Geographic Information System Vector Research Articles

Multiscale mapping of local climate zones in Tokyo using airborne LiDAR data, GIS vectors, and Sentinel-2 imagery

ABSTRACT Multisource remote sensing and geographic information system (GIS) data have contributed powerfully to the large-scale automated mapping of local climate zones (LCZs). However, the accessibility of high-resolution height data, the applicability of standard thresholds to local contexts, and the dependence of mapping scales have limited LCZ classification studies. In this study, we combined airborne LiDAR data, Sentinel-2 imagery, and GIS vector (buildings and roads) data to develop a multiscale automated LCZ classification scheme in the 23 special wards of Tokyo. Based on the optimized thresholds of seven LCZ properties, GIS-based LCZ mapping was implemented using fuzzy logic classifiers at the block scale and at different grid-cell scales (100 m–1000 m). In addition to assessing accuracy using reference samples, multidate thermal infrared data (Landsat-8 and ASTER data) were used to understand the LCZ-LST (land surface temperature) relationship at multiple scales. The results showed that the overall accuracies of LCZs could be significantly increased by threshold optimization at all scales. Significant differences in LCZs and LSTs among different mapping units were observed. The highest overall accuracy was greater than 80% at the 100-m grid-cell scale. As the size of grid cells increased, the overall accuracy of LCZ classification decreased. For each LCZ, the mean daytime/nighttime LST exhibited more variation by date than by scale. This study provides a promising picture of GIS-based LCZ mapping and LCZ-LST relationships at multiple scales.

AN APPROACH TO UPDATING VECTOR FEATURES BY CLUSTERING ALGORITHM

Abstract. The availability of Geographic Information System (GIS) data has increased in recent years, as well as the need to update its data. One way of updating GIS vector data is by deleting and inserting data to the databases by manually defined unique value. Other one is by deleting and adding data manually. These methods all require manual predefined unique values or manual operations which is not suitable for updating data in a timely and fast way, especially large numbers of data are updated. Therefore, an automated vector data updating method has the potential to significantly increase productivity, particularly as existing GIS vector data application increase in size, the data become outdated more quickly. In this paper, we consider the difference between GIS vector data and other data that GIS vector data have spatial coordinates and topology relationships, propose an approach to updating of vector features based on clustering algorithm. First, the minimum bounding box and its center point of vector features are calculated, and then clusters the center points with DBSCAN to get some clusters. In the end, extract the smallest bounding box of every cluster and update vector features with the boxes. This approach uses smallest bounding box to update features is to reduce database query cost and improves the data update efficiency. Experiments show that the method is effective and feasible.

Registration method for GIS vector road data and SAR image

Road detection of synthetic aperture radar (SAR) image is an important part of SAR image interpretation. Thus, in the influence of sophisticated background and speckle noise, it is hard to directly extract the road information from the SAR image. So a SAR image registration method using existing geographic information system (GIS) road data is proposed to assist road detection for SAR image in this paper. By making full use of road geometry structure information, a road structure support is defined. Then all the positions in the SAR image are searched to find the biggest structure support as the optimum matching centre. The image registration for SAR image and road vector data is finally realised. Experimental results show that the registration accuracy of the proposed method is ∼2 pixels, which can be used for shoreline extraction, target positioning and so on.

LandQv2: A MapReduce-Based System for Processing Arable Land Quality Big Data

Arable land quality (ALQ) data are a foundational resource for national food security. With the rapid development of spatial information technologies, the annual acquisition and update of ALQ data covering the country have become more accurate and faster. ALQ data are mainly vector-based spatial big data in the ESRI (Environmental Systems Research Institute) shapefile format. Although the shapefile is the most common GIS vector data format, unfortunately, the usage of ALQ data is very constrained due to its massive size and the limited capabilities of traditional applications. To tackle the above issues, this paper introduces LandQv2, which is a MapReduce-based parallel processing system for ALQ big data. The core content of LandQv2 is composed of four key technologies including data preprocessing, the distributed R-tree index, the spatial range query, and the map tile pyramid model-based visualization. According to the functions in LandQv2, firstly, ALQ big data are transformed by a MapReduce-based parallel algorithm from the ESRI Shapefile format to the GeoCSV file format in HDFS (Hadoop Distributed File System), and then, the spatial coding-based partition and R-tree index are executed for the spatial range query operation. In addition, the visualization of ALQ big data with a GIS (Geographic Information System) web API (Application Programming Interface) uses the MapReduce program to generate a single image or pyramid tiles for big data display. Finally, a set of experiments running on a live system deployed on a cluster of machines shows the efficiency and scalability of the proposed system. All of these functions supported by LandQv2 are integrated into SpatialHadoop, and it is also able to efficiently support any other distributed spatial big data systems.

Appraisal of Soil Conservation Capacity Using NDVI Model-Based C Factor of RUSLE Model for a Semi Arid Ungauged Watershed: a Case Study

Revised Universal Soil Loss Equation (RUSLE) model and Geographic Information System (GIS) have been used to map and quantify vulnerability of soil erosion in a semi arid ungauged Jhagrabaria watershed of Allahabad district of state Uttar Pradesh, India. The spatial pattern of average annual soil erosion rate was estimated by integrating different thematic layers at GIS platform. GIS vector layers including, rainfall erosivity (Ro), slope length and steepness (LS) factor, soil erodibility (K), conservation practices (P), and cover management factor (C) have been estimated using satellite data and Normalized Difference Vegetation Index (NDVI), respectively. The resultant map of average annual soil erosion shows a maximum soil loss in range of 0.3 to 0.6 t ha−1 year−1 (82.41 km2 and 54.92% of watershed area). While maximum soil loss ranges from > 9.0 t ha−1 year−1 (0.67 km2 and 0.45% of watershed) was reported under hillocks (lower part) of the study area. The work outlines that the vegetation cover arrests the soil erosion in the region. The study provides spatial erosion risk maps of 30 m. These maps will be useful in developing better strategies for land planning and management in the environmentally sensitive small hillocks areas.

Selective Encryption Algorithm Using Hybrid Transform for GIS Vector Map

Nowadays, geographic information system (GIS) is developed and implemented in many areas. A huge volume of vector map data has been accessed unlawfully by hackers, pirates, or unauthorized users. For this reason, we need the methods that help to protect GIS data for storage, multimedia applications, and transmission. In our paper, a selective encryption method is presented based on vertex randomization and hybrid transform in the GIS vector map. In the proposed algorithm, polylines and polygons are focused as the targets for encryption. Objects are classified in each layer, and all coordinates of the significant objects are encrypted by the key sets generated by using chaotic map before changing them in DWT, DFT domain. Experimental results verify the high efficiency visualization by low complexity, high security performance by random processes.

A comparative study of the segmentation of weighted aggregation and multiresolution segmentation

Multiresolution segmentation (MRS) algorithm has been widely used to handle very-high-resolution (VHR) remote sensing images in the past decades. Unfortunately, segmentation quality is limited by the dependency of parameter selection on users’ experience and diverse images. Contrarily, the segmentation by weighted aggregation (SWA) can partly overcome the above limitations and produce an optimal segmentation for maximizing the homogeneity within segments and the heterogeneity across segments. However, SWA is solely tested and justified with digital photos in computer vision field instead of VHR images. This study aims at evaluating SWA performance on VHR imagery. First, multiscale spectral, shape, and texture features are defined to measure homogeneity of image objects for segmentation. Second, SWA is implemented to handle QuickBird, unmanned aerial vehicle (UAV), and GF-1 VHR images and further compared with MRS in eCognition software to demonstrate the applicability of SWA to diverse images in building, vegetation and water, forest stands, farmland, and mountain areas. Third, the results are fully evaluated with quantitative measurements on segmented objects and classification-based accuracy assessment on geographic information system vector data. The results indicate that SWA can produce higher quality segmentations, need fewer parameters and manual interventions, create fewer segmentation levels, incorporate more features, and obtain larger classification accuracy than MRS.

Modeling the Relationship between the Gross Domestic Product and Built-Up Area Using Remote Sensing and GIS Data: A Case Study of Seven Major Cities in Canada

City/regional authorities are responsible for designing and structuring the urban morphology based on the desired land use activities. One of the key concerns regarding urban planning is to establish certain development goals, such as the real gross domestic product (GDP). In Canada, the gross national income (GNI) mainly relies on the mining and manufacturing industries. In order to estimate the impact of city development, this study aims to utilize remote sensing and Geographic Information System (GIS) techniques to assess the relationship between the built-up area and the reported real GDP of seven major cities in Canada. The objectives of the study are: (1) to investigate the use of regression analysis between the built-up area derived from Landsat images and the industrial area extracted from Geographic Information System (GIS) data; and (2) to study the relationship between the built-up area and the socio-economic data (i.e., real GDP, total population and total employment). The experimental data include 42 multi-temporal Landsat TM images and 42 land use GIS vector datasets obtained from year 2005 to 2010 during the summer season (June, July and August) for seven major cities in Canada. The socio-economic data, including the real GDP, the total population and the total employment, are obtained from the Metropolitan Housing Outlook during the same period. Both the Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) were used to determine the built-up areas. Those high built-up values within the industrial areas were acquired for further analysis. Finally, regression analysis was conducted between the real GDP, the total population, and the total employment with respect to the built-up area. Preliminary findings showed a strong linear relationship (R2= 0.82) between the percentage of built-up area and industrial area within the corresponding city. In addition, a strong linear relationship (R2= 0.8) was found between the built-up area and socio-economic data. Therefore, the study justifies the use of remote sensing and GIS data to model the socio-economic data (i.e., real GDP, total population and total employment). The research findings can contribute to the federal/municipal authorities and act as a generic indicator for targeting a specific real GDP with respect to industrial areas.

Selective Encryption Scheme for Vector Map Data using Chaotic Map

With the rapid interest in Geographic Information System (GIS) contents, a large volume of valuable GIS dataset has been distributed illegally by pirates, hackers, or unauthorized users. Therefore the problem focus on how to protect the copyright of GIS vector map data for storage and transmission. But GIS vector map data is very large and current data encryption techniques often encrypt all components of data. That means we have encrypted large amount of data lead to the long encrypting time and high complexity computation. This paper presents the selective encryption scheme using hybrid transform for GIS vector map data protection to store, transmit or distribute to authorized users. In proposed scheme, polylines and polygons in vector map are targets of selective encryption. We select the significant objects in polyline/polygon layer, and then they are encrypted by the key sets generated by using Chaotic map before changing them in DWT, DFT domain. Experimental results verified the proposed algorithm effectively and error in decryption is approximately zero.

Douglas Peucker 근사화 알고리즘과 빈 분류 기반 벡터 맵 데이터 압축

Vector data represents a map by its coordinate and Raster data represents a map by its pixel. Since these data types have very large data size, data compression procedure is a compulsory process. This paper compare the results from three different methodologies; GIS (Geographic Information System) vector map data compression using DP(Douglas-Peucker) Simplification algorithm, vector data compression based on Bin classification and the combination between two previous methods. The results shows that the combination between the two methods have the best performance among the three tested methods. The proposed method can achieve 4-9% compression ratio while the other methods show a lower performance.

Selective Encryption Algorithm Based on DCT for GIS Vector Map

With the rapid interest in Geographic Information System (GIS) contents, a large volume of valuable GIS dataset has been distributed illegally by pirates, hackers, or unauthorized users. Therefore the problem focus on how to protect the copyright of GIS vector map data for storage and transmission. At this point, GIS security techniques focusing on secure network and data encryption have been studied and developed to solve the copyright protection and illegal copy prevention for GIS digital map. But GIS vector map data is very large and current data encryption techniques often encrypt all components of data. That means we have encrypted large amount of data lead to the long encrypting time and high complexity computation. This paper presents a novel selective encryption scheme for GIS vector map data protection to store, transmit or distribute to authorized users using K-means algorithm. The proposed algorithm only encrypts a small part of data based on properties of polylines and polygons in GIS vector map but it can change whole data of GIS vector map. Experimental results verified the proposed algorithm effectively and error in decryption is approximately zero.

A progressive transmission strategy for GIS vector data under the precondition of pixel losslessness

It is well known that the final results of geographic information system (GIS) mapping are composed of rasterized pixels in most display devices. Based on this, pixel equivalence is proposed as two vector data that can produce the same raster image on a device under the same mapping processes and parameters. Moreover, if between the two data, one is considered the original data and the other is its reduced-quantity version, then the latter can be regarded as pixel losslessness relative to the former. In this case, the reduced-quantity version can be used instead of the original data to produce the same result from the perspective of visualization. The aim of this article is to explore how to produce the transmitted data, including an initial code and a series of increments that are used for progressive transmission, to retain pixel losslessness. By illustrating that the vector data can be represented by a vertex or ordered sequence of vertices, this article introduces the detailed concepts of pixel equivalence and pixel losslessness of vector data. Then, the requirements for pixel equivalence between two vertices are deduced by analyzing the coordinate transform processes under specific assumptions. The method to generate an initial code and a series of increments related to a single vertex is proposed to produce vertices that are pixel equivalence to the original vertex under different coordinate transform processes. By discussing the traversing mode of the ordered sequence of vertices based on quadtree cells, a coding algorithm is proposed to develop the progressive transmission related to the multi-vertex and to generate reduced-quantity versions to retain pixel losslessness. Finally, experiments are designed to demonstrate that, under the precondition of pixel losslessness, the proposed progressive transmission method can significantly reduce the amount of transmitted data in the network environment.

Progressive vector compression for high-accuracy vector map data

With the increase in the number of applications using digital vector maps and the development of surveying techniques, a large volume of GIS (geographic information system) vector maps having high accuracy and precision is being produced. However, to achieve their effective transmission while preserving their high positional quality, these large amounts of vector map data need to be compressed. This paper presents a compression method based on a bin space partitioning data structure, which preserves a high-level accuracy and exact precision of spatial data. To achieve this, the proposed method a priori divides a map into rectangular local regions and classifies the bits of each object in the local regions to three types of bins, defined as category bin (CB), direction bin (DB), and accuracy bin (AB). Then, it encodes objects progressively using the properties of the classified bins, such as adjacency and orientation, to obtain the optimum compression ratio. Experimental results verify that our method can encode vector map data constituting less than 20% of the original map data at a 1-cm accuracy degree and that constituting less than 9% at a 1-m accuracy degree. In addition, its compression efficiency is greater than that of previous methods, whereas its complexity is lower for close to real-time applications.

Disguise and Reduction Methods of GIS Vector Data Based on Difference Expansion Principle

Information disguising is one of the important means to solve the problem of information security. However, the research aiming at vector data hasn’t got the well-deserved attention. The technology of difference expansion has the characteristics of non-destructive information disguise and reduction, this paper based on these characteristics presents a method of information disguise and reduction for GIS (Geographic Information System) vector data. For the illegal user this method can be realized the sensitive information hiding and restricting the use, but for the legitimate user who has key file it can restore data without loss. During in the vector data remote service, this method can effectively solve the secure transmission and disguising decryption-data etc.

Image Watermark Similarity Calculation of GIS Vector Data

In recent years, digital watermarking is playing an increasingly important role in the geographical information copyright protection. However, usually there is a high undetected rate in image watermarking authentication of GIS (Geographic Information System) vector data, because in watermark similarity calculation there are some problems, for example, the extracted watermark displacement and length changing, etc. This paper based on these focused research: the extracted watermark's position correction methods on the basis of the original watermark; the non-equal length strings similarity calculation methods, formed an image watermark similarity calculation of GIS vector data. This method effectively solved the problem of the high undetected rate in watermark certification, and to some extent improved the vector data's copyright authentication theoretical basis and technology.