Abstract
Detailed information on urban land uses has been an essential requirement for urban land management and policymaking. Recent advances in remote sensing and machine learning technologies have contributed to the mapping and monitoring of multi-scale urban land uses, yet there lacks a holistic mapping framework that is compatible with different end users’ demands. Moreover, land use mix has evolved to be a key component in modern urban settings, but few have explicitly measured the spatial complexity of land use or quantitively uncovered its driving forces. Addressing these challenges, here we developed a novel two-stage bottom-up scheme for mapping essential urban land use categories. In the first stage, we conducted object-based land use classification using crowdsourcing features derived from multi-source open big data and an automated ensemble learning approach. In the second stage, we identified parcel-based land use attributes, including the dominant type and mixture mode, by spatially correlating land parcels with the object-based results. Furthermore, we investigated the potential influencing factors of land use mix using principal components analysis and multiple linear regression. Experimental results in Ningbo, a coastal city in China, showed that the proposed framework could accurately depict the distribution and composition of urban land uses. At the object scale, the highest classification accuracy was as high as 86% and 78% for the major (Level I) and minor (Level II) categories, respectively. At the parcel scale, the generated land use maps were spatially consistent with the object-based maps. We found larger parcels were more likely to be mixed in land use, and industrial lands were characterized as the most complicated category. We also identified multiple factors that had a collective impact on land use mix, including geography, socioeconomy, accessibility, and landscape metrics. Altogether, our proposed framework offered an alternative to investigating urban land use composition, which could be applied in a broad range of implications in future urban studies.
Highlights
This article is an open access articleOur planet witnessed rapid urbanization in recent decades
We developed a novel two-stage bottom-up framework for urban land use categories mapping with multi-source geospatial big data and an automatic ensemble learning approach
Taking Ningbo as the case study, we provided a comprehensive review of urban land use composition in a Chinese city with the four research aims as follows: (1) derive urban land use classification maps accurately at both object and parcel scales; (2) verify the efficiency and robustness of ensemble learning in object-based urban land use classification; (3) measure the degree of land use mix at the parcel scale; and (4) investigate potential influencing factors that drive land use mix
Summary
This article is an open access articleOur planet witnessed rapid urbanization in recent decades. By 2018, global artificial surface areas reached 797,076 km , more than 2.5 times that of 1990 [1]. This trend is expected to continue in the coming decades that by 2050, about 70% of the world’s population (6.7 billion) is going to live in urban areas [2,3]. 2021, 13, 4241 the meantime has triggered a series of environmental and ecological problems, such as environmental degradation [4,5], greenspace exposure [6,7], cropland displacement [8,9], and biodiversity loss [10,11]. To maintain such trade-off as well as achieve sustainability, it is of great importance to capture the spatiotemporal dynamics of urban land use changes from historical retrospect and future prediction, which in fundament, requires the availability of accurate and fine-resolution urban land use maps
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