Tile Map Research Articles

An Improved Generative Adversarial Network for Generating Multi-Scale Electronic Map Tiles Considering Cartographic Requirements

An Improved Generative Adversarial Network for Generating Multi-Scale Electronic Map Tiles Considering Cartographic Requirements

A novel global multi-level terrain data tile production method

Abstract. Terrain services have increasingly wide applications in urban planning, 3D gaming animation, and other fields. Map websites typically provide terrain services to users in the form of terrain shaded relief tiles. Map tile technology offers a new solution for large-scale concurrent user access. Although terrain shaded relief tiles can effectively represent changes in terrain relief, users cannot adjust the visualized raster images for visualization schemes, making data analysis and applications more challenging. The emergence of Terrain-RGB technology has effectively addressed this limitation. However, currently, internet maps mostly provide multi-level map services to users in the form of web map tile service (WMTS). Terrain-RGB images produced by simple encoding tools may exhibit grid lines when rendered on tiles with a small grid spacing, affecting the usability of Terrain-RGB products. Therefore, this paper proposes a technical approach for producing global multi-level Terrain-RGB products using digital elevation model (DEM), focusing on addressing the issues of gridlines and splicing traces from different scenes DEM data, offering a new solution for map websites to release analyzable terrain services.

An Approach to Checking Map Tiles by Annotation Recognition

Abstract. As the capabilities of geographic information services increase, web geographic information services represented by internet mapping have become increasingly popular, widely used in various location-based services. It is precisely because of the surge in demand that higher requirements have been put forward for the updating and quality control of internet map data. The quality inspection of geographic vector data is relatively well-developed, with many inspection rules and automated processes, but there is a clear deficiency in the quality inspection of map tiles, which mainly relies on manual visual inspection, restricting the rapid updating and release of map tiles. To solve this problem, this paper proposes an automated map tile inspection method. First, web crawling technology is used to capture map tiles, and to avoid the problem of incomplete display of annotations across tiles, adjacent tiles are all captured and merged into one image; second, OCR technology is used for image text detection and text recognition, and finally, the newly added recognition results are stored in the database and compared and confirmed with the results in the database. The experiment proves that the method proposed in this paper is feasible and may greatly improve work efficiency, especially as the updating speed of online maps accelerates, an automated tile quality inspection method can greatly improve the updating speed.

Transforming satellite imagery into vector maps using modified GANs

Vector maps find widespread utility across diverse domains due to their capacity to not only store but also represent discrete data boundaries such as building footprints, disaster impact analysis, digitization, urban planning, location points, transport links, and more. Although extensive research exists on identifying building footprints and road types from satellite imagery, the generation of vector maps from such imagery remains an area with limited exploration. Furthermore, conventional map generation techniques rely on labor-intensive manual feature extraction or rule-based approaches, which impose inherent limitations. To surmount these limitations, we propose a novel method called HPix, which utilizes modified Generative Adversarial Networks (GANs) to generate vector tile map from satellite images. HPix incorporates two hierarchical frameworks: one operating at the global level and the other at the local level, resulting in a comprehensive model. Through empirical evaluations, our proposed approach showcases its effectiveness in producing highly accurate and visually captivating vector tile maps derived from satellite images. We achieved a pixel-level accuracy of 61.04% and an SSIM score of 0.75, outperforming all other existing methods. We further extend our study’s application to include mapping of road intersections and building footprints cluster based on their area. We also show usability of our proposed architecture as a general-purpose solutions in other tasks like edges-to-photo, BW-to-color, or labels-to-street scene. GitHub:https://github.com/aditya-taparia/Satellite-Image-to-Vector-Map.

Visual place recognition for aerial imagery: A survey

Aerial imagery and its direct application to visual localization is an essential problem for many Robotics and Computer Vision tasks. While Global Navigation Satellite Systems (GNSS) are the standard default solution for solving the aerial localization problem, it is subject to a number of limitations, such as, signal instability or solution unreliability that make this option not so desirable. Consequently, visual geolocalization is emerging as a viable alternative. However, adapting Visual Place Recognition (VPR) task to aerial imagery presents significant challenges, including weather variations and repetitive patterns. Current VPR reviews largely neglect the specific context of aerial data. This paper introduces a methodology tailored for evaluating VPR techniques specifically in the domain of aerial imagery, providing a comprehensive assessment of various methods and their performance. However, we not only compare various VPR methods, but also demonstrate the importance of selecting appropriate zoom and overlap levels when constructing map tiles to achieve maximum efficiency of VPR algorithms in the case of aerial imagery. The code is available on our GitHub repository — https://github.com/prime-slam/aero-vloc.

Method of Forming and Algorithm of Saving a Photo Map of the Seafloor during its Photography by an Unmanned Underwater Vehicle

The paper proposes a new method and algorithm for creating a photo map of the seabed from photographs obtained from on-board photo and video systems of autonomous uninhabited underwater vehicles during fulfillment of their various missions. This photo map is formed from photographs of the seabed by sequentially overlapping them. At the same time, data on the position of the underwater vehicle received from the on-board navigation system at the time of receipt of each photograph is used to position each photo image on the generated photo map. And to rotate and scale these photographic images, data on the orientation of the underwater vehicle in the global coordinate system and its height above the bottom, also obtained from the on-board navigation system, are used. To provide quick access to the photo map, it is proposed to store it on an on-board data storage device in the form of a tile map, widely used when creating interactive geographical maps, for example, Google Maps. However, the formation of a tile map is usually carried out on the basis of all the already received photographic images, as is done with satellite images, which cannot be applied on board underwater vehicles when data from the photo-video system is received sequentially. Therefore, a special algorithm has been developed and implemented that generates a tile map during the movement of the underwater vehicle and updates only the area of the tile map in which the photo is currently being taken. This minimizes the computational load and ensures the formation of a photo map of the bottom using standard on-board computing devices. The conducted semi-natural experiments with data obtained as a result of performing real missions using the MMT-3000 underwater vehicle confirmed the high efficiency of using the developed method and algorithm.

Methodology of Mosaicking and Georeferencing for Multi-Sheet Early Maps with Irregular Cuts Using the Example of the Topographic Chart of the Kingdom of Poland

The Topographic Chart of the Kingdom of Poland (pol. Topograficzna Karta Królestwa Polskiego, commonly referred to as ‘the Quartermaster’s Map’, hereinafter: TKKP) is the first Polish modern topographic map of Poland (1:126,000, 1843). Cartographic historians acclaim its conception by the General Quartermaster of the Polish Army, noting its editorial principles and technical execution as exemplars of the early 19th-century cartographic standards. Today, it stands as a national heritage relic, furnishing invaluable insights into the former Polish Kingdom’s topography. Although extensively utilised in geographical and historical inquiries, the TKKP has yet to undergo a comprehensive geomatic investigation and publication as spatial data services. Primarily, this delay stems from the challenges of mosaicking and georeferencing its 60 constituent sheets, owing to the uncertain mathematical framework and irregular sheet cuts. In 2023, the authors embarked on rectifying this by creating a unified TKKP mosaic and georeferencing the map to contemporary reference data benchmarks. This endeavour involved scrutinising the map’s mathematical accuracy and verifying prior findings. The resultant product is accessible via the ‘Maps with the Past’ platform, developed by the Institute of History of the Polish Academy of Sciences The dissemination of raster data services adhering to OGC standards such as WMTS (Web Map Tile Service), ECW (Enhanced Compression Wavelet), and COG (Cloud Optimized GeoTIFF) facilitates the swift and seamless integration of the generated data into web and GIS tools. The digital edition of the TKKP emerges as a pivotal resource for investigations spanning natural and anthropogenic environmental transformations, sustainable development, and cultural heritage studies.

Generating grid maps via the snake model

Generating grid maps via the snake model

PEMBUATAN BUSANA PESTA MALAM DENGAN MAPPING TILE BORDIR PADA BAGIAN EKOR ROK

The aim of this study was to find out the process of making evening party dresses with embroidered tile mapping on the tail of the skirt and find out the results of making evening party clothes with embroidered tile mapping on the tail of the skirt. The method used in this study is the creation of work which consists of four stages. First, the pre-design stage. Second, the design stage. Third, the embodiment stage. Fourth, the presentation stage. The results of this study are: The process of making evening party dresses with mapping on the tail of the skirt starts from making a party dress design concept and making sketches. The process of applying mapping to party attire starts with cutting the embroidered tiles used for mapping according to the design. Then the embroidered tile pieces are arranged and placed on the tail of the party dress skirt. The finished result of making an evening party dress with embroidered tile mapping on the tail of the skirt is in accordance with the design concept. The selection of material characteristics using maroon duchess cloth and the application of embroidered tile mapping on the tail of the party dress skirt makes the dress look luxurious.

Quadtree decomposition-based Deep learning method for multiscale coastline extraction with high-resolution remote sensing imagery

As one of the most critical features on the earth's surface, coastal zone mandates high-quality extraction of its representative feature, the coastline. Prior methodologies primarily emphasize on edge and small-scale information. However, during large-scale image processing, misclassification might occur due to the difficulty in determining whether a local area belongs to the land or sea. To address this, we propose a deep learning-based multiscale coastline extraction algorithm in this study. It comprises a multiscale coastal zone dataset built upon a tile map service structure and a scene classification-based multiscale coastal zone classifier, employing quadtree decomposition to identify coastal zones from low to high levels. Contrasting with conventional semantic segmentation, the scene classification network, owing to its larger receptive field, can accurately discern land and sea. This accuracy is further enhanced by using quadtree decomposition to process images with lower resolution and larger coverage. The results suggest that our proposed method effectively eliminates confusing features, with the overall experimental classification accuracy attesting to the effectiveness of our approach, yielding a 6% improvement. Moreover, the screening process in this study significantly reduces the number of input samples for the segmentation network, thus boosting computational speed.

The Five-Dollar Model: Generating Game Maps and Sprites from Sentence Embeddings

The five-dollar model is a lightweight text-to-image generative architecture that generates low dimensional images or tile maps from an encoded text prompt. This model can successfully generate accurate and aesthetically pleasing content in low dimensional domains, with limited amounts of training data. Despite the small size of both the model and datasets, the generated images or maps are still able to maintain the encoded semantic meaning of the textual prompt. We apply this model to three small datasets: pixel art video game maps, video game sprite images, and down-scaled emoji images and apply novel augmentation strategies to improve the performance of our model on these limited datasets. We evaluate our models' performance using cosine similarity score between text-image pairs generated by the CLIP VIT-B/32 model to demonstrate quality generation.

Map vector tile construction for arable land spatial connectivity analysis based on the Hadoop cloud platform

The concentrated connection of arable land is one of the important indicators reflecting the quality of cultivated land, and large-scale arable land blocks are more conducive to agricultural mechanization operation, thereby improving the land use efficiency. However, the calculation of farmland connectivity is essentially a large-scale calculation of spatial vector data, especially for the national or global farmland patch data. This article proposes a framework for calculating farmland connectivity based on spatial vector map tiles and parallelizes the algorithm based on the Hadoop cloud platform. The framework is based on the tile pyramid model and uses the Douglas–Peucker algorithm to simplify the data to meet the needs of rapid display of large-scale data under multi-scale. The consistency and integrity of the front display of vector tiles are ensured using the setting tile buffer. Meanwhile, the parallelization of the vector tile construction algorithm is realized based on the MapReduce programming mode. Finally, the effectiveness and usability of this framework were verified through the calculation of patch connectivity on the tillage map. Experiments show that the algorithm can not only meet the rapid construction requirements of large-scale vector tile data but also support the cultivated land spatial connectivity analysis and greatly improve the efficiency of supporting data calculation.

A Hexagon Tile Map Algorithm for Displaying Spatial Data

A Hexagon Tile Map Algorithm for Displaying Spatial Data

A Super-pixel Scaling Approach to Detect Inconsistency of Cross-layer Tile Maps

A Super-pixel Scaling Approach to Detect Inconsistency of Cross-layer Tile Maps

A first look at CRISM hyperspectral mapping mosaicked data: Results from Mawrth Vallis

The CRISM instrument on MRO collected visible-infrared hyperspectral mapping data (HSP; 180 m/pixel, 262 spectral channels covering 365 to 3937 nm) that covered ∼39% of Mars. Here we present results from a new processing pipeline for these data that produces 5° x 5° hyperspectral mapping tiled mosaics that overlap the coverage of recently released multispectral map tile products (version 4 Multispectral Reduced Data Records, MRDRs; 180 m/pixel, 72 spectral channels). These data enable regional investigations into compositional variations that require high spectral resolution or wavelengths not included in MRDRs. In addition to standard processing techniques available in the CRISM Analysis Toolkit (CAT), the pipeline includes a new correction for systematic discrepancies in radiometric calibration between CRISM observing modes, an improved filtering algorithm to remediate noise, and a technique to correct for differences in radiometry among data strips that arise from differences in photometric and atmospheric conditions. Demonstration hyperspectral mapping tiles covering the Mawrth Vallis region were developed and compared with MRDRs and high spatial resolution hyperspectral targeted observations. The new processing pipeline shows an improvement in data quality over standard processing using CAT utilities. Compared to targeted observations (18 or 36 m/pixel, 545 spectral channels), hyperspectral mapping tile mosaics reveal compositional information across a much greater spatial extent at the expense of 5–10 times coarser spatial resolution. In addition, the hyperspectral mapping tile mosaics reveal greater compositional detail in both the spatial and spectral dimensions compared to MRDRs, but with sparser spatial coverage. Sample hyperspectral mapping tile mosaics over the Mawrth Vallis region reveal hydrated silica, Al-bearing smectite, Fe/Mg-bearing smectite, and mixed clays, consistent with compositions previously reported in the literature.

COMTILES: A CASE STUDY OF A CLOUD OPTIMIZED TILE ARCHIVE FORMAT FOR DEPLOYING PLANET-SCALE TILSETS IN THE CLOUD

Abstract. The container formats commonly used for managing map tiles, such as MBTiles and GeoPackage, were originally designed with only POSIX filesystem access in mind. This makes these file formats inefficient to use in a cloud native environment, especially in combination with large tilesets. The Cloud Optimized GeoTIFF format solves the problem of providing large satellite data in the cloud, creating a new category of so-called cloud optimized data formats. This type of format allows geospatial data to be deployed as a single file on a cheap and scalable cloud object storage such as AWS S3 and accessed directly from a browser without the need for a dedicated backend. Based on the concepts of the COG format, this contribution proposes a new cloud optimized file format called COMTiles, specially designed for planet-scale tilesets. This format has the potential to simplify the deployment workflow of large tilesets in a cloud-native environment, while simultaneously reducing the hosting costs. In comparison to PMTiles, another cloud-optimized tile archive solution, COMTiles can reduce the number of transferred data and the performance of decoding portions of the file. COMTiles also adds support for different coordinate systems.

Inconsistency Detection in Cross-Layer Tile Maps with Super-Pixel Segmentation

The consistency of geospatial data is of great significance for the application and updating of geographic information in web maps. Due to the multiple data sources and different temporal versions, the tile web maps usually meet the inconsistency question across different layers. This study tries to develop a method to detect this kind of inconsistency utilizing a raster-based scaling approach. Compared with vector-based handling, this method can be directly available for multi-level tile images in a pixel representation form. The proposed cross-layer raster tile map rendering method (CRTMRM) consists of four primary aspects: geographic object separation, consistency rendering rules, data scaling and derivation with super-pixel segmentation, and inconsistency detection. The scale transformation strategy with the super-pixel attempts to obtain a simplified representation. Taking the scale lifespan variation and geometric consistency rules into account, the inconsistency detection of tile maps is conducted between temporal versions, multi-sources, and different scales through actual and derived data overlay analysis. The experiment focuses on features of cross-layer water or vegetation areas with Level 9 to Level 14 in Baidu Maps, Amap, and Google Maps. This method is able to serve as a basis for massive unstructured web map data inconsistency detection and support intelligent web map rendering.

Progressive Dynamic Registration Method for Tile Maps Based on Optimum Multi-Features

Different tile maps may use different coordinate systems, and it is difficult to superimpose maps from different sources. In this regard, we propose a progressive dynamic registration (PDR) method based on optimum features extracted from images of tile map screenshots of a certain scale. Using this method, we can automatically register maps in roughly the same area from different sources without knowing the map project information. The better features among feature points and feature surfaces are selected for image registration based on the richness of the features in the map, and a new matching filter that combines the characteristics of the tile map and the features is proposed. In the progressive registration process during map zooming, the result of the adjacent scales are used as reference values for coarse registration. After experimental verification in different areas, the root mean square error of PDR is below 2.5 in different maps and is better than that of the registration method using feature points only. Moreover, the registration accuracies of remote sensing maps in all areas and vector maps in nonurban areas are better than that of the method based on coordinate system transformation. The calculation results of PDR can register not only the tile maps but also other nontiled vector or remote sensing data.

HexTile: A Hexagonal DGGS-Based Map Tile Algorithm for Visualizing Big Remote Sensing Data in Spark

The advent of the era of big remote sensing data has transformed traditional data management and analysis models, among which visualization analysis has gradually become an effective method, and map tiles for remote sensing data have always played an important role. However, in high-latitude regions, especially in polar regions, the deformation caused by map projection still exists, which lowers the accuracy of global or large-scale visual analysis, as well as the execution efficiency of big data. To solve the above problems, this paper proposes an algorithm called HexTile, which uses a hexagonal discrete global grid system (DGGS) model to effectively avoid problems caused by map projection and ensure global consistency. At the same time, the algorithm was implemented based on the Spark platform, which also has advantages in efficiency. Based on the DGGS model, hierarchical hexagon map tile construction and a visualization algorithm were designed, including hexagonal slicing, merging, and stitching. The above algorithms were parallelized in Spark to improve the big data execution efficiency. Experiments were carried out with Landsat-8, and the results show that the HexTile algorithm can not only guarantee the quality of global data, but also give full play to the advantages of the cluster in terms of efficiency. Additionally, the visualization was conducted with Cesium and OpenLayers to validate the integration and completeness of hexagon tiles. The scheme proposed in this paper could provide a reference for spatiotemporal big data visualization technology.

Automatic Creation of Tiled Maps in Two-Dimensional Top-Down Digital Role Playing Games

Automatic Creation of Tiled Maps in Two-Dimensional Top-Down Digital Role Playing Games