Mapping Agency Research Articles

A coupled cartographic approach between bioclimatology and vegetation formations of Mexico

Aims: The task of classifying and naming Mexican vegetation types has been undertaken by previous botanists, ecologists, and mapping agencies. However, discrepancies remain due to the lack of criteria and joint efforts from a geographical and botanical perspective. We aim to unravel the complex interactions between climate and vegetation in Mexico using climatic data and advanced mapping techniques, display in maps the transition from land cover to vegetation maps and couple geobotanical and bioclimatological approaches to provide a sound, unified system for identifying Mexican bioclimatic physiognomic patterns. Methods: Bioclimatic mapping was developed from the Digital Climatic Atlas of Mexico data source. In addition, land cover and vegetation data were obtained from the National Institute of Statistics and Geography of Mexico regrouped as described by the Standardized Hierarchical Vegetation Classification. These data were analysed via standard map crossing technics using geographic information systems. Results and conclusions: The results revealed five ombrotypes and five thermotypes, leading to the identification of 13 different bioclimatic classes, which, when combined with physiognomic types, led us to recognize 11 forests, 3 shrublands and 3 herbaceous formations (at a scale of 1:4,000,000). The core outcome is a detailed bioclimatic/physiognomic vegetation map including forests, shrublands and areas dominated by Herbaceous/Non-Vascular formations. The map highlights the critical importance of harmonising methodologies to ensure comprehensive and accurate insights into Mexico’s bioclimatic diversity. Taxonomic reference: Villaseñor et al. (2005). Syntaxonomic reference: Velázquez et al. (2021). Abbreviations: INEGI = Instituto Nacional de Estadística y Geografía; SECLAVEMEX = Standardized Hierarchical Vegetation Classification; WBCS = Worldwide Bioclimatic Classification System.

Towards National Connected Digital Twins - A Geospatial Perspective

Abstract. Digital Twins are realistic digital representations of the physical world, frequently characterised by a two way link between digital and physical. Originating in manufacturing, they are now expanding to city and national scales. In this paper we explore connections between Geographic Information Science and National Digital Twins. Six different viewpoints and perspectives are presented on the topic, highlighting the importance of: geospatial data engineering; metadata engineering; standards; challenges facing mapping agencies; governance and public values; and a ”reality check” that explores the gaps between what is required and what can currently be achieved. We present 22 recommendations and summarise the findings by presenting a high level research agenda to enable better understanding and articulation of the link between the two.

From research to production: radiometric block adjustment to automate and improve tonal adjustment of orthomosaics created from airborne images

Abstract. Production of orthophotos and orthomosaics from airborne imaging has been standard procedure of National mapping agencies for decades. Various aspects affect to the colour, or tones of the images: time of the day and year, atmosphere, illumination conditions, view and illumination angle, BRDF-effects (Bi-directional reflectance distribution function), object, sensor, and whole imaging system. Quantitative and automated solution for creating evenly coloured image mosaics is to use method based on radiometric block adjustment, that has similarities to more well-known geometric block adjustment. We have applied radiometric block adjustment method called RadBA, originally developed for drone image blocks collected with hyperspectral sensor, to image block collected with large-format photogrammetric camera. Goals of our work are: 1) to speed up orthophoto deliveries to Finish Food Authority used for EU farming subsidies monitoring, 2) improve the quality of images delivered to Finnish Forest Centre used for forest inventory, and 3) to automate, fasten and improve the current colour balancing process of orthophotos at National Land Survey of Finland. RadBA-based tonal processing with 4-parameter BRDF-correction was able to clearly improve the tonal quality of the image mosaic collected during three different dates. We still need to automate various steps of the RadBA-workflow and improve final steps in converting 16-bits per band mosaics to visually good looking 8-bits per band mosaics. Our long-term goal is to create tonally high quality, seamless orthomosaic of whole Finland.

Building Detection from SkySat Images with Transfer Learning: a Case Study over Ankara

AbstractThe detection and continuous updating of buildings in geodatabases has long been a major research area in geographic information science and is an important theme for national mapping agencies. Advancements in machine learning techniques, particularly state-of-the-art deep learning (DL) models, offer promising solutions for extracting and modeling building rooftops from images. However, tasks such as automatic labelling of learning data and the generalizability of models remain challenging. In this study, we assessed the sensor and geographic area adaptation capabilities of a pretrained DL model implemented in the ArcGIS environment using very-high-resolution (50 cm) SkySat imagery. The model was trained for digitizing building footprints via Mask R‑CNN with a ResNet50 backbone using aerial and satellite images from parts of the USA. Here, we utilized images from three different SkySat satellites with various acquisition dates and off-nadir angles and refined the pretrained model using small numbers of buildings as training data (5–53 buildings) over Ankara. We evaluated the buildings in areas with different characteristics, such as urban transformation, slums, regular, and obtained high accuracies with F‑1 scores of 0.92, 0.94, and 0.96 from SkySat 4, 7, and 17, respectively. The study findings showed that the DL model has high transfer learning capability for Ankara using only a few buildings and that the recent SkySat satellites demonstrate superior image quality.

A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities

Abstract. Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is nowadays seen as a standard approach for classifying any zone according to a set of urban canopy parameters. While many methods already exist to map the LCZ, only few tools are openly and freely available. This paper presents the algorithm implemented in the GeoClimate software to identify the LCZ of any place in the world based on vector data. Six types of information are needed as input: the building footprint, road and rail networks, water, vegetation, and impervious surfaces. First, the territory is partitioned into reference spatial units (RSUs) using the road and rail network, as well as the boundaries of large vegetation and water patches. Then 14 urban canopy parameters are calculated for each RSU. Their values are used to classify each unit to a given LCZ type according to a set of rules. GeoClimate can automatically prepare the inputs and calculate the LCZ for two datasets, namely OpenStreetMap (OSM, available worldwide) and the BD TOPO® v2.2 (BDT, a French dataset produced by the national mapping agency). The LCZ are calculated for 22 French communes using these two datasets in order to evaluate the effect of the dataset on the results. About 55 % of all areas have obtained the same LCZ type, with large differences when differentiating this result by city (from 30 % to 82 %). The agreement is good for large patches of forest and water, as well as for compact mid-rise and open low-rise LCZ types. It is lower for open mid-rise and open high-rise, mainly due to the height underestimation of OSM buildings located in open areas. Through its simplicity of use, GeoClimate has great potential for new collaboration in the LCZ field. The software (and its source code) used to produce the LCZ data is freely available at https://doi.org/10.5281/zenodo.6372337 (Bocher et al., 2022); the scripts and data used for the purpose of this article can be freely accessed at https://doi.org/10.5281/zenodo.7687911 (Bernard et al., 2023) and are based on the R package available at https://doi.org/10.5281/zenodo.7646866 (Gousseff, 2023).

Evaluating the Impact of the Recent Combined and Satellite-Only Global Geopotential Model on the Gravimetric Geoid Model

Geoid represents Earth’s surface, ocean, and gravitational field, which influence the elevations, shape, and mass distribution of the geopotential surface, a hypothetical surface that is perpendicular to the direction of gravity at every point. This geopotential surface serves as a reference for measuring elevations and is used as a fundamental reference surface for geodetic and surveying purposes. In this study, the Least Squares Modification of Stokes Formula (LSMS) with Additive Corrections (AC), also known as the KTH method, is used to generate a new gravimetric geoid model for Peninsular Malaysia. The KTH method was developed at the Royal Institute of Technology (KTH) in Stockholm-Sweden. The dataset used is the most recent global digital elevation model, Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global, generated by the National Aeronautics and Space Administration (NASA) and the National Imagery and Mapping Agency (NIMA). In addition to this elevation data, the dataset includes the Global Geopotential Model (GGM), which is composed of the XGM2016, XGM2019e, Tongji_GGMG2021S, and Tongji-Grace02k models. Furthermore, it incorporates sets of regional gravity data, including terrestrial gravity, airborne gravity, and marine gravity anomalies, all of which are derived from the Technical University of Denmark (DTU 21). The actual 45 Global Navigation Satellite System (GNSS)-levelling points data have been compared to the gravimetric geoid model developed in this study and the geoid acquired from Department of Survey and Mapping Malaysia (DSMM). According to the statistical results, NXGM2019e provides better accuracy, with the Root Mean Square Error (RMSE) geoid model errors of ±0.033 m, compared to the deviations in free-air anomalies, XGM2019e, which has the minimum RMSE of 10.291 mGal. Meanwhile, Tongji-GMMG2021S has the maximum RMSE of 14.792 mGal. The geoid is derived from the XGM2019e model and has maximum and minimum values of 0.032 m and 0.147 m, respectively, with mean residuals of 0.089 m. In conclusion, the XGM2019e has the potential to determine a precise local geoid model for Peninsular Malaysia

LEARNING ON THE EDGE: BENCHMARKING ACTIVE LEARNING FOR THE SEMANTIC SEGMENTATION OF ALS POINT CLOUDS

Abstract. While most research in automatic semantic segmentation of 3D geospatial point clouds is concerned with enhancing respective Machine Learning (ML) models, we aim to shift the focus to be more of a data-centric nature. This means, we consider the creation of respective data sets that ML models learn from as key component, since even the most sophisticated model performs poorly when learning from suboptimal data. In this regard, the straightforward approach of providing labeled data abundantly is prohibitively expensive and just not scalable in times of high-frequency data acquistion cycles, where a dedicated training set should be available for each new epoch, as ML models often lack generalizability. As a remedy, we rely on Active Learning (AL), which is a cost-efficient and quick method to generate required training data at scale. Although AL has been (scarcely) applied in the geospatial domain before, a comprehensive evaluation of its capabilities, including benchmarking of achievable accuracies is lacking. Therefore, we apply the AL concept to both ISPRS’ current point cloud benchmark data sets as well as to a third large scale National Mapping Agency point cloud. Respective experiments are conducted with both a feature-driven Random Forest classifcation approach and a data-driven Submanifold Sparse Convolutional Neural Network classifier. Our experiments verify that by labeling only a fraction of available training points (typically ⪡ 1%), we can still reach accuracies that are at maximum only about 5 percentage points worse compared to leading benchmark contributions.

Running blind: the sensory practices of visually impaired runners

ABSTRACT In this article, we consider what it means to be running blind. As an idiom, this phrase refers to doing something through guesswork, without a plan or pre-existing knowledge; instead, our notion of running blind offers an alternative perspective. Drawing upon a novel theoretical approach, which is multidisciplinary and engages with the work of visually impaired (VI) and non-VI scholars, we establish the rich, creative and diverse sensory practices of VI runners. The data in this article is drawn from a qualitative study involving eight blind and partially sighted participants, who each took part in two semi-structured interviews. Using abductive analysis, our discussion is organised around the route: 1) Knowing the route; 2) Navigating the route; and 3) Pleasures of the route. While VI runners’ sensory practices are unique, there are significant commonalities in our participants’ experiences of running blind, including the mapping process, agency and freedom en route, multisensorial modes of navigation and more-than-visual running pleasures. This article demonstrates the importance of exploring non-ocularnormative conceptions of sport and physical activity, which is of value for VI runners, guide runners and key stakeholders alike.

GEOMETRIC PROCESSING OF VERY HIGH-RESOLUTION SATELLITE IMAGERY: QUALITY ASSESSMENT FOR 3D MAPPING NEEDS

Abstract. In recent decades, the geospatial domain has benefitted from technological advances in sensors, methodologies, and processing tools to expand capabilities in mapping applications. Airborne techniques (LiDAR and aerial photogrammetry) generally provide most of the data used for this purpose. However, despite the relevant accuracy of these technologies and the high spatial resolution of airborne data, updates are not sufficiently regular due to significant flight costs and logistics. New possibilities to fill this information gap have emerged with the advent of Very High Resolution (VHR) optical satellite images in the early 2000s. In addition to the high temporal resolution of the cost-effective datasets and their sub-meter geometric resolutions, the synoptic coverage is an unprecedented opportunity for mapping remote areas, multi-temporal analyses, updating datasets and disaster management. For all these reasons, VHR satellite imagery is clearly a relevant study for National Mapping and Cadastral Agencies (NMCAs). This work, supported by EuroSDR, summarises a series of experimental analyses carried out over diverse landscapes to explore the potential of VHR imagery for large-scale mapping.

Ai-Based On-Demand Mapping: An Alternative In Response To Ever-Growing Mapping Agencies’ Challenges

Ai-Based On-Demand Mapping: An Alternative In Response To Ever-Growing Mapping Agencies’ Challenges

OPENSTREETMAP AS AN INPUT SOURCE FOR PRODUCING GOVERNMENTAL DATASETS: THE CASE OF THE ITALIAN MILITARY GEOGRAPHIC INSTITUTE

Abstract. The role of Volunteered Geographic Information (VGI) to integrate, update or complement authoritative datasets released by governments has become increasingly important. This work analyses the contribution of OpenStreetMap (OSM), the most popular VGI project, as one of the input sources that the Military Geographic Institute (IGM), one of the Italian governmental mapping agencies, has used for producing the National Summary Database (DBSN). This database, which was recently released for 12 out of the 20 Italian regions, has a schema organised into a hierarchical structure composed of 10 layers, 30 themes and 93 classes, where each geospatial object carries information on the specific data source it was derived from. For each DBSN layer and theme, we first calculated the fraction of objects derived from OSM in all the Italian regions and related provinces. We found a heterogeneous picture with OSM contribution generally being limited, with the exception of few regions and layers/themes where the DBSN was almost exclusively derived from OSM. An in-depth comparison between the DBSN and OSM building datasets showed that OSM building completeness is varying across Italian regions and provinces, but in all regions there are buildings in OSM that are not included in the DBSN. The work shed light on the opportunities and obstacles for OSM to become a primary input source for the production of governmental datasets.

Thematic Comparison between ESA WorldCover 2020 Land Cover Product and a National Land Use Land Cover Map

This work presents a comparison between a global and a national land cover map, namely the ESA WorldCover 2020 (WC20) and the Portuguese use/land cover map (Carta de Uso e Ocupação do Solo 2018) (COS18). Such a comparison is relevant given the current amount of publicly available LULC products (either national or global) where such comparative studies enable a better understanding regarding different sets of LULC information and their production, focus and characteristics, especially when comparing authoritative maps built by national mapping agencies and global land cover focused products. Moreover, this comparison is also aimed at complementing the global validation report released with the WC20 product, which focused on global and continental level accuracy assessments, with no additional information for specific countries. The maps were compared by following a framework composed by four steps: (1) class nomenclature harmonization, (2) computing cross-tabulation matrices between WC20 and the Portuguese map, (3) determining the area occupied by each harmonized class in each data source, and (4) visual comparison between the maps to illustrate their differences focusing on Portuguese landscape details. Some of the differences were due to the different minimum mapping unit ofCOS18 and WC20, different nomenclatures and focuses on either land use or land cover. Overall, the results show that while WC20 detail is able to distinguish small occurrences of artificial surfaces and grasslands within an urban environment, WC20 is often not able to distinguish sparse/individual trees from the neighboring cover, which is a common occurrence in the Portuguese landscape. While selecting a map, users should be aware that differences between maps can have a range of causes, such as scale, temporal reference, nomenclature and errors.

Uvajanje tehnologije Linked Data v geodetskih upravah: sistematični pregled literature

This research focuses on a systematic literature review of the geospatial Linked Data (LD) publication at National Mapping and Cadastral Agencies in Europe (NMCA) responsible for land administration. We analysed scientific studies published from 2014 to 2023. Our research aimed to find out the reasons for the relatively slow adoption of geospatial data publication as LD. Therefore, we searched for the most common problems, solutions, and challenges for publishing prioritised data themes in NMCAs. Applied research methodology relies on well-established approaches such as analysing Population Problems, Intervention, Comparison, Outcomes, and Circumstances (PICOC) and backward snowballing. The eligibility process is presented by Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. We analysed nineteen extracted papers and identified twelve key issues from full-text reading. Frequency analyses resulted in the overview of the most frequently published geospatial themes as LD in several European countries. We addressed five research questions and concluded that in addition to systematic support for developing vocabularies and ontologies, trust, awareness, and knowledge about best practices could influence the success of LD publication at NMCAs.

Historical development of SIRGAS

Abstract The Geodetic Reference System for the Americas (Sistema de Referencia Geodésico para las Américas, SIRGAS) was initiated in 1993 for South America at an international conference organised by the International Association of Geodesy (IAG), the Pan-American Institute for Geography and History (PAIGH), the Deutsches Geodätisches Forschungsinstitut (DGFI), and the U.S. Defense Mapping Agency (DMA) in Asunción, Paraguay. The corresponding South American reference network was observed in 1995 by a ten-day GPS campaign at 58 stations. The network was extended to Central and North America in 2000 and immediately afterwards converted to a frame of continuously observing GNSS stations instead of short-term campaigns. The linear station position changes (velocities) were estimated by a multi-year least squares adjustment of weekly solutions, the first being published in 2002. The total set of station velocities served for the computation of continuous surface deformation models, the first over South America was published in 2005. Today, SIRGAS is accepted by most of the American states as the official geodetic reference frame. Besides the product generation (station positions, velocities, and surface deformation), SIRGAS is active in education and training offering schools and workshops for students, surveyors, and other stakeholders.

LOCATION-ENABLED DIGITAL TWINS – UNDERSTANDING THE ROLE OF NMCAS IN A EUROPEAN CONTEXT

Abstract. Digital Twins are realistic digital representation of physical objects and are differentiated from traditional models by the live connection between the digital and the physical worlds, often enabled by sensors. They provide insights into the physical world for decision makers, for example via simulation, and can be used to directly alter the physical world without manual intervention. While they have their origins in manufacturing, they are increasingly being used within the built environment, by both public and private sectors. Increasingly city-wide and National Digital Twins are also being considered, to underpin local, municipal and central government decision making. For these emerging Digital Twins, location data such as that provided by National Mapping and Cadastral Agencies (NMCAs) has the potential to underpin Digital Twin modelling. It thus becomes important for NMCAs to better understand Digital Twins in order to determine whether current data offerings can meet this new demand and how best to support the various activities. As a first stage investigation under the auspices of EuroSDR, this paper explores challenges and opportunities for NMCAs and others working in the location sector, presenting the result of an international survey and workshop on these topics. We conclude that there is significant overlap with existing challenges within the geospatial community and those required to better support Digital Twins - e.g. interoperability and data management and governance. Additionally, the opportunity for a broader understanding and uptake of location data offered by Digital Twins should not be ignored.

EXPLORING EXISTING 3D RECONSTRUCTION TOOLS FOR THE GENERATION OF 3D CITY MODELS AT VARIOUS LOD FROM A SINGLE DATA SOURCE

Abstract. The use of three-dimensional (3D) city models has increased in a wide range of applications beyond visualisation. However, generation and maintenance of 3D data comes at a high cost, time, and workload. The purpose of the generalisation where coarser versions are obtained from a source data is of great interest for National Mapping and Cadastral Agencies (NMCA), which would benefit obtaining multiple 3D versions of an area from a single source. The main aim of the exploration presented in this paper is to study the potential of downsizing point clouds as an approach to generate 3D city models at multiple levels of detail from a single source and evaluate the steps required to ensure the output is fit for real world applications from an NMCA context. While interesting results are obtained when testing with sample data, no software managed to semi-automatically reconstruct 3D model for buildings of rather complicated geometry.

Automatic vectorization of fluvial corridor features on historical maps to assess riverscape changes

ABSTRACT The vectorization of historical maps is an important scientific issue for understanding the dynamics of change recorded by territories. Historical maps are potentially an excellent source of data for characterizing river changes at large scales. The use of vectorized data is essential for such characterization, as well as for highlighting changes in the planform alignment of such reaches over time. At a regional network scale of several thousand kilometers of river, such work requires the vectorization of several hundred or even thousands of maps. This work proposes an automated vectorization procedure for the hydrographic network detailed in the cartographic resources of the IGN (the French National Mapping Agency). The ultimate goal is to use these historical maps to track the planform evolution of the elementary landscape units (water, bare banks, and riparian vegetation) that constitute river corridors at the basin network scale. The Historical Maps Vectorization Toolbox was developed to automatically vectorize river corridor objects (sediment banks, water surfaces, and vegetation polygons) with a high level of accuracy. The toolbox works with a 2-step process: first it classifies the colors detected on the map, then it reconstructs the objects of the fluvial corridor. We also demonstrate a practical use of the toolbox through measuring changes in the surface area of river networks of several hundred kilometers.

Soviet Tourist Maps: A Short Overview

ABSTRACT In recent years, many maps produced in the former Soviet Union have become available. However, researchers have focused on topographical maps or those of Western cities produced by the General Staff for military use, and less on maps created by GUGK, the state mapping agency, for domestic purposes, including tourism. By contrast, this paper focuses on maps produced for tourists, whether travelling by private car or on an organized rail or bus tour. The 42 maps reviewed take three main forms: those covering a single administrative region, ranging from an oblast to a Soviet republic; those of a specific tourist area or associated with a literary hero; and strip maps connecting major cities. The paper considers the general design and symbology used on these maps, tracing some of the stylistic changes and developments in the quarter-century prior to the collapse of the Soviet Union.

MAINTENANCE AND UPDATE NATIONAL TOPOGRAPHIC DATA BASE IN HIGH FREQENCY USING MODERN PROCEDURES AND TECHNIQUES

Abstract. The state of Israel is a small country, in terms of area, with one the higher population growth rate among all the OECD countries. Hence, each year more than 50,000 housing units built and more than 1,500 km roads constructed in average, each year along the last decade.The Survey of Israel )SOI( is a National Mapping and Cadastral agency (NMCA) that holds the responsibility to update, maintain and disseminate the National Topographic Data Base (NTDB) to all of the governmental offices and municipalities, to utilize, plan and promote the construction of the public needs.Today, the reality is dynamic and the changes occurs every day, therefore, the users expect that the existing database will portray the current reality as possible. In order to address the user's requirements for rapid updating rate, the Survey of Israel promote a new and modern initiatives, including:Promote the cooperation and working relationship with the municipalities to rapidly update the streets, address and POI’s.Data Fusion from different governmental and municipalities data sources.Developing change detection techniques to update the buildings and roads layers.Developing new frameworks for the manual inspection and QA/QC procedures.Developing automatic tools to update the height component via high resolution DSM and DTM (50cm GSD).Disseminate quarterly the NTDB products to the users.Develop a modern processes management system for the update and QA workflowIn this article, we will demonstrate the initiatives and techniques that improved our NTDB updating rate to less than a year and a half in a national average.

BUILDING CHANGE DETECTION IN AIRBORNE LASER SCANNING AND DENSE IMAGE MATCHING POINT CLOUDS USING A RESIDUAL NEURAL NETWORK

Abstract. National Mapping Agencies (NMAs) acquire nation-wide point cloud data from Airborne Laser Scanning (ALS) sensors as well as using Dense Image Matching (DIM) on aerial images. As these datasets are often captured years apart, they contain implicit information about changes in the real world. While detecting changes within point clouds is not a new topic per se, detecting changes in point clouds from different sensors, which consequently have different point densities, point distributions and characteristics, is still an on-going problem. As such, we approach this task using a residual neural network, which detects building changes using height and class information on a raster level. In the experiments, we show that this approach is capable of detecting building changes automatically and reliably independent of the given point clouds and for various building sizes achieving mean F1-Scores of 80.5% and 79.8% for ALS-ALS and ALS-DIM point clouds on an object-level and F1-Scores of 91.1% and 86.3% on a raster-level, respectively.