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Localization based on single-line lidar is widely used in various robotics applications, such as warehousing, service, transit, and construction, due to its high accuracy, cost-effectiveness, and minimal computational requirements. However, challenges such as LiDAR degeneration and frequent map changes persist in hindering its broader adoption. To address these challenges, we introduce the Contribution Sampling and Map-Updating Localization (CSMUL) algorithm, which incorporates weighted contribution sampling and dynamic map-updating methods for robustness enhancement. The weighted contribution sampling method assigns weights to each map point based on the constraints within degenerate environments, significantly improving localization robustness under such conditions. Concurrently, the algorithm detects and updates anomalies in the map in real time, addressing issues related to localization drift and failure when the map changes. The experimental results from real-world deployments demonstrate that our CSMUL algorithm achieves enhanced robustness and superior accuracy in both degenerate scenarios and dynamic map conditions. Additionally, it facilitates real-time map adjustments and ensures continuous positioning, catering to the needs of dynamic environments.

An analysis of the soil cover of Russia as presented on the soil map on a scale of 1 : 2.5 M with the use of a new substantive-genetic soil classification system has been performed at the level of soil orders. The high level of classification-based generalization makes it possible to assess the most general patterns of soil geography and soil resources and to identify changes that have occurred as a result of renaming of each polygon on the map with the use of the new classification. The areas occupied by soil orders have been calculated. In total, there are 24 soil orders on the new map, including 21 orders of natural soils and 3 orders (agrozems, turfzems, stratozems) of anthropogenically transformed soils. Soils of the orders of agro-abrazems, chernozems, and turbozems are not presented on the map. As on most small-scale soil maps of Russia, the zonal regularities of the soil cover in the East European Plain and high lithogenic mosaicity in Central and Eastern Siberia are clearly seen. The new map includes soil orders that were absent on the initial map: cryozems, cryometamorphic and hydrometamorphic soils, lithozems, cryoabrazems, cryoturbozems, urbostratozems, and organo-accumulative soils. Soils characteristic of humid conditions predominate: Al-Fe-humus soils (Podzols) (319.2 M ha, or 19% of the land fund of Russia), gley soils (Gleysols) (223.9 M ha, 13%), texture-differentiated soils (Luvisols and Regosols) (190.8 M ha, 11%), and peat soils (Histosols) (143.5 M ha, 8%) and occupy more than a half of the territory of Russia. The area of humus-accumulative soils most suitable for arable use is 103.6 M ha (6%). Considerable areas are occupied by soils of the orders of cryozems (Turbic Cryosols) (111.4 M ha), iron-metamorphic soils (Chromic Cambisols) (92.7 M ha), structure-metamorphic soils (Cambisols) (47.3 M ha), pale-metamorphic soils (Cambic Cryosols) (12.8 M ha), hydrometamorphic soils (Calcic Gleysols) (4.3 M ha), and cryometamorphic soils (Cambisols Gelic) (3.4 M ha), which corresponds to the vast continental territory of Russia with balanced moisture conditions. Separate place belongs to the soils with strict limitations for use (lithosols (Leptosols), weakly developed soils (Regosols, Nudilithic Leptosols)) but playing important biospheric functions and requiring special protection.

High-definition digital map is the foundation of autonomous driving technology, which provides precise location information for autonomous vehicles. However, with the change of environment and the passage of time, the fidelity performance of unstructured road map will gradually decline. Therefore, this paper proposes a digital map updating method of open-pit mine based on vehicle-cloud collaboration to effectively improve the efficiency of map updating. Firstly, a three-layer map model structure is constructed to ensure the integrity of map updating of open-pit mine. Then, a track-based map updating method is proposed for many scenarios of open-pit mines. Meanwhile, a map updating framework based on vehicle-cloud collaboration is designed to improve the efficiency of automatic driving map updating in open-pit mines. Experimental results show that this method can effectively reduce the map updating delay. Compared with the single-vehicle mode, the average map updating delay is reduced by 75%. At the same time, the relative error of map updating is reduced, and the overall accuracy is increased by 10%, which meets the requirements of map fidelity.

ABSTRACTThe earth surface changes continuously due to the natural causes and ‎human activities. New generations of satellite sensors, such as WorldView and ‎GeoEye, provide new data to better delineate, track and visualise changes in land ‎cover. A number of classes are used in the satellite images. All artefacts with ‎elevations greater than the ground surface (buildings in particular) may appear in ‎a wrong location. The correction of buildings position is an important task for ‎mapping applications. The main aim of this study is to introduce a change ‎detection approach using very high resolution satellite images (VHR) for map ‎updating. In this approach, an approximated method for building relief ‎displacement correction was developed.‎In this paper, image preprocessing was carried out and information content ‎of the satellite image was evaluated. Then change detection between GeoEye-1 ‎image and Sohag map was carried out using post-classification comparison ‎technique. After that the change map result was divided into two classes: building ‎and non-building. All objects were transformed from raster to vector format. For ‎building objects, the height was estimated. A python code was written to calculate ‎relief displacement using buildings height and shadow length. The vector layer ‎was added to update the reference map. The results showed the ability of very ‎high-resolution satellites images for updating large scale maps in Egypt. Also, the ‎approximated method for building relief displacement correction is a promising ‎method. It has RMSE accuracy of 0.95m.‎

Urban land cover classification using Very High Resolution (VHR) satellite images is a very important source of information for map updating. Egyptian environment has more challenges in feature extraction. The main problem lies in the spectral similarity between different land cover classes. Also, great diversity in sizes, shapes, and materials of each class. The main aim of this work is to represent a new automatic indices-based classification method for map updating using VHR satellite images. The method uses a set of spectral indices with their thresholds in consecutive order, chosen based on WorldView-2 (WV-2) bands, to classify land cover in the Egyptian environment. For this study, WV-2 satellite images with eight spectral bands were used. The proposed method is compared with five traditional classification methods; Minimum distance, Spectral angle mapper, Mahalanobis distance, Spectral correlation mapper, and Maximum likelihood method, which included in ERDAS 2015 software, for validation purpose and checking its stability. The results show that the extracted features with the proposed method can contribute significantly to update Egyptian medium scale maps. The average overall accuracy achieved with the proposed approach (75.31%) is higher than those obtained using Minimum distance (54.0%), Spectral angle mapper (69.50%), and Mahalanobis distance (73.63%). Also, it is near to those obtained by the Spectral correlation mapper (76.50%), and Maximum likelihood method (78.25%).

Robust building detection from satellite images has been a subject of interest for several decades. Very High Resolution (VHR) satellite images support the efficient extraction of manmade objects. The main aim of this paper is to present an approach for building extraction from VHR satellite images for map updating in Egypt. To achieve this aim, a comparison of pixel and object-based classification techniques has been applied. Then, different refinement processes based on shadow, context, shape, and Digital Surface Model (DSM) data are carried out. Two study areas from the VHR satellite images for Assuit and Sohag cities are used. A comparison of the classification techniques shows that the Maximum Likelihood Classifier (MLC) for pixel-based technique and Support Vector Machine (SVM) for object-based technique give the highest overall accuracy results. Refinement based on shadow, context, shape, and DSM information improves the overall accuracy with an average of 18%. Thus, the building extraction results can contribute significantly to update maps in Egypt.

This paper illustrates a land consumption map for Italy (year 2017) at a scale 1:1,300,000, and the assessment of its changes (2012–2017). We define land consumption as the replacement of a non-artificial land cover to an artificial land cover, both permanent and no-permanent. The maps are a 10 m spatial resolution raster, produced by photointerpretation of very high resolution images and semiautomatic classification of high resolution remote sensing images. An overall accuracy of 97.7% for the map of 2012 and of 99.66% for the map of 2017 was obtained. The results suggest that the method proposed is appropriate to detect land consumption, both for the urban densification and for the sprawling phenomena, from national to local level. Furthermore, because of the high spatial resolution and the classification scheme adopted, it is suitable for an effective monitoring system, compared to other existing classification systems or monitoring programs.

Urban areas are changing rapidly. In order to document the urban realities in topographic databases and geographic information systems efficient methods are required. Vector data of buildings are of special importance. A methodology for an automated generation of cartographically enhanced data is presented and applied to two test sites at Vaihingen, Germany. The steps of the workflow are described in detail. The examples use imagery of a large-format aerial camera to map different types of buildings. First, land cover maps are generated by means of supervised classification using two sets of attributes (basic attributes and attribute profile, basic attributes and dispersion). After the enhancement of the extracted buildings their outlines have straight, orthogonal, and parallel line segments created by least squares adjustment. The assessment of the geometric accuracy used 264 well-defined building corners and two types of references (land cover map, ortho-image). The obtained average standard deviation of the coordinates was σx,y = 1.0 m. The additional use of an attribute profile did not improve upon the geometric accuracy that was obtained by means of five attributes (height above ground, normalized difference vegetation index, standard deviation of the elevations in the 5 × 5 pixels window, intensity value of the near-infrared band, and standard deviation of intensities in the 5 × 5 surrounding at a pixel of the near-infrared band). The experiences with the developed software reveal that a graphical output of intermediate results is helpful to obtain complete and reliable results at complex building structures.

Spatial data of cartographic bases are often used for nautical and aeronautical charts, reference maps, or other thematic maps. Traditionally, such spatial data sets of different scales are created apart from each other, are stored in different databases, and, in many cases, have no relations. This makes the update of the spatial data a complex and costly task. Meanwhile, automatic detection of changes in spatial data, when older versions of data are compared to more recent version of data, saves time in this process. To model such a process, two major tasks are solved, namely, detection of a change in spatial data and identification of its significance to answer whether the change detected is significant enough that an update of the data will be effective. The model also takes into account the requirements for spatial data, the scale of the data, and update methods.