametric test (p < 0.05). Results. There was a significant increase in the asymmetry and kurtosis of the polarization azimuth, a significant inhibition of the mean of polarization azimuth of phase digital microscopic images of native histological sections of thyroid biopsy in patients with nodular goiter, a significant increase in the asymmetry and kurtosis of digital microscopic images of native histological sections of thyroid biopsy in patients with nodular goiter. A significant increase in dispersion and a probable decrease in the asymmetry and kurtosis of multifractal spectra of polarization azimuth maps of digital microscopic images of native histological sections of thyroid biopsy in patients with nodular goiter were shown. Conclusions. There was a significant increase in the biophysical optical parameters of digital microscopic images of native histological sections of thyroid biopsy in patients with nodular goiter due to an increase in colloidal follicles and the growth of connective tissue around them. A probable inhibition of the average azimuth of polarization of phase digital microscopic images of native histological sections and a significant decrease in the asymmetry and kurtosis of multifractal spectra of polarization azimuth maps of digital microscopic images of native histological sections of thyroid biopsy in patients with nodular goiter are caused by the activation of thyrocytes as an amorphous component as a result of activation of the dysregulatory pathological process due to violations of the principle of direct and negative feedback regulation of endocrine functions. [ABSTRACT FROM AUTHOR]

The growing adoption of digital twins in geomatics sectors requires efficient 3D mapping techniques. However, the complexity and cost of producing cartographically enriched 3D scenes pose significant challenges, hindering widespread application, particularly in domains with limited mapping expertise and budgets. The proposed methodology in this paper leverages recent advances in natural language processing and artificial intelligence, particularly large language models (LLMs), to reduce the expertise required for 3D mapping and to address the high costs and complexity associated with traditional cartographic processes. It introduces a natural language interface for 3D symbology, aimed at simplifying the design and automating the creation of cartographically enriched 3D scene. By allowing cartographers converse with the mapping system, the system translates verbal descriptions into structured symbology rules in 3D digital cartographic model, which are then used to generate cartographically enriched 3D scenes. The method chains multiple ad-hoc LLM-based agents for entity linking, conversation handling, and symbology rule verification. Prompt engineering methods, such as chain-of-though and retrieval augmented generation, have been used to guide the agents' reasoning process or leverage knowledge base, respectively. Experiment and application in utility networks demonstrates the method's capability to accurately interpret and execute 3D symbology rules from natural language inputs, resulting in cartographically enriched 3D scenes that are reproducible and scalable. This work represents a pioneer study to implement a natural language interface for 3D mapping. It not only enhances the usability and accessibility of 3D mapping in digital twins but also sets a foundational method for future research in natural language-based mapping interfaces. [ABSTRACT FROM AUTHOR]

This study focuses on the streets and spatial networks of the historic urban area in Changsha, the capital of Hunan Province, China, by mainly investigating the evolution of their geometric and topological characteristics. It draws on the theories and methods of urban morphology and space syntax, combines the digital historical maps at different times, and uses methods such as historical map spatial interpretation, geographic information system (GIS), sDNA tools, and urban morphological parameter analysis to explore and sort out the evolutionary process of the street and alley network in Changsha from the early modern period to the present. The paper constructs a parameter system for analyzing the street and alley network of historic urban areas from a geometric and topological perspective. It introduces the indicators of road density, road orientation, intersection density, and approaches such as closeness, betweenness, and intelligibility in space syntax into the parameter analysis framework of street and alley network morphology and spatial characteristics evolution. By comparing the changes in various parameters at different spatial scales, the process of the spatial order evolution of the street and alley network in the historic urban area is analyzed, and the evolutionary rules of the various periods' morphological characteristics are extracted. [ABSTRACT FROM AUTHOR]

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