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In 1870, the newly created Instituto Geográfico of Spain, the precursor of the current Instituto Geográfico Nacional (IGN), began to carry out work for the preparation of the National Topographic Map of Spain, a major project that would take almost 100 years to be completed. This work began with the measurement of geodetic bases throughout the national territory. Subsequently, the necessary topographic surveys were conducted to delimit the boundaries of municipalities, and to represent all the planimetric elements. As a part of this, surveys were carried out with topographic compasses, which allowed work to be performed at a good pace and with sufficient accuracy for cartographic purposes. The current IGN keeps in its archives all the documentation generated in the work related to the completion of this major project. The objective of this study is to extract information from this documentation on the magnetic declination measured at that time, and to evaluate it as a possible source of historic geomagnetic information for use in future works. To achieve this, we compared the recovered declination dataset with those generated for the same locations and dates using two independent sources: the Cov-Obs.x2 geomagnetic field model, which spans the last two centuries, and the declination data used to produce the first Spanish declination chart developed by the IGN at the beginning of the 20th century. The results show a clear agreement between the recovered dataset and both sources of independent declination data, suggesting that this dataset is valuable for improving our understanding of the recent geomagnetic field history and for refining main field models for the last centuries.

This study described corneal maps and anterior-segment tomography images of rabbits’ eyes. The exams of 10 male 14-month-old rabbits (20 eyes) were analyzed. The animals were sedated and evaluated using a Galilei G4. The following maps were analyzed qualitatively: axial and instantaneous curvature, pachymetry, total corneal power, best-fit sphere (BFS) and best-fit toric asphere (BFTA) elevation, in addition to anterior-segment tomography images. The curvature maps were aspheric. A doughnut-shaped ring was observed in the posterior instantaneous curvature map. No patterns were observed in the elevation maps. The total corneal power (TCP) maps were multifocal and the TCP increased from the center to the periphery of the cornea. The pachymetry maps were uniform, without the pachymetry progression pattern seen in the human cornea. To the best of our knowledge, this is the first research to analyze in detail the topographic maps of rabbits’ corneas using the Galilei device. The maps were aspheric prolate, multifocal and uniformly thin; the anterior chamber was shallow; and the lens was spherical. In view of the importance of this species as an experimental model, the relevance of studies concerning rabbit corneal characteristics is highlighted. RESUMO: O objetivo deste trabalho foi descrever os mapas corneais e as imagens tomográficas do segmento anterior dos olhos de coelhos. Os exames de 10 coelhos machos de 14 meses (20 olhos) foram analisados. Os animais foram sedados e avaliados utilizando o Galilei G4. Os seguintes mapas foram avaliados qualitativamente: curvatura axial e instantânea, paquimetria, poder total da córnea (TCP), elevação (melhor encaixe esférico) BFS e (melhor encaixe tórico asférico) BFTA. em adição às imagens de tomografia do segmento anterior. Foram observados mapas de curvatura asféricos. Um anel semelhante a um doughnut foi observado no mapa de curvatura instantânea posterior. Os mapas de elevação não apresentaram padrão. Foram observados mapas de Poder corneal total (TCP) multifocal sendo que o poder aumentava do centro para a periferia da córnea. Os mapas de paquimetria foram uniformes, sem o padrão de progressão paquimétrica observado na córnea humana. De acordo com o conhecimento dos autores, este é o primeiro trabalho a analisar detalhadamente os mapas topográficos da córnea de coelhos usando o aparelho Galilei. Foram observados mapas asféricos prolatos, multifocais e uniformemente finos; câmara anterior rasa e lente espessa e esférica. Tendo em vista a relevância da espécie como animal experimental, destaca-se a importância de trabalhos a respeito da córnea dos coelhos.

Land use cover change (LUCC) is a major driver of global environmental and socio-economic transformations, with implications for carbon emissions, biodiversity, and sustainable development. However, robust historical analyses have often been limited by a lack of high-quality, spatially detailed baseline data. This study addresses this gap by being the first to apply deep learning-based image segmentation techniques to extract a comprehensive set of land use cover (LUC) information from historical topographic maps at a fine spatial resolution and at a cross-country scale. Specifically, we utilized topographic maps (1:50000) created by the U.S. Army Map Service during the Vietnam War (1963–1973) to create detailed historical LUC maps of Laos and Vietnam. We compared multiple model architectures on the manually labeled training data, with the UNet++ achieving the best performance. The resulting maps, produced at 4 m and 30 m resolutions, include 10 LUC classes and achieved high overall accuracies of 98.8% for Laos and 98.6% for Vietnam on separate test sets. Analysis of the maps revealed forest cover losses of 18.2% in Laos and 25.0% in southern Vietnam (below 17° N) by 1990 and a 36.8% reduction of mangrove forests in Vietnam by 1996. Transitions from forest to shrubland dominated in northern parts of Laos and Vietnam while transitions to cropland were most prevalent in Savannakhet province of Laos and the Southeast region of Vietnam. The maps provide a novel baseline for assessing post-war LUCC dynamics in Southeast Asia allowing for spatially explicit analyses of conflict and policy impacts at the time. Furthermore, the study demonstrates a transferable methodology that makes archival map collections accessible for large-scale historical global change research.

We produced the first spatially explicit, cross-border, digital map of long-term (160 years) land use in the Carpathian Ecoregion, the Hungarian part of the Pannonian plains and the historical region of Moravia in the Czech Republic. We mapped land use in a regular 2 × 2 km point grid. Our dataset comprises of 91,310 points covering 365,240 km² in seven countries (Czechia, Slovakia, Austria, Hungary, Poland, Ukraine and Romania). We digitized three time layers: (1) for the Habsburg period, we used maps of the second Habsburg military survey from years 1819–1873 at the scale 1:28,800 and the Szatmari's maps from years 1855–1858 at scale 1:57,600; (2) The World Wars period was covered by national topographic maps from years 1915–1945 and scales here ranged between 1:20,000–1:100,000; and (3) the Socialist period was mapped from national topographic maps for the years 1950–1983 at scales between 1:25,000–1:50,000. We collected metadata about the years of mapping and map sources. We used a hierarchical legend for our maps, so that the land use classification for the entire region consisted of 9 categories at the most general level and of 22 categories depending on the period and a country.

Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision based on the fusion of probabilities. Individually, the classifier based on PI achieved 93.1% accuracy, whereas the deep classifiers reached classification accuracies over 90% only in isolated cases. Overall, the average accuracy of the deep networks over the four corneal maps ranged from 86% (SfN) to 89.9% (AN). The classifier ensemble increased the accuracy of the deep classifiers based on corneal maps to values ranging (92.2% to 93.1%) for SqN and (93.1% to 94.8%) for AN. Including in the ensemble-specific combinations of corneal maps’ classifiers and PI increased the accuracy to 98.3%. Moreover, visualization of first learner filters in the networks and Grad-CAMs confirmed that the networks had learned relevant clinical features. This study shows the potential of creating ensembles of deep classifiers fine-tuned with a transfer learning strategy as it resulted in an improved accuracy while showing learnable filters and Grad-CAMs that agree with clinical knowledge. This is a step further towards the potential clinical deployment of an improved computer-assisted diagnosis system for KCN detection to help ophthalmologists to confirm the clinical decision and to perform fast and accurate KCN treatment.

Point symbols on a scanned topographic map (STM) provide crucial geographic information. However, point symbol recognition entails high complexity and uncertainty owing to the stickiness of map elements and singularity of symbol structures. Therefore, extracting point symbols from STMs is challenging. Currently, point symbol recognition is performed primarily through pattern recognition methods that have low accuracy and efficiency. To address this problem, we investigated the potential of a deep learning-based method for point symbol recognition and proposed a deep convolutional neural network (DCNN)-based model for this task. We created point symbol datasets from different sources for training and prediction models. Within this framework, atrous spatial pyramid pooling (ASPP) was adopted to handle the recognition difficulty owing to the differences between point symbols and natural objects. To increase the positioning accuracy, the k-means++ clustering method was used to generate anchor boxes that were more suitable for our point symbol datasets. Additionally, to improve the generalization ability of the model, we designed two data augmentation methods to adapt to symbol recognition. Experiments demonstrated that the deep learning method considerably improved the recognition accuracy and efficiency compared with classical algorithms. The introduction of ASPP in the object detection algorithm resulted in higher mean average precision and intersection over union values, indicating a higher recognition accuracy. It is also demonstrated that data augmentation methods can alleviate the cross-domain problem and improve the rotation robustness. This study contributes to the development of algorithms and the evaluation of geographic elements extracted from STMs.

The demand for large-scale topographic maps in Indonesia has significantly increased due to the implementation of several government initiatives that necessitate the utilization of spatial data in development planning. Currently, the national production capacity for large-scale topographic maps in Indonesia is 13,000 km2/year using stereo-plotting/mono-plotting methods from photogrammetric data, Lidar, high-resolution satellite imagery, or a combination of the three. In order to provide the necessary data to the respective applications in a timely manner, one strategy is to only generate critical layers of the maps. One of the topographic map layers that is often needed is land cover. This research focuses on providing land cover to support the accelerated provision of topographic maps. The data used are very-high-resolution satellite images. The method used is a deep learning approach to classify very-high-resolution satellite images into land cover data. The implementation of the deep learning approach can advance the production of topographic maps, particularly in the provision of land cover data. This significantly enhances the efficiency and effectiveness of producing large-scale topographic maps, hence increasing productivity. The quality assessment of this study demonstrates that the AI-assisted method is capable of accurately classifying land cover data from very-high-resolution images, as indicated by the Kappa values of 0.81 and overall accuracy of 86%, respectively.

Purpose. To develop a method for generating accurate large-scale topographic maps in densely vegetated areas using UAV (Unmanned Aerial Vehicle) and GNSS (Global Navigation Satellite System) technologies. The focus is on correcting elevation data from UAV imagery through a polynomial model based on CORS checkpoints. Methodology. The research was conducted in Mai Pha commune, Lang Son province, Vietnam. A DJI Phantom 3 Pro UAV was used to capture aerial images, and GNSS-RTK (CORS) technology was employed to collect ground control points. A polynomial surface fitting method (1st to 3rd degree) was applied to model vegetation thickness and correct the digital surface model (DSM) to obtain a digital elevation model (DEM). The DSM was smoothed using various radii (0; 20; 50; 70; 100 m), and the accuracy was assessed using different numbers of checkpoints (150; 350; 1,000). A software module was developed to automate the correction process. Findings. The accuracy of the DEM improved with an increased number of checkpoints and an appropriate smoothing radius. The best results were achieved with 1,000 checkpoints and a smoothing radius of 50 m. The corrected DEM achieved elevation accuracy within 1–2 meters, meeting regulatory standards for large-scale topographic maps (1:2,000–1:5,000). The method proved effective even in areas with dense vegetation, where traditional mapping methods face limitations. Originality. This study introduces a novel approach that integrates UAV photogrammetry with GNSS-RTK data and polynomial surface modeling to correct elevation data in vegetated areas. The developed software module automates the correction process, enhancing efficiency and consistency. Practical value. The proposed method enables the creation of accurate, large-scale digital topographic maps in challenging environments with dense vegetation. It offers a cost-effective and efficient alternative to traditional surveying methods, with potential applications in forestry, land management, and infrastructure planning.

Topographic maps provide detailed information on variations in ground elevation, which is essential for precision farmland levelling. This paper reports the development and experimental study on an innovative approach of generating topographic maps at farmland-level with the advantages of high efficiency and simplicity of implementation. The experiment uses a low-altitude Unmanned Aerial Vehicle (UAV) as a platform and integrates Light Detection and Ranging (LiDAR) distance measurements with Post-Processing Kinematic Global Positioning System (PPK-GNSS) coordinates. A topographic mapping experiment was conducted over two fields in Henan Province, China, and primitive errors of the topographic surveying data were evaluated. The Root Mean Square Error (RMSE) between elevation data of the UAV-LiDAR topographic mapping system and ground truth data was calculated as 4.1 cm and 3.6 cm for Field 1 and Field 2, respectively, which proved the feasibility and high accuracy of the topographic mapping system. Furthermore, the accuracies of topographic maps generated using different geo-spatial interpolation models were also evaluated. The results showed that a TIN (Triangulated Irregular Network) interpolation model expressed the best performances for both Field 1 with sparse topographic surveying points, and Field 2 with relatively dense topographic surveying points, when compared with other interpolation models. Moreover, we concluded that as the spatial resolution of topographic surveying points is intensified from 5 m × 0.5 m to 2.5 m × 0.5 m, the accuracy of the topographic map based on the TIN model improves drastically from 7.7 cm to 4.6 cm. Cut-fill analysis was also implemented based on the topographic maps of the TIN interpolation model. The result indicated that the UAV-LiDAR topographic mapping system could be successfully used to generate topographic maps with high accuracy, which could provide instructive information for precision farmland levelling.

Topographic map-reading skills are critical for certain professions but can be difficult to learn. The purpose of this pilot study is to provide insight on the role augmented reality technology can play in the development of topographic map-reading skills. Using a situated cognition theoretical framework, this study tracks the development of students’ skills in three different instructional approaches using the Topographic Mapping Assessment (TMA), instructor observations, and student feedback. Using a quasi-experimental research design, 85 college-level students in eight sections of an introductory undergraduate geoscience laboratory course were assigned to a control group ( n  = 19) that was instructed using the standard curriculum (paper-and-pencil lab exercises and field trips), a 2-D group ( n  = 14) that completed six activities using 2-D maps, or an augmented reality sandbox (ARS) group ( n  = 52) that completed six activities requiring both 2-D maps and augmented reality technology. Results from multi-level analyses of covariance suggest no significant difference in overall post-instruction scores, except female students in the ARS groups ( n  = 17) tended to score higher than students in the control group ( n  = 11), potentially indicating this method can increase outcomes for females in STEM. Other identified instructional benefits of using the ARS include increased collaboration between students, greater visibility to the instructor of student difficulties and challenges, and improved ability for the instructor to provide real-time feedback and guidance.