General matrix multiplication (GEMM) on spatial accelerators is highly sensitive to mapping choices in both execution efficiency and energy consumption. However, the mapping space exhibits combinatorial explosion, which makes it extremely challenging to obtain optimal mappings within an acceptable time budget. Existing approaches typically face challenges: They often lack global-optimality guarantees and become prohibitively slow as the mapping space grows. To address these limitations, we propose \textsc{GOMA}, a geometric-abstraction-based, globally optimal GEMM mapping framework via analytical modeling, which achieves efficient solving while guaranteeing optimality. \textsc{GOMA} introduces, from first principles, a geometric abstraction for GEMM mapping, yielding an exact analytical energy objective with $O(1)$ evaluation for any given mapping. The objective is highly accurate. \textsc{GOMA} then formulates mapping selection as an integer optimization problem under hardware and mapping constraints, using the analytical energy model as the objective to automate mapping search. \textsc{GOMA} can quickly compute a global-optimal mapping for any (GEMM workload, target hardware) pair, achieving this for the first time in mapping space exploration. Experiments confirm that across representative accelerators and large language model prefill workloads, \textsc{GOMA} improves the energy--delay product (EDP) by $2.24$--$4.24\times$ over SOTA mappers, while accelerating time-to-solution by $3.83$--$73.6\times$.
In this paper a class of mappings on unit interval is constructed. These mapping preserve uniform distribution and theirs iterations form a sequence which is Buck uniformly distributed. In the third part some prorties of these mappings are proven.
Over the years, the date of the aerial photogrammetric survey has become increasingly necessary, always for professionals (mandatory indication in the title block of each cartographic survey), and recently for ordinary citizens when it is necessary to ascertain the legitimate status of a property (information required for example since Law 28 February 1985, n. 47 - Regulations on the control of urban-building activity).
Dominika Krausková, Tomáš Mikita, Petr Hrůza
et al.
Accurate mapping of small water structures and streambeds is essential for hydrological modeling, erosion control, and landscape management. While traditional geodetic methods such as GNSS and total stations provide high precision, they are time-consuming and require specialized equipment. Recent advances in mobile technology, particularly smartphones equipped with LiDAR sensors, offer a potential alternative for rapid and cost-effective field data collection. This study assesses the accuracy of the iPhone 14 Pro’s built-in LiDAR sensor for mapping streambeds and retention structures in challenging terrain. The test site was the Dílský stream in the Oslavany cadastral area, characterized by steep slopes, rocky surfaces, and dense vegetation. The stream channel and water structures were first surveyed using GNSS and a total station and subsequently re-measured with the iPhone. Several scanning workflows were tested to evaluate field applicability. Results show that the iPhone LiDAR sensor can capture landscape features with useful accuracy when supported by reference points spaced every 20 m, achieving a vertical RMSE of 0.16 m. Retention structures were mapped with an average positional error of 7%, with deviations of up to 0.20 m in complex or vegetated areas. The findings highlight the potential of smartphone LiDAR for rapid, small-scale mapping, while acknowledging its limitations in rugged environments.
Rahmat Fauzi Ramadhan, Nur Hidayani Alimuddin, Irsan Rahman
Land disputes and the structural deficiency of Indonesia's land administration system have persisted as fundamental obstacles to the realization of agrarian justice. Digital certificate mapping (plotting), mandated by Ministry of ATR/BPN Circular Number 13/SE/XII/2017, represents a pivotal policy instrument designed to establish spatial legal certainty over registered land rights and to accelerate the national agrarian reform programme. This research examines the implementation of plotting at the Kolaka Regency Land Office, Sulawesi Tenggara, and critically analyses its impact on the acceleration of agrarian reform through a socio-legal approach integrating statutory analysis with primary field data. The findings reveal that plotting, when correctly implemented, contributes substantially to spatial legal certainty by detecting overlapping certificates and producing georeferenced digital parcel records through the SIPETIK system. However, empirical evidence from documented cases including a certificate location mismatch affecting Ny. Harmiani (SHM No. 00614) and an unauthorized plotting case in Okoko Village exposes systemic deficits in technical capacity, institutional accountability, and community engagement that undermine the system's effectiveness for the most vulnerable rights holders. This research concludes that the transformative potential of digital plotting for agrarian reform is contingent upon coordinated investment in licensed cadastral surveyor capacity, institutional transparency mechanisms, and systematic community socialization conditions that remain structurally underdeveloped in the Eastern Indonesian land administration context.
Optimum planning and effective land consolidation, widely discussed by contemporary authors, is a response to the perceivable need to modernise global agriculture to ensure the community’s food security and create steady, sustainable development in rural areas. Adequate leveraging of agricultural policy instruments requires setting a correct strategic direction, including allocating available funds and considering the technical feasibility of the adopted assumptions. The selection of relevant methods to ensure the efficient and complete accomplishment of the anticipated results should follow a rational analysis of the actual work complexity. This paper presents an innovative, proprietary method for evaluating the difficulty of potential land consolidation using a standardised cadastral data set. The designed tool, which relies on automated algorithms applied in a GIS environment, provides accurate data describing the expected land consolidation complexity at individual stages of the procedure. Detailed and current information on land ownership, use, and farm geometry processed using efficient spatial and statistical analysis methods provides transparent and unambiguous results. The proposed solution was used in developing the difficulty assessment of land consolidation in 58 villages of the Strzyżów district in southeastern Poland.
Accurate and dense mapping in large-scale environments is essential for various robot applications. Recently, implicit neural signed distance fields (SDFs) have shown promising advances in this task. However, most existing approaches employ projective distances from range data as SDF supervision, introducing approximation errors and thus degrading the mapping quality. To address this problem, we introduce N$^{3}$-Mapping, an implicit neural mapping system featuring normal-guided neural non-projective signed distance fields. Specifically, we directly sample points along the surface normal, instead of the ray, to obtain more accurate non-projective distance values from range data. Then these distance values are used as supervision to train the implicit map. For large-scale mapping, we apply a voxel-oriented sliding window mechanism to alleviate the forgetting issue with a bounded memory footprint. Besides, considering the uneven distribution of measured point clouds, a hierarchical sampling strategy is designed to improve training efficiency. Experiments demonstrate that our method effectively mitigates SDF approximation errors and achieves state-of-the-art mapping quality compared to existing approaches.
In the article a technique of the usage of $f$-continuous functions (on mappings) and their families is developed. A proof of the Urysohn's Lemma for mappings is presented and a variant of the Brouwer-Tietze-Urysohn Extension Theorem for mappings is proven. Characterizations of the normality properties of mappings are given and the notion of a perfect normality of a mapping is introduced. It seems to be the most optimal in this approach.
Disaster mapping is a critical task that often requires on-site experts and is time-consuming. To address this, a comprehensive framework is presented for fast and accurate recognition of disasters using machine learning, termed DisasterNets. It consists of two stages, space granulation and attribute granulation. The space granulation stage leverages supervised/semi-supervised learning, unsupervised change detection, and domain adaptation with/without source data techniques to handle different disaster mapping scenarios. Furthermore, the disaster database with the corresponding geographic information field properties is built by using the attribute granulation stage. The framework is applied to earthquake-triggered landslide mapping and large-scale flood mapping. The results demonstrate a competitive performance for high-precision, high-efficiency, and cross-scene recognition of disasters. To bridge the gap between disaster mapping and machine learning communities, we will provide an openly accessible tool based on DisasterNets. The framework and tool will be available at https://github.com/HydroPML/DisasterNets.
Hoang-Anh Phan, Phuc Vinh Nguyen, Thu Hang Thi Khuat
et al.
In recent years, 3D mapping for indoor environments has undergone considerable research and improvement because of its effective applications in various fields, including robotics, autonomous navigation, and virtual reality. Building an accurate 3D map for indoor environment is challenging due to the complex nature of the indoor space, the problem of real-time embedding and positioning errors of the robot system. This study proposes a method to improve the accuracy, speed, and quality of 3D indoor mapping by fusing data from the Inertial Measurement System (IMU) of the Intel Realsense D435i camera, the Ultrasonic-based Indoor Positioning System (IPS), and the encoder of the robot's wheel using the extended Kalman filter (EKF) algorithm. The merged data is processed using a Real-time Image Based Mapping algorithm (RTAB-Map), with the processing frequency updated in synch with the position frequency of the IPS device. The results suggest that fusing IMU and IPS data significantly improves the accuracy, mapping time, and quality of 3D maps. Our study highlights the proposed method's potential to improve indoor mapping in various fields, indicating that the fusion of multiple data sources can be a valuable tool in creating high-quality 3D indoor maps.
This paper presents the problem of cadastral maps. The cadastre that existed until now, consisting of paper maps and land books, is now becoming insufficient. Its shortcomings cause developments leading to its improvement. One way is to create a Land Information System.
“Dare vita al Metaverso richiederà uno sforzo congiunto tra imprese, politica e società civile", ha spiegato durante l'incontro
"un portavoce di Meta, la società madre di Facebook, Instagram e WhatsApp. Abbiamo lavorato con il governo italiano per
rafforzare i punti di forza del paese nei settori della tecnologia e del design e identificare gli investimenti futuri. Siamo lieti che
abbia discusso delle opportunità culturali, sociali ed economiche che il metaverso porterà all’Italia e non vediamo l'ora di
continuare questa collaborazione. "
S. Buján, J. Guerra-Hernández, Eduardo González-Ferreiro
et al.
Knowledge about forest road networks is essential for sustainable forest management and fire management. The aim of this study was to assess the accuracy of a new hierarchical-hybrid classification tool (HyClass) for mapping paved and unpaved forest roads with LiDAR data. Bare-earth and low-lying vegetation were also identified. For this purpose, a rural landscape (area 70 ha) in northwestern Spain was selected for study, and a road network map was extracted from the cadastral maps as the ground truth data. The HyClass tool is based on a decision tree which integrates segmentation processes at local scale with decision rules. The proposed approach yielded an overall accuracy (OA) of 96.5%, with a confidence interval (CI) of 94.0–97.6%, representing an improvement over pixel-based classification (OA = 87.0%, CI = 83.7–89.8%) using Random Forest (RF). In addition, with the HyClass tool, the classification precision varied significantly after reducing the original point density from 8.7 to 1 point/m2. The proposed method can provide accurate road mapping to support forest management as an alternative to pixel-based RF classification when the LiDAR point density is higher than 1 point/m2.
Yuki Katsumata, Akinori Kanechika, Akira Taniguchi
et al.
Using the spatial structure of various indoor environments as prior knowledge, the robot would construct the map more efficiently. Autonomous mobile robots generally apply simultaneous localization and mapping (SLAM) methods to understand the reachable area in newly visited environments. However, conventional mapping approaches are limited by only considering sensor observation and control signals to estimate the current environment map. This paper proposes a novel SLAM method, map completion network-based SLAM (MCN-SLAM), based on a probabilistic generative model incorporating deep neural networks for map completion. These map completion networks are primarily trained in the framework of generative adversarial networks (GANs) to extract the global structure of large amounts of existing map data. We show in experiments that the proposed method can estimate the environment map 1.3 times better than the previous SLAM methods in the situation of partial observation.
Jason J. Bramburger, Steven L. Brunton, J. Nathan Kutz
Despite many of the most common chaotic dynamical systems being continuous in time, it is through discrete time mappings that much of the understanding of chaos is formed. Henri Poincaré first made this connection by tracking consecutive iterations of the continuous flow with a lower-dimensional, transverse subspace. The mapping that iterates the dynamics through consecutive intersections of the flow with the subspace is now referred to as a Poincaré map, and it is the primary method available for interpreting and classifying chaotic dynamics. Unfortunately, in all but the simplest systems, an explicit form for such a mapping remains outstanding. This work proposes a method for obtaining explicit Poincaré mappings by using deep learning to construct an invertible coordinate transformation into a conjugate representation where the dynamics are governed by a relatively simple chaotic mapping. The invertible change of variable is based on an autoencoder, which allows for dimensionality reduction, and has the advantage of classifying chaotic systems using the equivalence relation of topological conjugacies. Indeed, the enforcement of topological conjugacies is the critical neural network regularization for learning the coordinate and dynamics pairing. We provide expository applications of the method to low-dimensional systems such as the Rössler and Lorenz systems, while also demonstrating the utility of the method on infinite-dimensional systems, such as the Kuramoto--Sivashinsky equation.
Abstract This paper proposes a simple and fast method for the identification of structural changes affecting buildings in urban environments by using a combination of Synthetic Aperture Radar (SAR) imagery and Geospatial Information System (GIS) processing. The identification of changes in urban settlements is of great interest for damage assessment after natural disasters, cadastral mapping and monitoring urban development or illegal activities, such as the construction of unauthorized buildings. Satellite remote sensing is useful in this scenario and SAR data is attractive due to its wide and ubiquitous coverage, the day and night all-weather availability, the exact repetition of the acquisition geometry, the repeated illumination and the sensitivity to slight changes in the geometrical structure of the targets in the scene. This sensibility is an advantage, but turns into a drawback especially in an urban environment where every subtle change may cause an unwanted detection. This environment is indeed one of the most challenging for the detection of those changes that are of any real interest since these events are masked by thousands of irrelevant detections. This paper tackle this problem with a combination of an improved, high-resolution coherent change detection technique called M-CCD and with a GIS post-processing. The result is a map of changes affecting buildings that are of a significant scale and consequently of a certain interest in an urban environment. In this contribution, the complete workflow is detailed and an assessment of the detected changes is done with high resolution optical images through visual photo-interpretation. A comparison with other SAR and optical change detection methods is also carried out.
The most common way for robots to handle environmental information is by using maps. At present, each kind of data is hosted on a separate map, which complicates planning because a robot attempting to perform a task needs to access and process information from many different maps. Also, most often correlation among the information contained in maps obtained from different sources is not evaluated or exploited. In this paper, we argue that in robotics a shift from single-source maps to a multi-layer mapping formalism has the potential to revolutionize the way robots interact with knowledge about their environment. This observation stems from the raise in metric-semantic mapping research, but expands to include in its formulation also layers containing other information sources, e.g., people flow, room semantic, or environment topology. Such multi-layer maps, here named hypermaps, not only can ease processing spatial data information but they can bring added benefits arising from the interaction between maps. We imagine that a new research direction grounded in such multi-layer mapping formalism for robots can use artificial intelligence to process the information it stores to present to the robot task-specific information simplifying planning and bringing us one step closer to high-level reasoning in robots.
Let $Ω$ and $Ω'$ be open subsets of a flat $(2,3,5)$-distribution. We show that a $C^1$-smooth contact mapping $f : Ω\to Ω'$ is a $C^\infty$-smooth contact mapping. Ultimately, this is a consequence of the rigidity of the associated stratified Lie group (the Tanaka prolongation of the Lie algebra is of finite-type). The conclusion is reached through a careful study of some differential identities satisfied by components of the Pansu-derivative of a $C^1$-smooth contact mapping.
Emphasis is placed on the existing procedure for conducting a land inventory, which does not fully take into account the specific features of land use of certain sectors of land use. Emphasis is placed on the specialized activities of the National Academy of Agrarian Sciences of Ukraine (NAAS), which uses land and real estate in combination for scientific and research activities. It is emphasized that the land resources and property of institutions and enterprises of NAAS appear in two planes of available information from the State Land Cadastre (SCC) and the data from the State Register of Real Property Rights (DRRP). At the same time, the existing land inventory procedure is limited in terms of comparing the information of the DZK and DRRP data. Emphasis is placed on the need to compare the data of DZK and DRRP of the current state of land use of institutions and enterprises of NAAS through analysis as a method of scientific knowledge. The main approaches, requirements to the structural elements of the analysis in scientific, methodological and practical areas are proposed. The structure of interaction of constituent elements at the analysis of a modern condition of use of the earths of establishments, the enterprises of NAAS is resulted.
This work presents a modular architecture for simultaneous mapping and target driven navigation in indoors environments. The semantic and appearance stored in 2.5D map is distilled from RGB images, semantic segmentation and outputs of object detectors by convolutional neural networks. Given this representation, the mapping module learns to localize the agent and register consecutive observations in the map. The navigation task is then formulated as a problem of learning a policy for reaching semantic targets using current observations and the up-to-date map. We demonstrate that the use of semantic information improves localization accuracy and the ability of storing spatial semantic map aids the target driven navigation policy. The two modules are evaluated separately and jointly on Active Vision Dataset and Matterport3D environments, demonstrating improved performance on both localization and navigation tasks.