An integrative method for mapping urban land use change using "geo-sensor" data

Chang, Charles; Ye, Zhiwei; Huang, Qunying; Wang, Caixia

doi:10.1145/2835022.2835031

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…The DI-based urban land use mapping processes the RS and GBD independently with different models and methods and combines the RS-based and GBD-based results for further generating the urban land use map. The DI-based method was first introduced by Chang et al [33] and provides an efficient way for mapping urban land use in Kunming City by integrating POI data and Landsat images. Since then, a series of studies have followed that method.…”

Section: Di-based Urban Land Use Mappingmentioning

confidence: 99%

“…In this study, urban parcels with impervious surfaces were defined as built-up, otherwise, the parcels were labeled as non-built-up. This step was carried out under the assumption that built-up parcels require at least partially impervious surfaces [33]. The non-built-up parcels were further classified as open space, while built-up parcels were used for further analysis.…”

Section: Di-based Urban Land Use Mappingmentioning

confidence: 99%

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Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Yin

Hamm

et al. 2021

Remote Sensing

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Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy.

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Section: Di-based Urban Land Use Mappingmentioning

confidence: 99%

Section: Di-based Urban Land Use Mappingmentioning

confidence: 99%

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Yin

Hamm

et al. 2021

Remote Sensing

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“…Previous studies have also identified the usefulness of integrating remote sensing and other types of ancillary data in urban LUC classification. Some of the ancillary data used in the literature include: zoning and housing density data [9]; population census data [11,12]; municipal master plan [34]; social media data [35]; and geographical points of interest data [10,35]. Some recent studies have also explored and recommended using high-resolution Google Earth imagery for improved LUC classification through visual interpretation [36][37][38][39].…”

Section: Performance Of Ulu Classification Approachmentioning

confidence: 99%

Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia

Simwanda

Murayama

2017

IJGI

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For most sub-Saharan African (SSA) cities, in order to control the historically unplanned urban growth and stimulate sustainable future urban development, there is a need for accurate identification of the past and present urban land use (ULU). However, studies addressing ULU classification in SSA cities are lacking. In this study, we developed an integrated approach of remote sensing and Geographical Information System (GIS) techniques to classify ULU in the developing SSA city of Lusaka. First, we defined six ULU classes (i.e., unplanned high density residential; unplanned low density residential; planned medium-high density residential; planned low density residential; commercial and industrial; public institutions and service areas). ULU parcels, created using road networks as homogenous units separating ULU classes, were used to classify ULU. We utilised the combined detail of cadastral and land use data plus high-resolution Google Earth imagery to infer ULU and classify the parcels. For residential ULU, we also created density thresholds for accurate separation of the classes. We then used the classified ULU parcels for post-classification sorting of built-up pixels extracted from three Landsat TM/ETM+ imageries (1990, 2000, and 2010) into respective ULU classes. Three ULU maps were produced with overall accuracy values of 84.09% to 85.86%. The maps provide information that is relevant to urban planners and policy makers for sustainable future urban planning of Lusaka City. The study also provides an insight for ULU classification in SSA cities with complex urban landscapes similar to Lusaka.

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A Data Driven Approach to Study the Social and Political Statuses of Urban Communities in Kunming

Chang

2020

J Chin Hist

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This article presents a data-driven approach to the study of the social and political statuses of urban communities in modern Kunming. Such information is lacking in government maps and documents. Using data from a wide variety of sources, many unconventional, I subject them to critical evaluation and computational analysis to extract information that can be used to produce a land use map of sufficient detail and accuracy to allow scholars to address and even answer questions of a socio-political, economic and, indeed, humanistic nature. My method can also be applied to other Chinese cities and to cities elsewhere that lack accurate information.

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An integrative method for mapping urban land use change using "geo-sensor" data

Cited by 3 publications

References 20 publications

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia

A Data Driven Approach to Study the Social and Political Statuses of Urban Communities in Kunming

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