Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities

Чэн, Бин; Xu, Bing; Gong, Peng

doi:10.1080/20964471.2021.1939243

Cited by 43 publications

(9 citation statements)

References 198 publications

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“…A very high rate of urbanization resulting from natural increase, internal and external migration in Irbid city is responsible for the city's growth and change in land use patterns. Urban land use is the main category of land use that is first influenced by human activities (Chen et al, 2021). The conventional surveying and mapping methods have not delivered the necessary information in a timely and cost-effective manner.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Urban Growth and Land Use Change Detection with Gis Techniques In Irbid City, Jordan

Alwedyan

2023

IRSPSD International

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Urban growth and land use change are just a few of the many perplexing factors affecting our cities in the future. The city of Irbid has been characterized by rapid growth as a result of political and social developments related to the events in the region, where the structure of Irbid city and its spatial organization have radically changed; it was transformed from a village to a city and later to the second-largest city in Jordan. This study, using GIS techniques, aimed to monitor the urban growth direction and land use change pattern over the past seven decades, specifically during the period from 1950 to 2019 by creating GIS maps related to the land use of Irbid city. It also discussed the influencing factors. The study proved the effectiveness of GIS and its techniques in producing accurate land use maps and detailed change statistics for Irbid city, which provide an important database assist in the formulation of policies related to land use and urban growth in the city or in making decisions and developing plans related to the providing of appropriate facilities. The study concludes with practical recommendations for urban development of Irbid city for a better understanding of the city growth pattern.

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Section: Introductionmentioning

confidence: 99%

Monitoring Urban Growth and Land Use Change Detection with Gis Techniques In Irbid City, Jordan

Alwedyan

2023

IRSPSD International

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“…In this context, geospatial technologies are important in the paradigm shift and transition to climate change initiatives, greener economies, natural resource conservation, sustainable carbon emissions, catastrophe effect assessment and management, and sustainable agriculture production, among other things [17][18][19][20][21][22]. These technologies play a crucial role in the development of land cover maps and datasets using integrated efficient and cost-effective approaches including remote sensing and machine learning [14].…”

Section: Introductionmentioning

confidence: 99%

An International Library for Land Cover Legends: The Land Cover Legend Registry

Mushtaq

Henry

O’Brien

et al. 2022

Land

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Information on land cover is vital to numerous United Nations (UN) missions, including achieving the Sustainable Development Goals (SDGs). Because land cover data are developed by a variety of organizations for a range of objectives, they are based on different classification schemes and have discrepancies. In addition, the sustainability for land cover is hampered by limited access to information and documentation. Accordingly, international standards for land cover are developed to improve interoperability between different land cover datasets. However, the use and development of land cover datasets are limited by various factors including availability of properly documented land cover legends in support of different applications including change assessment, comparison, and international reporting. The purpose of this article is to highlight the importance of land cover in achieving several goals and to introduce the first international platform for land cover legend, named Land Cover Legend Registry (LCLR). This registry is a contribution to the international land cover community and the UN in effort to promote and support data harmonization processes and interoperability from local to global level, and vice versa. Users can not only use the registry for preparing consistent datasets, but also contribute to it by providing the latest data to ensure the long-term availability of both updated and existing datasets around the world. Moreover, building on the experience developing land cover legends with different nations, a brief explanation on the preparation of legends is also provided. Additionally, it is more important than ever to develop land cover registers to support the use, expansion, integration, and use uptake of land cover data, particularly for innovative remote sensing, machine learning, and information and communication technologies and techniques that build on existing and national contexts.

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“…Urban transportation and land use have a close coupling relationship. In the short term, the spatial distribution of urban land use is relatively static, and various areas with different land-use properties complete the exchange of people and products through urban transportation links, that is, urban land use patterns determine the generation and spatial distribution of urban transportation [ 2 ]. In the long term, the construction of transportation infrastructure and the supply of transportation services improve accessibility, and the increased commuting costs of traffic congestion reduce the land value of the area, and this difference in value further adjusts the land development process through the individual choice behavior of urban residents, thus affecting the urban spatial structure [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

The Big Data Model for Urban Road Land Use Planning Is Based on a Neural Network Algorithm

Zhang

2022

Computational Intelligence and Neuroscience

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The spatial differentiation of land use induces traffic demand and guides the construction of traffic supply; traffic conditions are an important influencing factor in determining the nature of land use, and there is a close interaction between the two. This study uses a neural network-based approach at the urban grid level to portray representative phenomena of urban development and analyze the interaction between transportation and land use. The results reflect the model’s effective simulation of urban laws, and the case study reveals the differences in the laws of different cities, to guide the benign development of cities and transportation. This article firstly conducts a study on the theoretical foundation; compares the development history, planning, and design methods and practical experience of road planning and resilient planning; summarizes the experience of resilient road system design; and analyzes the future development trend, based on the above basic theoretical research, to develop research ideas and methods. Secondly, the scenario analysis method is explicitly applied to analyze various scenarios that may occur in the future development process of simulated urban roads and rank the scenarios based on the probability of occurrence. For the impact of traffic on land use, the concepts of vitality and potential are introduced, and a multidimensional long and short-term memory network (MDLSTM) model is established. The model takes into account land use lags and potential transfer and has relatively higher prediction accuracy. The results show that larger cities with urban dominant industries and tertiary industries also have higher land use potential and the more significantly influenced by traffic.

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Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities

Cited by 43 publications

References 198 publications

Monitoring Urban Growth and Land Use Change Detection with Gis Techniques In Irbid City, Jordan

Monitoring Urban Growth and Land Use Change Detection with Gis Techniques In Irbid City, Jordan

An International Library for Land Cover Legends: The Land Cover Legend Registry

The Big Data Model for Urban Road Land Use Planning Is Based on a Neural Network Algorithm

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