Ivan Zvonkov, Gabriel Tseng, Inbal Becker-Reshef
et al.
Accurate and up-to-date land cover maps are essential for understanding land use change, a key driver of climate change. Geospatial embeddings offer a more efficient and accessible way to map landscape features, yet their use in real-world mapping applications remains underexplored. In this work, we evaluated the utility of geospatial embeddings for cropland mapping in Togo. We produced cropland maps using embeddings from Presto and AlphaEarth. Our findings show that geospatial embeddings can simplify workflows, achieve high-accuracy cropland classification and ultimately support better assessments of land use change and its climate impacts.
Georeferenced Access Points as a Strategic Node in the Evolution of Territorial Information Systems - This paper explores the strategic role of georeferenced access points and civic numbering as foundational components of advanced Territorial Information Systems (SIT) within Italian public administrations. The quality and consistency of georeferenced street and building numbers represent a fundamental component of territorial data infrastructures, enabling reliable integration between cadastral datasets, administrative services and emergency response systems.
Drawing from the experience of LdP Progetti GIS — involving more than 130 municipalities across five regions — the article demonstrates how the integration of Accesses, Buildings and Street Toponyms enables an interoperable Web-GIS ecosystem supporting digital services, data governance and operational decision-making. Real case studies from the municipalities of Siena, Arezzo, Empoli and Pistoia illustrate concrete applications such as emergency management, fiscal intelligence (TARI compliance), housing planning and economic activity monitoring. The results highlight significant improvements in administrative efficiency, transparency and open-data availability, positioning geospatial infrastructures as a key enabler of digital transformation in the Public Sector.
P. C. D. O. Campos, Diego Leonardo Rosa, Maria Esther Soares Marques
et al.
Monitoring natural slopes, embankments, and unstable slopes is crucial to reducing predisposition to mass movements, especially in areas with geotechnical instability and high rainfall. This study proposes a methodology to identify geotechnical and pluviometric triggers of mass movements in railway slopes. It involves registering slopes and embankments along the railroad, recording accumulated rainfall indices, and documenting associated accidents. The experimental program included a cadastral survey at a pilot site on the MRS company’s railway network in the Paraopeba branch, Minas Gerais, Brazil. Surface and subsurface drainage conditions, anthropic interventions, and modifications affecting slope stability were also examined. Additionally, the history of accidents involving geotechnical and regional rainfall indices were incorporated to identify potential triggering events for mass movements. The study found a good correlation between landslide records and geotechnical risk mapping but a low correlation between landslide records and rainfall isohyets. The latter result is attributed to the low density and poor distribution of rainfall data and active pluviometers in the region. Overall, understanding the geological–geotechnical characteristics of slopes and the correlation between accidents and rainfall indices provides valuable insights for predicting potential landslide occurrences.
The use of computer vision and deep learning in boundary documentation for land registration stems from the ongoing demand for appropriate mapping approaches of unregistered land rights to eradicate the global challenge of tenure insecurity. Previous research has yielded promising results towards automated extraction of photo‐visible cadastral boundaries from high‐resolution imagery. Nonetheless, the extraction of invisible cadastral boundaries is still a challenge. This study investigates the place of sensor/s on‐board unmanned aerial vehicles and deep learning algorithms in detecting cadastral boundaries. It develops a participatory boundary marking procedure using low‐cost markers to bring monument to previously invisible and ill‐defined cadastral boundaries. After that, the researchers trained and tested the accuracy of a convolutional neural network, namely single shot multi‐box detector (SSD) based on Residual Neural Network (ResNet) and Visual Geometry Group (VGG) backbone networks to automatically detect cadastral boundary markers from unmanned aerial vehicle imagery. SSD based on ResNet34 performed best with 0.88 precision, 0.92 recall and 0.91 F measure or (F1) score. VGG19‐based SSD yielded a precision of 0.47, recall of 0.53 and F1 score of 0.50. The horizontal accuracy of the cadastral map generated varied from 0.089 to 0.496 m per parcel, with a standard deviation of 0.120 m. Results show that this approach is practical for cadastral mapping in rural areas.
The comparison of cadastral systems worldwide proves challenging, given the profound cultural, linguistic, technical, and social disparities among countries. This complexity extends to the examination of data, encompassing issues related to land policy, laws, regulations, ownership, management, and technology within each nation's cadastral framework, forming the foundation for numerous studies. This article delves into a comparative analysis of the cadastral systems of Turkey and Iran, two nations sharing a common geography, similar cultures, and identical religious beliefs. The focus lies on content examination, exploring historical development, organizational structure, and the comprehensive status of cadastres across both countries. The study further scrutinizes the technologies employed and the mapping infrastructure integral to each nation's cadastral system. This research offers valuable insights into the similarities and differences between these two countries, shedding light on the intricate dynamics of cadastral systems within a shared cultural and geographical context.
Komi Arsène Fulbert Adjayi, Koffi Kpotchou, K. Nassi
et al.
In Togo and particularly in greater Lomé and Atakpamé, we see that the State has difficulty regulating or controlling land. The objective of this article is to examine the factors that make land regulation difficult in Togo in general and in greater Lomé and Atakpamé in particular. To achieve this, the research used quantitative and qualitative approaches based respectively on questionnaire administration and individual interview techniques. For the quantitative survey, 208 landowners were interviewed in greater Lomé compared to 122 in Atakpamé. Concerning the qualitative approach, 24 resource people were interviewed in greater Lomé compared to 21 in Atakpamé. The results of the investigations showed that the non-registration of plots purchased in the cadastral service, the absence of land mapping, unconventional subdivisions, land speculation and the purchase of family land constitute factors which make regulation and management difficult. land security. To compensate for illegal urban practices and sustainably control land, it is necessary, in addition to existing measures, to adopt an inclusive and participatory approach which would bring populations, local elected officials and the state into a chain of collaboration.
Dr. Roberto Capua holds a Master’s degree in Electronic Engineering and a Ph.D. in Positioning, Navigation and Wireless Location from the University of Calgary. He has more than 28 years of experience in the field of GNSS applications, Research, and Development for Public and Private Organizations. He has worked on relevant European Commission projects on Galileo design and application development. He was a Program Manager on satellite navigation applications for a European Satellite Service Provider. His areas of activity include advanced GNSS Augmentation Systems for High Accuracy and Integrity, GNSS Software Receivers, and GNSS surveying for cadastral and mapping applications. He has worked on the development of hardware and software navigation and communication technologies for Road, Inland waterway, Aerospace, Customs application, and Cadastral Surveying. Currently, he is the Head of the Innovation Unit and responsible for GNSS R&D activities for Sogei, the Technology partner of the Ministry of Economy and Finance of Italy. He is the Secretary of the Space Y Association and Chairman of the RTCM SC-134 Committee on “Integrity for GNSS-Based High Accuracy Applications”, as well as an RTCM Board of Directors Member. He is also President of the Satellite Navigation Commission at the Italian Order of Engineers – Rome. He has been the International Technical Representative for the ION Satellite Division (2023-2024). Dr. Sam Pullen is a senior researcher within the GNSS Laboratory at Stanford University, where he received his Ph.D. in Aeronautics and Astronautics in 1996. He has supported the FAA and other service providers in developing system concepts, technical requirements, integrity algorithms, and performance models for GBAS, SBAS, and other GNSS services and applications. He has also performed GNSS system design, application development, risk assessment, and legal support through his consultancy, Sam Pullen Consulting. He was awarded the ION Early Achievement Award in 1999 and became an ION Fellow in 2017. Dr. Mathieu Joerger is an assistant professor at Virginia Tech. He
For an infinite type surface $Σ$, we consider the space of (marked) convex hyperbolic structures on $Σ$, denoted $H(Σ)$, with the Fenchel-Nielsen topology. The (big) mapping class group acts faithfully on this space allowing us to investigate a number of mapping class group invariant subspaces of $H(Σ)$ which arise from various geometric properties (e.g. geodesic or metric completeness, ergodicity of the geodesic flow, lower systole bound, discrete length spectrum) of the hyperbolic structure. In particular, we show that the space of geodesically complete convex hyperbolic structures in $H(Σ)$ is locally path connected, connected and decomposes naturally into Teichmüller subspaces. The big mapping class group of $Σ$ acts faithfully on this space allowing us to classify mapping classes into three types ({\it always quasiconformal, sometimes quasiconformal, and never quasiconformal}) in terms of their dynamics on the Teichmüller subspaces. Moreover, each type contains infinitely many mapping classes, and the type is relative to the underlying subspace of $H(Σ)$ that is being considered. As an application of our work, we show that if the mapping class group of a general topological surface $Σ$ is algebraically isomorphic to the modular group of a Riemann surface $X$, then $Σ$ is of finite topological type and $X$ is homeomorphic to it. Moreover, a big mapping class group can not act on any Teichmüller space with orbits equivalent to modular group orbits.
Miguel Arturo Vega Torres, Alexander Braun, André Borrmann
This paper presents a pioneering solution to the task of integrating mobile 3D LiDAR and inertial measurement unit (IMU) data with existing building information models or point clouds, which is crucial for achieving precise long-term localization and mapping in indoor, GPS-denied environments. Our proposed framework, SLAM2REF, introduces a novel approach for automatic alignment and map extension utilizing reference 3D maps. The methodology is supported by a sophisticated multi-session anchoring technique, which integrates novel descriptors and registration methodologies. Real-world experiments reveal the framework's remarkable robustness and accuracy, surpassing current state-of-the-art methods. Our open-source framework's significance lies in its contribution to resilient map data management, enhancing processes across diverse sectors such as construction site monitoring, emergency response, disaster management, and others, where fast-updated digital 3D maps contribute to better decision-making and productivity. Moreover, it offers advancements in localization and mapping research. Link to the repository: https://github.com/MigVega/SLAM2REF, Data: https://doi.org/10.14459/2024mp1743877.
The preventive assessment of archaeological
interest is law in Italy since
2005, but was practised since well before:
it is the normative and scientific
medium that regulates the relationship
between public works and archaeology,
and as such needs to be properly
communicated by the archaeologists
in charge, so as to interest and involve
citizens.
An unusual situation exists in Indonesia, where cadastral measurement and mapping for land registration do not adhere to a single coordinate reference framework. The Technical Guidelines for PTSL (2020–2022) state that land parcel measurements can be tied to the National TDT and/or to CORS reference stations, despite the fact that these two use different datum systems. If this is done, issues of gaps and overlaps may arise between adjacent land parcels during mapping. This study aims to highlight the multiplicity of coordinate reference frameworks used in cadastral measurement and mapping, and their relation to the national single map policy. A descriptive-comparative method with a quantitative approach was employed to compare the coordinates resulting from TDT measurements that fall within different coordinate systems and are used in cadastral mapping. The results indicate significant differences in the TDT position coordinates due to the different epoch references of the datum systems, as well as variations in the shape of the TDT network in the study area. This implies that TDT of orders 2, 3, 4, and densification orders from BPN cannot be used as a reference for land parcel binding in cadastral mapping. Keywords: mapping datum, multiple coordinate reference frameworks, coordinate differences, cadastral mapping. INTISARI Suatu kondisi yang tidak lazim terjadi di Indonesia, di mana pengukuran dan pemetaan kadastral untuk pendaftaran tanah tidak mengacu pada satu kerangka acuan koordinat yang tunggal. Petunjuk Teknis PTSL (2020–2022) menyatakan bahwa pengukuran bidang-bidang tanah dapat diikatkan pada TDT Nasional dan/atau pada stasiun acuan CORS, meskipun keduanya menggunakan sistem datum yang berbeda. Jika hal ini dilakukan, akan timbul masalah gap dan overlap pada bidang-bidang tanah yang berbatasan saat pemetaan dilakukan. Penelitian ini bertujuan untuk mengungkap keberagaman kerangka acuan koordinat yang digunakan dalam pengukuran dan pemetaan kadastral, serta kaitannya dengan kebijakan peta tunggal nasional. Metode deskriptif-komparatif dengan pendekatan kuantitatif digunakan untuk membandingkan koordinat hasil pengukuran TDT yang berada pada sistem koordinat yang berbeda dan digunakan dalam pemetaan kadastral. Hasil penelitian menunjukkan adanya perbedaan yang signifikan dalam koordinat posisi TDT, yang disebabkan oleh perbedaan epok referensi sistem datum. Selain itu, terdapat variasi perubahan bentuk jaring TDT di daerah penelitian. Ini berarti bahwa TDT orde 2, 3, 4, dan orde perapatan dari BPN tidak dapat digunakan sebagai acuan pengikatan bidang tanah untuk pemetaan kadastral. Kata Kunci: datum pemetaan, kerangka acuan koordinat yang beragam, perbedaan koordinat, pemetaan kadastral.
Building extraction based on high-resolution remote sensing imagery has been widely used in automatic surveying and mapping. However, few methods have been developed for building instance extraction, i.e., extracting each building’s footprint separately, which is required in a number of applications, such as the smallest unit of a cadastral database. In building instance extraction, there are two challenges: 1) buildings with various scales exist in the imagery and 2) precise building footprints are difficult to extract due to the blurry boundaries. In this article, to solve these problems, a multiscale U-shaped convolutional neural network building instance extraction framework with edge constraint (EMU-CNN) for high-spatial-resolution remote sensing imagery is proposed. The proposed framework consists of three components: 1) a multiscale fusion U-shaped network (MFUN); 2) a region proposal network (RPN); and 3) an edge-constrained multitask network (ECMN). First, in the proposed method, the MFUN includes three parallel branches to learn multiple building features with different scales. The RPN then detects the positions of the building instances, even for buildings that are connected with each other. Moreover, according to the instance positions, the ECMN is proposed to extract a precise mask and suppress overfitting. The experiments conducted on a self-annotated data set and two public data sets (the ISPRS Vaihingen semantic labeling contest data set and the WHU aerial image data set) show that the EMU-CNN method can achieve excellent performance and shows great robustness at different scales.
Information about the species composition of a forest is necessary for assessing biodiversity in a particular region and making economic decisions on the management of forest resources. Recognition of the species composition, according to the Earth’s remote sensing data, greatly simplifies the work and reduces time and labor costs in comparison with a traditional inventory of the forest, conducted through ground-based observations. This study analyzes the possibilities of tree species discrimination in coniferous–deciduous forests according to Sentinel-2 data using two automated recognition methods: random forest (RF) and generative topographic mapping (GTM). As remote sensing data, Sentinel-2 images of the Raifa section of Volga-Kama State Reserve in the Tatarstan Republic, Russia used: six images for the vegetation period of 2020. The analysis was carried out for the main forest-forming species. The training sample was created based on the cadastral data of the forest fund. The recognition quality was assessed using the F1-score, precision, recall, and accuracy metrics. The RF method showed a higher recognition accuracy. The accuracy of correct recognition by the RF method on the training sample reaches 0.987, F1-score = 0.976, on the control sample, accuracy = 0.764, F1-score = 0.709.
Halima Bouzidi, Mohanad Odema, Hamza Ouarnoughi
et al.
Heterogeneous MPSoCs comprise diverse processing units of varying compute capabilities. To date, the mapping strategies of neural networks (NNs) onto such systems are yet to exploit the full potential of processing parallelism, made possible through both the intrinsic NNs' structure and underlying hardware composition. In this paper, we propose a novel framework to effectively map NNs onto heterogeneous MPSoCs in a manner that enables them to leverage the underlying processing concurrency. Specifically, our approach identifies an optimal partitioning scheme of the NN along its `width' dimension, which facilitates deployment of concurrent NN blocks onto different hardware computing units. Additionally, our approach contributes a novel scheme to deploy partitioned NNs onto the MPSoC as dynamic multi-exit networks for additional performance gains. Our experiments on a standard MPSoC platform have yielded dynamic mapping configurations that are 2.1x more energy-efficient than the GPU-only mapping while incurring 1.7x less latency than DLA-only mapping.
Jose Rafael Borges Zampiva, Guillermo Penafort-Sanchis, Bruna Orefice-Okamoto
et al.
A reflection mapping is a singular holomorphic mapping obtained by restricting the quotient mapping of a complex reflection group. We study the analytic structure of double point spaces of reflection mappings. In the case where the image is a hypersurface, we obtain explicit equations for the double point space and for the image as well. In the case of surfaces in $\C^3$, this gives a very efficient method to compute the Milnor number and delta invariant of the double point curve.
Robotic mapping is useful in scientific applications that involve surveying unstructured environments. This paper presents a target-oriented mapping system for sparsely distributed geologic surface features, such as precariously balanced rocks (PBRs), whose geometric fragility parameters can provide valuable information on earthquake shaking history and landscape development for a region. With this geomorphology problem as the test domain, we demonstrate a pipeline for detecting, localizing, and precisely mapping fragile geologic features distributed on a landscape. To do so, we first carry out a lawn-mower search pattern in the survey region from a high elevation using an Unpiloted Aerial Vehicle (UAV). Once a potential PBR target is detected by a deep neural network, we track the bounding box in the image frames using a real-time tracking algorithm. The location and occupancy of the target in world coordinates are estimated using a sampling-based filtering algorithm, where a set of 3D points are re-sampled after weighting by the tracked bounding boxes from different camera perspectives. The converged 3D points provide a prior on 3D bounding shape of a target, which is used for UAV path planning to closely and completely map the target with Simultaneous Localization and Mapping (SLAM). After target mapping, the UAV resumes the lawn-mower search pattern to find the next target. We introduce techniques to make the target mapping robust to false positive and missing detection from the neural network. Our target-oriented mapping system has the advantages of reducing map storage and emphasizing complete visible surface features on specified targets.
In this article, we study the normal generation of the mapping class group. We first show that a mapping class is a normal generator if its restriction on the invariant subsurface normally generates the (pure) mapping class group of the subsurface. As an application, we provided a criterion for reducible mapping classes to normally generate the mapping class groups in terms of its asymptotic translation lengths on Teichmüller spaces. This is an analogue to the work of Lanier-Margalit dealing with pseudo-Anosov normal generators.
Mapping programs from one architecture to another plays a key role in technologies such as binary translation, decompilation, emulation, virtualization, and application migration. Although multicore architectures are ubiquitous, the state-of-the-art translation tools do not handle concurrency primitives correctly. Doing so is rather challenging because of the subtle differences in the concurrency models between architectures. In response, we address various aspects of the challenge. First, we develop correct and efficient translations between the concurrency models of two mainstream architecture families: x86 and ARM (versions 7 and 8). We develop direct mappings between x86 and ARMv8 and ARMv7, and fence elimination algorithms to eliminate redundant fences after direct mapping. Although our mapping utilizes ARMv8 as an intermediate model for mapping between x86 and ARMv7, we argue that it should not be used as an intermediate model in a decompiler because it disallows common compiler transformations. Second, we propose and implement a technique for inserting memory fences for safely migrating programs between different architectures. Our technique checks robustness against x86 and ARM, and inserts fences upon robustness violations. Our experiments demonstrate that in most of the programs both our techniques introduce significantly fewer fences compared to naive schemes for porting applications across these architectures.
We give a direct proof that middle mapping spaces in coherent nerves of Kan enriched categories have the same homotopy type as the original mapping spaces.