Land Classification Plugin for QGIS Using Pix2Pix

Introduction Virtual globes are relatively recent phenomena in the computer world, but they are quickly becoming ubiquitous and popular. Their usefulness for geographic education is unquestionable, but the educators must be informed consumers of this technology and be aware of both the capabilities and limitations of virtual globes. Knowing what makes virtual globes different form Geographic Information Systems (GIS) and online mapping applications is also important. Learning how to use virtual globes and analyze spatial data that they depict may be an overwhelming task at first. There is a lot of information available on Internet on digital globes (blogs, user communities, etc.), but it is much dispersed. Our article is an attempt to consolidate and summarize the most important up‐to‐date information on virtual globes, with an emphasis on their educational applications. In this Teaching and Learning Guide we provide an annotated list of the best resources that exist on digital globes and focus questions for classroom discussions on this subject. We also provide four practical exercises with step‐by‐step instructions and color illustrations. These exercises do not use any GIS software and can be used in various geography and social studies courses. Key Readings Riedl A, 1999, Virtual globes: A new era for globes?, ICA 1999 19th International Cartographic Conference This paper provides an early idea of a ‘virtual globe’ that can use multimedia capabilities like sound, animation and interactivity that can enrich the way of visualizing natural phenomena and processes. Aurambout J P, Pettit C and Lewis H, 2008, Virtual globes: the next GIS? In: Landscape analysis and visualisation. Heidelberg: Springer Berlin, pp. 509–532 This paper compares the strengths and weaknesses of five major virtual globes and evaluates their capabilities to present the GIS data is the context of agricultural sciences and natural resources management. It shows the present limitations of virtual globes in visualizing the GIS data and suggests several improvements in the virtual globes. Brown M C, 2006, Hacking Google maps and Google earth. New York: Wiley This book provides a step‐by‐step tutorial for creating applications to make maps that reveal statistical data, plot and calculate distances for routes, highlight archaeological information etc. Butler D, 2006, Virtual globes: the web‐wide world. Nature (439): 776–778 With the help of various examples, the article shows that online tools like Google Earth and other easy‐to‐use virtual globes, are changing the way we interact and communicate with spatial data. Goodchild M, 2005, What does Google earth mean for the spatial sciences? Biennial Conference of the Spatial Sciences Institute. The author discusses technical aspects of showing GIS data and satellite images on the virtual globes. He named virtual globes as ‘Global GIS’ and said that the VG has the potential to extend spatial science to a much larger community of social scientists, users and students. Tuttle B T, Anderson S, and Huff R, 2008, Virtual globes: an overview of their history, uses, and future challenges. Geography Compass 2 (5): 1478–1505. This paper provides a commentary on the rise of virtual globes, reviews the current literature available and identifies the areas that require more research. Focus Questions These focus questions/topics can be used in the context of any introductory mapping or GIS class to initiate the discussion of the importance and the limitations of virtual globes. How have virtual globes changed the way we visualize spatial data? What are the limitations of virtual globes? Can virtual globes replace GIS? What are the implications of inaccuracies in the data in virtual globes? Virtual globes and the digital divide Examples of Practical Exercises Below we provide four student activities that illustrate the use of three virtual globes (Google Earth, Virtual Earth, and Skyline Globe) in education. We believe that the rich visual environment of virtual globes enables students to frame interesting questions, and to visualize and analyze various spatial layers, produced and overlaid without any GIS software. The first two activities are designed for a novice user of virtual globes. Activity one helps in delineating the extent of urban sprawl in Long Island, NY using Skyline Globe. The second activity allows to visualize the country wise changes in internet use through time using Google Earth. The last two activities and intended for more advanced users as they require familiarity with Microsoft Excel (activity 3) and concepts of image classification (activity 4). Activity 3 helps in mapping the toxic release inventory (TRI) of the Worcester County on Massachusetts and finding out the economic status of the population living near the TRI sites the using MS Excel and Google Earth. Activity 4 shows how the students can use virtual globes for their various remotely sensed image classification processes. The Links to downloading instructions for the four globes are listed in the Appendix of the original paper. Activity 1: Estimating the extent of urban sprawl In this activity we will use interactive tools of virtual globes (on‐screen digitizing and area calculations) to estimate the extent of urban sprawl in Long Island, New York. Here virtual globe is used to perform tasks that were previously only available in GIS software. Required Software Skyline Globe Process Long Island, is it the largest island in the continental United States. We will use virtual globe to calculate the percentage of urban land from the total area of the island. Open Skyline Globe and search/zoom to Long Island, NY. Click on the Layers tab at the top of the screen and check the ‘Urban sprawls (US)’ check box in the left hand side panel. You’ll see the urban sprawl in Long Island in red color (). To find the area covered by urban sprawl, click on the Measure tab and select ‘Area measurement’ in the left hand side panel (). Digitize various urban sprawl areas on Long Island and write down the area covered by each of them. Add up the areas of the sprawl together. Find the total area of Long Island from literature/Internet. Calculate the percentage of area covered by urban sprawl in the Long Island.

Land cover classification is a fundamental operation in remote sensing, with applications in environmental monitoring, urban planning, and agricultural management. This research investigates the use of Convolutional Neural Networks (CNNs) for efficient and accurate classification of land cover types from satellite imagery. Through the use of CNNs, the model can identify spatial and spectral features that distinguish different land cover classes, including urban, forest, water, and agricultural areas. The capability of CNNs to handle high-resolution satellite datasets significantly enhances classification accuracy and computation time compared to conventional approaches. The results of this study provide valuable insights for land conservation and management practices while laying the groundwork for future developments in remote sensing technologies.

Ice gouging of the seabed is a significant consideration for offshore pipelines in the arctic. A methodology is presented for using GIS (Geographical Information System) software for analyzing pipeline burial depth requirements for protection against ice gouging. This approach allows users to perform development concept evaluations, estimate pipeline burial depth requirements, test and optimize various routes and configurations, and generate estimates for pipe and trenching costs once the required input parameters are generated for a specific region, site or development. The framework and algorithms are described and ice gouge data from the public domain are used for demonstration purposes. The use of the LCP (Least Cost Path) algorithm (included in most GIS software) for pipeline route optimization is demonstrated. This subject is of relevance for any offshore development that requires trenching of pipelines in an ice-gouged seabed. Introduction A substantial portion of the world's petroleum reserves are believed to be in arctic offshore regions and other offshore ice- frequented environments. As the world's energy demand continues to increase, oil and gas developments in these environments will also increase accordingly. Consequently, more subsea pipelines will be constructed in these environments. Some of these pipelines (i.e. flowlines, subsea tiebacks and export pipelines) will be constructed in regions where the seabed is subjected to gouging by ice, requiring the evaluation of the magnitude of the risk, risk mitigation requirements and associated costs. The assessment of pipeline burial depth requirements for protection against ice gouging of the seabed is generally performed by specialists for a limited number of pipeline route options. However, the evaluation of offshore development options can involve the assessment of a number of field configurations and pipeline routing alternatives. The ability to determine pipeline burial requirements and costs, without reverting to specialists during iterations of the field development planning process, potentially represents a significant advantage in terms of time and cost savings. GIS (Geographical Information System) software is useful in the offshore design process, since various relevant data sets (i.e. bathymetry, sediment types, hazards, etc.) can be incorporated into the same platform. GIS software is being used fairly extensively in assessing pipeline routing and hazard assessment for applications on land, but similar offshore applications are not common. The procedure outlined here can be adapted to various regions, provided the required parameters are available. The analysis requires rasters defining ice gouge crossing rates and gouge depth parameters (as well as other parameters such as pipe response, failure criteria, etc.) to calculate the pipeline cover depth required to meet the target reliability, as well as cost values or functions to produce the associated cost rasters used as the basis for the LCP (Least Cost Path) analysis, which can assist in the selection of the optimum pipeline layout. While generating these rasters and incorporating the required algorithms would require the services of a specialist, the GIS tool can be readily employed by others who need to consider pipeline protection in the context of the overall development scenario(s). The final design would still be evaluated or checked by a specialist.

Introduction: Urban spatial planning is critical for the development of sustainable and livable cities. However, traditional planning methods often face challenges in handling complex planning scenarios and large-scale data.Methods: This paper introduces UrbanGenoGAN, a novel algorithm that integrates generative adversarial networks (GANs), genetic optimization algorithms (GOAs), and geographic information system (GIS) to address these challenges. Leveraging the generative power of GANs, the optimization capabilities of genetic algorithms, and the spatial analysis capabilities of GIS, UrbanGenoGAN is designed to generate optimized urban plans that cater to various urban planning challenges. Our methodology details the algorithm’s design and integration of its components, data collection and preprocessing, and the training and implementation processes.Results: Through rigorous evaluation metrics, comparative analysis with existing methodologies, and case studies, the proposed algorithm demonstrates significant improvement in urban planning outcomes. The research also explores the technical and practical considerations for implementing UrbanGenoGAN, including scalability, computational efficiency, data privacy, and ethical considerations.Discussion: The findings suggest that the integration of advanced machine learning and optimization techniques with spatial analysis offers a promising approach to enhancing decision-making in urban spatial planning. This work contributes to the growing field of AI applications in urban planning and paves the way for more efficient and sustainable urban development.

It is well-known that remote sensing technology has played a very important role in the urban planning and management since 1980s. In the past thirty years, as one of spatial data sources, remote sensing datasets, which contain more information of earth surface as compared with usual urban maps, have been used in urban planning and management in China. With the development of remote sensing technology such as the high spatial resolution sensors, 3D laser scanning, data mining and advanced image processing technology, remote sensing can be widely used in the whole range of urban planning processes. In this paper, we firstly make a review of the urban remote sensing applications in urban study and planning by giving examples. Then we identify remote sensing applications in different urban planning stages including master planning, district planning, detailed planning and planning implementation as well as in urban studies. We also analyze the bottlenecks of the remote sensing applications in urban planning and management in China at the end.

This paper illustrates the capabilities of geographic information system (GIS) software to create and display silicate structures. These models require no knowledge of editing functionality within the GIS software. Instead, the required input is the coordinates of the individual atoms or tetrahedra constructed in a word processing or spreadsheet program. These coordinates are then imported into the GIS software, and can be used to display chain, ring, sheet, and framework structures. Links to silicate models exported from the GIS software in a virtual reality modeling language (VRML) format are provided. These models can be displayed using a web browser equipped with any freely available VRML plug-in.Creating GIS-based silicate structures is an alternative to other software packages such as CrystalMaker® and Xtaldraw® designed specifically to create and display atomic structures. Although these GIS-based methods do not have all of the functionality of custom applications, GIS is a pervasive and growing technology with numerous applications in earth sciences and many other disciplines. Instructors may consider using this GIS-based method to introduce geospatial concepts, illustrate the breadth of GIS applications, and to establish interactive links between mineralogy and other disciplines.

Principles of stereoscopic vision have long been used in geographic data processing and visualization, e.g. Laussedat created what he called metrophotography in 1895 [2][3] and a stereoautograph for plotting from terrestrial photographs was produced in 1911 [2]. Today, a common and established use of stereoscopy in geographic domain is three-dimensional (3D) modeling of terrains and cities based on aerial, satellite and/or terrestrial imagery with photogrammetric techniques. In these systems, commonly, stereoscopic imagery is used as an input and a vector 3D representation of the terrain or city is obtained as an output. Stereoscopy has also been used for close-range photogrammetry, however within the scope of this paper the focus is on the cartographic use of stereoscopic visualizations. In recent years, a number of mainstream digital devices with stereoscopic capabilities are marketed (e.g. [4], [5], [6]), which indicates that we can expect to see more stereoscopic content in the near future [1]. In anticipation of such a development, it is also plausible to expect that stereoscopic geo-data will become more available for everyday use. It is, therefore, interesting to document which stereoscopic visualization features are supported by mainstream geographic information systems (GIS) software and whether these features can be improved. This work presents a survey on current stereoscopic visualization support in geographic information (GI) systems. The underlying objective is to outline future research needs by examining current stereoscopic 3D visualization techniques and tools in current GISs. This paper focuses on visualization modules of GIS software, and tries to answer the question “How commonly is stereoscopic visualization supported in GIS?" To achieve this, first whether such support exits or not in GIS software is documented, then supported types of stereoscopic operations and viewing methods are reported. Motivated by improving efficiency of stereoscopic visualizations in cartographic domain, within this survey, it is also examined whether these software offer level of detail (LOD) management support for stereoscopic datasets. Findings from the study are meant as a guide to researchers and practitioners to assess the current state of True 3D support in widely used GIS software.

Land Use and Land Cover (LULC) classification plays a crucial role in remote sensing applications such as urban planning, environmental monitoring, agricultural analysis, and climate studies. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have significantly improved classification accuracy for satellite imagery. This thesis presents a comparative study of two deep learning approaches for LULC classification using the EuroSAT dataset: a convolutional neural network trained from scratch and a transfer learning model based on a pre-trained VGG-19 architecture. The EuroSAT dataset consists of Sentinel-2 satellite images categorized into ten land cover classes. Experimental results demonstrate that transfer learning achieves superior classification performance compared to training a CNN from scratch, highlighting the effectiveness of pre-trained models for remote sensing image analysis.

Chinese cities are becoming larger and urbanization is accelerating with the rapid development of China's economic construction and formulation of planning objectives. Traditional manual management and information processing methods cannot satisfy the requirements of modern urban planning management. Thus, this study analyzes the application of geographic information system (GIS) in urban planning, starting with the characteristics of GIS technology. Structured analysis and design are conducted for urban planning and the location system. The software engineering approach is adopted throughout the entire process, from the survey to the overall design of the system and database. The evaluation factors of the pension facilities in Baohe, Hefei are extracted through the factor evaluation method, and the weight value is determined. The GIS analysis method is then used, and site suitability evaluation is performed on the Baohe district land layout. Results show that the application of urban planning and a location system based on GIS technology can provide the basis for urban planning.

I Geographical Information Systems and Planning.- 1 Geographical information systems: the emerging requirements.- 2 The application of geographical information systems in urban and regional planning.- 3 Growth of geographical information system applications in developing countries.- II Data Management.- 4 Intelligent information systems for accessing planning databases: the San Francisco experience.- 5 Geographical information systems database design: experiences of the Dutch National Physical Planning Agency.- III Urban Planning Applications.- 6 Information management within the planning process.- 7 Fixed asset management and geographical information systems in the Netherlands.- 8 Geographic information system development in Tacoma.- IV Decision Support Systems for Land Use Plannng.- 9 Regional planning for new housing in Randstad Holland.- 10 Geographical information system applications in environmental impact assessment.- 11 A geographical information system based decision support system for environmental zoning.- 12 Multicriteria analysis and geographical information systems: an application to agricultural land use in the Netherlands.- V Spatial Analysis, Modelling and Decision Support.- 13 The application of geographical information systems in the spatial analysis of crime.- 14 Spatial analysis and geographical information systems: a review of progress and possibilities.- 15 Geographical information systems and model based analysis: towards effective decision support systems.- 16 Decision support and geographical information systems.- VI Education and Management.- 17 Education in geographical information systems.- 18 How to cope with geographical information systems in your organisation.- VII Developments in Hardware and Software.- 19 Geoprocessing and geographic information system hardware and software: looking toward the 1990s.- 20 Geographical information systems and visualization.- VIII Information Based Societies.- 21 Geographical information systems in perspective.- References.- Color plates.

Spatial planning to inform actions and interventions that go well beyond traditional notions of urban planning is now an integral part of UK domestic policy making. Education in spatial planning is now required as a condition of becoming a Royal Town Planning Institute (RTPI) chartered town planner. All RTPI-accredited planning programmes have incorporated spatial planning education into their curricula. Unaccredited UK urban planning programmes and programmes in geography, environmental studies, and other disciplines related to the built environment are also teaching spatial planning. The RTPI does not prescribe particular content for spatial planning education and grants universities wide discretion on what to teach. While Geographical Information Systems (GIS) software and related spatial information technology are now widely used in urban planning, the RTPI does not require UK urban planning programmes to teach GIS or spatial thinking concepts from the emerging field of Geographical Information Science and Technology (GIS&T). While many urban planning programmes now make GIS&T concepts and GIS operations central features of spatial planning education, the competencies that they teach vary widely from programme to programme. This article summarises research on urban spatial planning education the author conducted for the Spatial Literacy in Teaching (SPLINT) project based at the University of Leicester.* It describes a competency-based model for UK urban spatial planning education that matches spatial information competencies against roles in spatial planning that professionals may perform on graduation. It suggests appropriate competencies at different levels of urban planning education along multiple urban planning career paths.

Recently, Satellite images became an important tools which were used in many cases of the practical studies, it has provide a widely view to the land covers and the ability to extract the information by applying the image processing technique on the images. The ability to detect the area of land covers from satellites images is very complex method because it is not only required a high geometrical satellite images but it needs to isolate each class from the classified image dependently from the another classes that viewed in the adopted image satellite. This process is very important to determine the area for each class with an accepted level of accuracy. In the present study, an active method was design to extract and determine the areas by using digital image processing operations (digital filtering, geometrical registration and supervised classification). Landsat Thematic Mapper (TM) satellite image, which covered the study area (Mosul Dam Lake), was adopted as a remote sensing data set for the processing stages. Geographical Information System (GIS) software was applied for the georeferencing process of the TM satellite image (according to UTM-WGS84-38N System) with respect to the georeferencing topographic map of the Mosul Dam. The result of the applied method was compared with result obtained from the GIS software, Global Mapper 9.2 (GM9.2) for the same study area, and then the output results of the two methods were compared to the traditional method to calculate the area of the class from topographic maps according to their complete and missing squares. A relatively good coincidence between the result of the present study and the other methods. The results and the applied technique of this research are very important in the practical applications related to the field of survey engineering, environmental studies (desertification and environmental pollution), forest, urban planning, geology…etc.

In recent years, land use/cover dynamic change has become a key subject that needs to be dealt with in the study of global environmental change. In this paper, remote sensing and geographic information systems (GIS) are integrated to monitor, map, and quantify the land use/cover change in the southern part of Iraq (Basrah Province was taken as a case) by using a 1:250 000 mapping scale. Remote sensing and GIS software were used to classify Landsat TM in 1990 and Landsat ETM+ in 2003 imagery into five land use and land cover (LULC) classes: vegetation, sand, urban area, unused land, and water bodies. Supervised classification and normalized difference build-up index (NDBI) were used respectively to retrieve its urban boundary. An accuracy assessment was performed on the 2003 LULC map to determine the reliability of the map. Finally, GIS software was used to quantify and illustrate the various LULC conversions that took place over the 13-year span of time. Results showed that the urban area had increased by the rate of 1.2% per year, with area expansion from 3 299.1 km2 in 1990 to 3 794.9 km2 in 2003. Large vegetation area in the north and southeast were converted into urban construction land. The land use/cover changes of Basrah Province were mainly caused by rapid development of the urban economy and population immigration from the countryside. In addition, the former government policy of “returning farmland to transportation and huge expansion in military camps” was the major driving force for vegetation land change. The paper concludes that remote sensing and GIS can be used to create LULC maps. It also notes that the maps generated can be used to delineate the changes that take place over time.

The objectives of this study are (1) analyzing the land cover class, (2) analyzing the changes of land cover area in Tabunio Watershed from 2000 to 2018 and (3) analyzing the land cover of Tabunio Watershed in 2018 which is included in the forest area. The data used are watershed boundary spatial data from the Ministry of Environment and Forestry Directorate of PDAS-HL, spatial data of land cover resulting from interpretation of landsat images from the Directorate of IPSDH Directorate General of Planning and Environmental Planning in 2000, 2003, 2006, 2009, 2012, 2015 and 2018 and spatial data on forest areas from the Ministry of Forestry. Classification of land cover class by the modification method from 23 classes to 11 classes namely Forest, Plantation Forest, Open Land, Mangrove, Plantation, Settlement, Dry Land Agriculture, Rice Fields, Shrubs, Pond and Water Bodies. Data processing through geographic information systems (GIS) software ArcGis 10 and Exel. This research method uses overlapping and descriptive analysis. Land Cover Conformity Test Results an accuracy rate of 91%. The results of the analysis of land cover change show that the plantation land cover class experienced the greatest rate of change during the period of 2000-2018 which increased by an area of 10.791,70 hectares (2.169,16%). The results of the analysis land cover of Tabunio Watershed in 2018 which is included in the forest area show that Dryland Agriculture covering 4.091.48 hectares (36.31%) is the largest land cover class found in all forest area functions.Keywords: Watershed; GIS; Land Closing Classes; Suitability of Land Closure; Change in Land Closure; Forest Area

Developing countries have evolved into having rapid urban growth in terms of cities, especially the medium-sized ones, which tend to expand without proper planning regulations. The spatial temporal dynamics and directional patterns of the urban growth have key importance in terms of sustainable land management and urban planning. This paper is an analysis of the spatio-temporal development process and directional growth of Nawabshah City, Sindh, Pakistan, on the basis of multi-temporal Landsat satellite images and Geographic Information System (GIS) methods between the year 1990 and 2024. To extract the built-up and non-built-up land cover classes, ArcMap classification was conducted on Landsat TM and OLI images in 1990, 2001, 2013, 2020 and 2024. Descriptive statistics and ANOVA were regarded to conduct the analysis of urban expansion intensity, spatial growth patterns and the statistical variations. Findings indicate that built-up area has been increasing at a steady accelerating rate of 4.57km2 in 1990 to 11.27km2 in 2024 with the process changing towards dispersed, corridor-driven and peripheral with the growth process. The results point to the impact of the transportation infrastructure, population increase and socio-economic development on the spatial reorganization of the city. The paper highlights how urban monitoring and strategic spatial planning of city in GIS is necessary to control the future development in urban areas in Nawabshah and other secondary cities like Nawabshah in Pakistan.