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Articles 1 - 3 of 3
Full-Text Articles in Urban Studies and Planning
Different Stages Of Urbanization Lead To Cropland Loss: Geospatial Data Analysis In A Global Rural-Urban Continuum Perspective, Xiyu Li, Le Yu
Different Stages Of Urbanization Lead To Cropland Loss: Geospatial Data Analysis In A Global Rural-Urban Continuum Perspective, Xiyu Li, Le Yu
I-GUIDE Forum
Rapid urbanization has resulted in widespread loss of cropland, but the spatial-temporal patterns and key stages of this transformation have not been clearly understood. In this study, we mapped cropland loss caused by urban expansion in rural-urban continuum since 2000 based on multiple global land maps. Results show a total 3.35 × 106 ha cropland loss resulting from urban expansion and about 70 % of this land use change occurred in Asia. However, there is a trend of reducing cropland loss area caused by urbanization over the years. In a detailed view of urbanization stages, most cropland losses occurred …
Large-Scale Google Street View Images For Urban Change Detection, Fangzheng Lyu, Xinlin Ma, Yan Song, Eric Zhu, Shaowen Wang
Large-Scale Google Street View Images For Urban Change Detection, Fangzheng Lyu, Xinlin Ma, Yan Song, Eric Zhu, Shaowen Wang
I-GUIDE Forum
Urbanization has entered a new phase characterized by urban changes occurring at a micro-scale and “under the roof”, as opposed to external modifications. These changes, known as urban retrofitting, involve the incorporation of novel technologies or features into pre-existing systems to promote sustainability. Given the limitations of remote sensing images in identifying such urban changes, novel tools need to be developed for detecting urban retrofitting. In this study, we first build a pipeline to collect large-scale time-series urban street view images from Google Street View in Mecklenburg County, North Carolina. And we examine the feasibility of utilizing the acquired dataset …
Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian
Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian
I-GUIDE Forum
Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or …