Abstract. Very High-resolution (VHR) optical satellites with a ground sampling distance (GSD) of 1m and less for nadir view began with IKONOS in 1999. There are now several VHR optical satellites. A WorldView-2 image compared the advantage of higher-resolution space images for mapping purposes with some lower-resolution VHR images. The orbit altitude of WorldView-2 (WV2) was changed from 767km to 680km in 2011, reducing the GSD in the nadir view from 0.46m to 0.41m. The WV2 image was taken at an incidence angle of 33.3°, resulting in a GSD of 0.49 m times 0.59 m, or 0.54 m on average. The information content analysis confirmed the generally required production scale of 0.05 to 0.1 mm GSD at map scale. This corresponds to a topographic map scale of 1:10,000 for 1 m and 1:5000 for 0.5 m GSD images. This is also based on test fields in İstanbul, Adalar district. The required mapping detail that could be identified using with the VHR space images is dominated by the ground resolution available as the ground sampling distance (GSD). WV2 imagery has proven to help update the GIS and cadastral database.
Finding the cadastral boundaries of farmlands is a crucial concern for land administration. Therefore, using deep learning methods to expedite and simplify the extraction of cadastral boundaries from satellite and unmanned aerial vehicle (UAV) images is critical. In this paper, we employ transfer learning to train a U-Net model with a ResNet34 backbone to detect cadastral boundaries through three-class semantic segmentation: "boundary", "field", and "background". We evaluate the performance on two satellite images from farmlands in Iran using "precision", "recall", and "F-score", achieving high values of 88%, 75%, and 81%, respectively, which indicate promising results.
Tristan Karch, Jakhongir Saydaliev, Isabella Di Lenardo
et al.
Cadastral data reveal key information about the historical organization of cities but are often non-standardized due to diverse formats and human annotations, complicating large-scale analysis. We explore as a case study Venice's urban history during the critical period from 1740 to 1808, capturing the transition following the fall of the ancient Republic and the Ancien Régime. This era's complex cadastral data, marked by its volume and lack of uniform structure, presents unique challenges that our approach adeptly navigates, enabling us to generate spatial queries that bridge past and present urban landscapes. We present a text-to-programs framework that leverages Large Language Models (\llms) to process natural language queries as executable code for analyzing historical cadastral records. Our methodology implements two complementary techniques: a SQL agent for handling structured queries about specific cadastral information, and a coding agent for complex analytical operations requiring custom data manipulation. We propose a taxonomy that classifies historical research questions based on their complexity and analytical requirements, mapping them to the most appropriate technical approach. This framework is supported by an investigation into the execution consistency of the system, alongside a qualitative analysis of the answers it produces. By ensuring interpretability and minimizing hallucination through verifiable program outputs, we demonstrate the system's effectiveness in reconstructing past population information, property features, and spatiotemporal comparisons in Venice.
The MITRE Adversarial Tactics, Techniques and Common Knowledge (MITRE ATT&CK) Attack Technique to Proactive Software Supply Chain Risk Management Framework (P-SSCRM) Task mapping described in this document helps software organizations to determine how different tasks mitigate the attack techniques of software supply chain attacks. The mapping was created through four independent strategies to find agreed-upon mappings. Because each P-SSCRM task is mapped to one or more tasks from the 10 frameworks, the mapping we provide is also a mapping between MITRE ATT&CK and other prominent government and industry frameworks.
Constructing high-definition (HD) maps from sensory input requires accurately mapping the road elements in image space to the Bird's Eye View (BEV) space. The precision of this mapping directly impacts the quality of the final vectorized HD map. Existing HD mapping approaches outsource the projection to standard mapping techniques, such as attention-based ones. However, these methods struggle with accuracy due to generalization problems, often hallucinating non-existent road elements. Our key idea is to start with a geometric mapping based on camera parameters and adapt it to the scene to extract relevant map information from camera images. To implement this, we propose a novel probabilistic projection mechanism with confidence scores to (i) refine the mapping to better align with the scene and (ii) filter out irrelevant elements that should not influence HD map generation. In addition, we improve temporal processing by using confidence scores to selectively accumulate reliable information over time. Experiments on new splits of the nuScenes and Argoverse2 datasets demonstrate improved performance over state-of-the-art approaches, indicating better generalization. The improvements are particularly pronounced on nuScenes and in the challenging long perception range. Our code and model checkpoints are available at https://github.com/Fatih-Erdogan/mapping-like-skeptic .
Israel Nunes da Silva, Gabriel Dietzsch, Elcio Hideiti Shiguemori
Typically, the modernization of Land Administration Systems (LAS) concentrates on overarching aspects and seldom investigates the spatial infrastructure that underpins it, thereby presenting challenges for the integration of geospatial data. For this purpose, Discrete Global Grid Systems (DGGS), characterized by its "congruent cartography", offer a promising solution within a multi-scale reference framework. Moreover, a significant gap exists in the absence of a DGGS designed to address the cartographic focus and usability requirements for land administration, such as equal-area sizing and geodetic precision. Developed at the Aeronautics Institute of Technology (ITA), the ITA Cadastral Ellipsoidal Reference Tessellation (ITACaRT) was introduced as an innovative DGGS to bridge this gap. The development of ITACaRT was guided by several key criteria, including its suitability for cadastral purposes at appropriate scales, compatibility with the WGS84 ellipsoid and Global Navigation Satellite Systems (GNSS), utilization of simple parallelogram-shaped equal-area cells, a direct tessellation adhering to Cartesian geometry for usability by geoinformation professionals, and decimal convergence to facilitate blockchain tokenization. Complementary to these criteria, a Compositional Hierarchical Indexing system was devised to represent cadastral vector features more efficiently than the atomic identifiers typical of conventional DGGS. ITACaRT thus establishes a solid foundation for contemporary LAS, providing a viable spatial infrastructure that supports emerging technologies such as blockchain.
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.
The Italian Institute for Environmental Protection and Research (ISPRA) is leading a nationwide initiative to map and restore seagrass meadows under the Marine Ecosystem Restoration (MER) project. This effort addresses the alarming decline of Posidonia oceanica and Cymodocea nodosa habitats, which are critical for carbon sequestration, biodiversity, and coastal resilience. The MER project’s mapping component, executed by Fugro and Compagnia Generale Ripreseaeree (CGR), in partnership with EOMAP – a Fugro company, and PlanBlue, employed a multi-sensor approach, combining satellite, airborne, vessel-based (high-resolution multibeam), and autonomous underwater vehicle (AUV) technologies. The integration of bathymetric LiDAR, multibeam, optical and multispectral data allowed continuous bathymetric coverage from the coastline to 50 metre depth. The Virgeo® platform, specifically developed by Fugro, facilitated real-time monitoring of acquisitions and data collected by ships and aircraft engaged in the surveys. This integrated approach provided a robust baseline for restoration planning and long-term monitoring, offering a scalable, cost-effective solution for national marine habitat assessments. The Piano Nazionale di Ripresa e Resilienza (PNRR) MER project was funded by MASE, coordinated by ISPRA and scientifically supported by Italian research institutes and universities (CNR-IGAG, IIM, Sapienza, INGV, PoliMi, UniPd, UniGe).
Israel Oluwaseun Taiwo, Olomolatan Matthew Ibitoye, Sunday Olukayode Oladejo
Towards achieving Fit-for-Purpose (FFP) cadastral mapping, this study compares conventional cadastral mapping methods of theodolite traverse, total station and Real Time Kinematic Global Navigation Satellite System (RTK GNSS) with high-resolution aerial imagery in Ekiti State, Nigeria. Evaluating time, cost, accuracy, and coverage, it finds that high-resolution aerial imagery can achieve results comparable to conventional instruments with wider coverage, thereby expediting land registration and fostering sustainable development. However, challenges in obtaining high-resolution imagery necessitate regulatory reform for UAV use. The study recommends adoption of innovative solutions to improve the spatial, legal, and institutional frameworks in the state and enhance land governance.
Israel Oluwaseun Taiwo, M. Ibitoye, Sunday Olukayode Oladejo
et al.
In developing nations, such as Ekiti State, Nigeria, the utilization of remotely sensed data, particularly satellite and UAV imagery, remains significantly underexploited in land administration. This limits multi-resolution imagery’s potential in land governance and socio-economic development. This study examines factors influencing UAV adoption for land administration in Nigeria, mapping seven rural, peri-urban, and urban sites with orthomosaics (2.2 cm to 3.39 cm resolution). Boundaries were manually delineated, and parcel areas were calculated. Using the 0.05 m orthomosaic as a reference, the Horizontal Radial Root Mean Square Error (RMSEr) and Normalized Parcel Area Error (NPAE) were computed. Results showed a consistent increase in error with increasing resolution (0.1 m to 1 m), with RMSEr ranging from 0.053 m (formal peri-urban) to 2.572 m (informal rural settlement). Formal settlements with physical demarcations exhibited more consistent values. A comparison with GNSS data revealed that RMSEr values conformed to the American Society for Photogrammetry and Remote Sensing (ASPRS) Class II and III standards. The research demonstrates physical demarcations’ role in facilitating cadastral mapping, with formal settlements showing the highest suitability. This study recommends context-specific imagery resolution to enhance land governance. Key implications include promoting settlement typology awareness and addressing UAV regulatory challenges. NPAE values can serve as a metric for assessing imagery resolution fitness for cadastral mapping.
Mean value coordinates can be used to map one polygon into another, with application to computer graphics and curve and surface modelling. In this paper we show that if the polygons are quadrilaterals, and if the target quadrilateral is convex, then the mapping is injective.
Maps provide robots with crucial environmental knowledge, thereby enabling them to perform interactive tasks effectively. Easily accessing accurate abstract-to-detailed geometric and semantic concepts from maps is crucial for robots to make informed and efficient decisions. To comprehensively model the environment and effectively manage the map data structure, we propose DHP-Mapping, a dense mapping system that utilizes multiple Truncated Signed Distance Field (TSDF) submaps and panoptic labels to hierarchically model the environment. The output map is able to maintain both voxel- and submap-level metric and semantic information. Two modules are presented to enhance the mapping efficiency and label consistency: (1) an inter-submaps label fusion strategy to eliminate duplicate points across submaps and (2) a conditional random field (CRF) based approach to enhance panoptic labels through object label comprehension and contextual information. We conducted experiments with two public datasets including indoor and outdoor scenarios. Our system performs comparably to state-of-the-art (SOTA) methods across geometry and label accuracy evaluation metrics. The experiment results highlight the effectiveness and scalability of our system, as it is capable of constructing precise geometry and maintaining consistent panoptic labels. Our code is publicly available at https://github.com/hutslib/DHP-Mapping.
LiDAR technology has emerged as a superior tool for largescale mapping, particularly in cadastral surveying. By using laser pulses to measure distances, LiDAR creates highly accurate 3D representations of the Earth’s surface, surpassing traditional methods in terms of speed and efficiency. This study investigated LiDAR’s effectiveness in rural cadastral mapping, addressing the limitations of traditional techniques like total stations and GNSS. Using FARO TLS and Leica RTK-GPS, researchers collected dense point cloud data and processed it using FARO Scene and Cloud Compare software. LiDAR demonstrated exceptional accuracy, achieving a mean square error of $\pm 0.047 \mathrm{~m}$, well within the acceptable limit for cadastral applications. Additionally, it significantly improved operational efficiency, reducing survey time and personnel requirements by approximately five times. These results highlight LiDAR’s potential to revolutionize cadastral mapping and enhance land administration in rural areas.
Abstract. Unmanned Aerial Systems (UAS) are increasingly used in different applications, including 3D urban modelling, cadastral mapping, urban planning, GIS information system and other fields because of their advantages. As a consequence, UAS equipment is constantly developed to provide more accurate results in a more reliable mode. This paper aims to evaluate the performances of a low-cost UAS system, namely DJI Phantom 4 Pro v2 equipped with a TeoKIT GNSS PPK (post-processing kinematic) module for cadastral mapping purposes. Two fights (oblique and nadir) over a residential area at 60 m height were performed and some 100 ground points were used to derive RMSE accuracies. Comparison between GNSS-aided with PPK processing and indirect georeferencing processes are performed. Given a mobile laser scanner (MLS) point cloud as ground truth, comparison with UAS point clouds and manually digitized features are also performed and reported.
Wolfgang Göderle, Christian Macher, Katrin Mauthner
et al.
Cadastres from the 19th century are a complex as well as rich source for historians and archaeologists, whose use presents them with great challenges. For archaeological and historical remote sensing, we have trained several Deep Learning models, CNNs as well as Vision Transformers, to extract large-scale data from this knowledge representation. We present the principle results of our work here and we present a the demonstrator of our browser-based tool that allows researchers and public stakeholders to quickly identify spots that featured buildings in the 19th century Franciscean Cadastre. The tool not only supports scholars and fellow researchers in building a better understanding of the settlement history of the region of Styria, it also helps public administration and fellow citizens to swiftly identify areas of heightened sensibility with regard to the cultural heritage of the region.