Challenges of comparing and matching roads from different spatial datasets

Abstract. Updating road networks in rapidly changing urban landscapes is an important but difficult task, often challenged by the complexity and errors of manual mapping processes. Traditional methods that primarily use RGB satellite imagery struggle with obstacles in the environment and varying road structures, leading to limitations in global data processing. This paper presents an innovative approach that utilizes deep learning and multispectral satellite imagery to improve road network extraction and mapping. By exploring U-Net models with DenseNet backbones and integrating different spectral bands we apply semantic segmentation and extensive post-processing techniques to create georeferenced road networks. We trained two identical models to evaluate the impact of using images created from specially selected multispectral bands rather than conventional RGB images. Our experiments demonstrate the positive impact of using multispectral bands, by improving the results of the metrics Intersection over Union (IoU) by 6.5%, F1 by 5.4%, and the newly proposed relative graph edit distance (relGED) and topology metrics by 2.2% and 2.6% respectively.

Despite advancements in divergence and distance measures across fuzzy set extensions, the development of such measures for Globular T-Spherical Fuzzy Sets (G-TSFSs) remains significantly unexplored. Existing approaches often fall short in capturing the rich semantics and high-dimensional uncertainty that G-TSFSs represent, limiting their utility in complex decision environments. This study is motivated by the need to fill this critical gap and advance decision science through more expressive and structurally aligned tools. This paper introduces a suite of novel divergence measures (Div-Ms) specifically formulated for G-TSFSs, a powerful tool for capturing uncertainty in multi-criteria group decision-making (MCGDM) under complex conditions. These Div-Ms serve as the foundation for developing new distance measures (Dis-Ms) and similarity measures (SMs), where both Dis-Ms and SMs are symmetry-based and their essential mathematical properties and supporting theorems are rigorously established. Leveraging these constructs, we propose a robust G-TSF-TOPSIS framework and apply it to a real-world problem, selecting optimal solar energy systems (SESs) for a university context. The model integrates expert evaluations, assuming equal importance due to their pivotal and complementary roles. A sensitivity analysis over the tunable parameter (ranging from 4.0 to 5.0 with an increment of 0.2) confirms the robustness and stability of the decision outcomes, with no changes observed in the final rankings. Comparative analysis with existing models shows superiority and soundness of the proposed methods. These results underscore the practical significance and theoretical soundness of the proposed approach. The study concludes by acknowledging its limitations and suggesting directions for future research, particularly in exploring adaptive expert weighting strategies for broader applicability.

In the study of intuitionistic fuzzy clustering, the construction of an intuitionistic fuzzy similarity matrix (IFSM) is a fundamental and important issue in the direct clustering analysis, since it determines clustering results and computational efforts. Many methods based on the axioms of intuitionistic fuzzy similarity relations are applicable to IFSM construction. However, most of existing methods may yield a “counterintuitive result” in some cases and consume much computational time. In this paper, we propose a novel intuitionistic fuzzy clustering method to deal with such problems. First, based on the normalized Hamming distance, we define a similarity measure between intuitionistic fuzzy numbers (IFNs), by which a similarity measure between intuitionistic fuzzy sets (IFSs) is induced. Second, a divergence measure between IFSs is obtained by extending the dissimilarity of IFNs. Third, we construct an IFSM by using together the similarity and divergence measures so as to cluster the intuitionistic fuzzy information. Finally, two examples are presented to show the effectiveness and advantages of our method.

A hierarchical shape description approach and its application in similarity measurement of polygon entities

This paper proposes a novel framework called Distributed Compressed Video Sensing (DISCOS) - a solution for Distributed Video Coding (DVC) based on the recently emerging Compressed Sensing theory. The DISCOS framework compressively samples each video frame independently at the encoder. However, it recovers video frames jointly at the decoder by exploiting an interframe sparsity model and by performing sparse recovery with side information. In particular, along with global frame-based measurements, the DISCOS encoder also acquires local block-based measurements for block prediction at the decoder. Our interframe sparsity model mimics state-of-the-art video codecs: the sparsest representation of a block is a linear combination of a few temporal neighboring blocks that are in previously reconstructed frames or in nearby key frames. This model enables a block to be optimally predicted from its local measurements by l1-minimization. The DISCOS decoder also employs a sparse recovery with side information to jointly reconstruct a frame from its global measurements and its local block-based prediction. Simulation results show that the proposed framework outperforms the baseline compressed sensing-based scheme of intraframe-coding and intraframe-decoding by 8 – 10dB. Finally, unlike conventional DVC schemes, our DISCOS framework can perform most encoding operations in the analog domain with very low-complexity, making it be a promising candidate for real-time, practical applications where the analog to digital conversion is expensive, e.g., in Terahertz imaging.

Design Recipes for FPGAs

Chapter 6 - Applications of blockchain in smart cities: detecting fake documents from land records using blockchain technology

We study the rheology of a granular paste, i.e. a dense suspension of non-Brownian particles, quantitatively at steady state, in a cylindrical Couette cell. Previous studies have shown a discrepancy between local and global measurements of the viscosity for these materials, making it impossible to predict their resistance to flow. Using both MRI investigation techniques and classical rheology studies, we show that agreement between local and global measurements can be obtained, provided the migration of particles inside the gap is taken into account. As found by Leighton & Acrivos (J. Fluid Mech. vol. 181, 1987, p. 415), the migration leads to a particle density gradient in the flow, the highly sheared regions being less dense in particles. Here, by comparing the local viscosity and particle density measurements from MRI with the macroscopic relation between viscosity and the volume fraction, it is shown that global and local measurements agree with each other. This consequently allows us to define a viscosity for dense suspensions.

An alternative approach to measure similarity between two deterministic transient signals

BackgroundPubChem provides a 3-D neighboring relationship, which involves finding the maximal shape overlap between two static compound 3-D conformations, a computationally intensive step. It is highly desirable to avoid this overlap computation, especially if it can be determined with certainty that a conformer pair cannot meet the criteria to be a 3-D neighbor. As such, PubChem employs a series of pre-filters, based on the concept of volume, to remove approximately 65% of all conformer neighbor pairs prior to shape overlap optimization. Given that molecular volume, a somewhat vague concept, is rather effective, it leads one to wonder: can the existing PubChem 3-D neighboring relationship, which consists of billions of shape similar conformer pairs from tens of millions of unique small molecules, be used to identify additional shape descriptor relationships? Or, put more specifically, can one place an upper bound on shape similarity using other "fuzzy" shape-like concepts like length, width, and height?ResultsUsing a basis set of 4.18 billion 3-D neighbor pairs identified from single conformer per compound neighboring of 17.1 million molecules, shape descriptors were computed for all conformers. These steric shape descriptors included several forms of molecular volume and shape quadrupoles, which essentially embody the length, width, and height of a conformer. For a given 3-D neighbor conformer pair, the volume and each quadrupole component (Qx, Qy, and Qz) were binned and their frequency of occurrence was examined. Per molecular volume type, this effectively produced three different maps, one per quadrupole component (Qx, Qy, and Qz), of allowed values for the similarity metric, shape Tanimoto (ST) ≥ 0.8.The efficiency of these relationships (in terms of true positive, true negative, false positive and false negative) as a function of ST threshold was determined in a test run of 13.2 billion conformer pairs not previously considered by the 3-D neighbor set. At an ST ≥ 0.8, a filtering efficiency of 40.4% of true negatives was achieved with only 32 false negatives out of 24 million true positives, when applying the separate Qx, Qy, and Qz maps in a series (Qxyz). This efficiency increased linearly as a function of ST threshold in the range 0.8-0.99. The Qx filter was consistently the most efficient followed by Qy and then by Qz. Use of a monopole volume showed the best overall performance, followed by the self-overlap volume and then by the analytic volume.Application of the monopole-based Qxyz filter in a "real world" test of 3-D neighboring of 4,218 chemicals of biomedical interest against 26.1 million molecules in PubChem reduced the total CPU cost of neighboring by between 24-38% and, if used as the initial filter, removed from consideration 48.3% of all conformer pairs at almost negligible computational overhead.ConclusionBasic shape descriptors, such as those embodied by size, length, width, and height, can be highly effective in identifying shape incompatible compound conformer pairs. When performing a 3-D search using a shape similarity cut-off, computation can be avoided by identifying conformer pairs that cannot meet the result criteria. Applying this methodology as a filter for PubChem 3-D neighboring computation, an improvement of 31% was realized, increasing the average conformer pair throughput from 154,000 to 202,000 per second per CPU core.

Similarity–divergence intuitionistic fuzzy decision using particle swarm optimization

Smart Cities theme has evolved in the last years, leading the cities to implement initiatives related to technical aspects to improve quality of life. Another focus on this theme is how to measure the added value of these initiatives to the population. The Brazilian Network of Smart and Human Cities (RBCIH) was created in order to join both approaches in Brazil, putting together members from academy, private initiative and local (municipality) government. Nowadays, RBCIH is composed by 350 Brazilian cities (in a universe of 5570 cities), indicating that a long-term work has to be executed by RBCIH in Brazil. Two purposes of RBCIH are presenting good practices and evaluating how smart and human a city is. In this work, a methodology is presented for constructing a Brazilian reference model and an evaluation method for smart cities, adherent to the Brazilian reality.

Visakhapatnam can play an important role in improving energy efficiency in its buildings by implementing ECBC. This document seeks to capture stakeholder recommendations on a road map for implementation, which can help all market players plan for implementation. Visakhapatnam also has an opportunity to serve as a role model for other Smart Cities and cities in general in India. The road map and steps that VUDA adopts to implement ECBC can provide helpful examples to these other cities.

The notion of ‘smart cities’ has gained the attention of policymakers, urban developers and government authorities around the world and is emerging as a major response to urbanization, economic regeneration and other environmental challenges faced by cities globally. Smart cities depend not only on a city's endowment of hard infrastructure (physical capital), but also and increasingly so, on the availability and quality of knowledge communication and social infrastructure (human and social capital). This emerging role of cities as information hubs and knowledge repositories is particularly decisive for urban competitiveness. Arguably, many smart city projects die after the pilot stage and the lessons learned from previous projects never scale up to inform subsequent implementations. This is a major impediment in the future development of smart cities, particularly around facilitating successful technological and procedural replication. In line with recent calls for a cautious rethink of the very rationale and relevance of the smart cities debate pointing to new avenues of research into interdisciplinary aspects, this paper reviews the extant smart city literature in an attempt to identify current theoretical streams and provide further insight into the role of Knowledge Management in smart city development. It also explores how cities can realise the full benefits of tacit knowledge, learning and collaboration. A review of forty-eight peer-reviewed articles is conducted. The findings suggest the lack of Knowledge Management models for smart city replication and reveal how socio-technical approaches can help to support collaboration and knowledge sharing. Policy recommendations for local and national governments on how cities can benefit from a shift towards collaborative knowledge-making are also provided.

We study the task of localizing multipartite entanglement in pure quantum states onto a subsystem by measuring the remaining systems. To this end, we fix a multipartite entanglement measure and consider two quantities: the multipartite entanglement of assistance (MEA), defined as the entanglement measure averaged over the post-measurement states and maximized over arbitrary measurements; and the localizable multipartite entanglement (LME), defined in the same way but restricted to only local single-system measurements. Both quantities generalize previously considered bipartite entanglement localization measures. In our work we choose the n-tangle, the genuine multipartite entanglement concurrence and the concentratable entanglement (CE) as the underlying seed measure, and discuss the resulting MEA and LME quantities. First, we prove easily computable upper and lower bounds on MEA and LME and establish Lipschitz-continuity for the n-tangle and CE-based LME and MEA. Using these bounds we investigate the typical behavior of entanglement localization by deriving concentration inequalities for the MEA evaluated on Haar-random states and performing numerical studies for small tractable system sizes. We then turn our attention to protocols that transform graph states. We give a simple criterion based on a matrix equation to decided whether states with a specified n-tangle value can be obtained from a given graph state, providing no-go theorems for a broad class of such graph state transformations beyond the usual “local Clifford plus local Pauli measurement” framework. This analysis is generalized to weighted graph states, which provide a realistic error model in current experiments preparing graph state. Our entanglement localization framework certifies the near-optimality of recently discussed local-measurement protocols to transform uniformly weighted line graph states into GHZ states, even when considering arbitrary entangled measurements. Finally, we demonstrate how our MEA and LME quantities can be used to detect critical phenomena such as phase transitions in transversal field Ising models. Since entanglement localization is operationally relevant throughout quantum networking and measurement-based quantum computation, our framework of results based on the MEA and LME has the potential for broad applications in these fields.