Rapid Detection of Land Cover Changes Using Crowdsourced Geographic Information: A Case Study of Beijing, China

Abstract

Land cover change (LCC) detection is a significant component of sustainability research including ecological economics and climate change. Due to the rapid variability of natural environment, effective LCC detection is required to capture sufficient change-related information. Although such information has been available through remotely sensed images, the complicated image processing and classification make it time consuming and labour intensive. In contrast, the freely available crowdsourced geographic information (CGI) contains easily interpreted textual information, and thus has the potential to be applied for capturing effective change-related information. Therefore, this paper presents and evaluates a method using CGI for rapid LCC detection. As a case study, Beijing is chosen as the study area, and CGI is applied to monitor LCC information. As one kind of CGI which is generated from commercial Internet maps, points of interest (POIs) with detailed textual information are utilised to detect land cover in 2016. Those POIs are first classified into land cover nomenclature based on their textual information. Then, a kernel density approach is proposed to effectively generate land cover regions in 2016. Finally, with GlobeLand30 in 2010 as baseline map, LCC is detected using the post-classification method in the period of 2010-2016 in Beijing. The result shows that an accuracy of 89.20% is achieved with land cover regions generated by POIs, indicating that POIs are reliable for rapid LCC detection. Additionally, an LCC detection comparison is proposed between remotely sensed images and CGI, revealing the advantages of POIs in terms of LCC efficiency. However, due to the uneven distribution, remotely sensed images are still required in areas with few POIs.

Urbanization in the North? An Examination of Urban Land Cover/Land Use Change in Nunavut

“…There is an emerging consensus that urbanization is critically important, particularly in the context of adhering to Sustainable Development Goals (SDGs), but there is considerable confusion over what defines urban growth and urbanization; whether 'it' is accelerating or slowing; whether it should be encouraged or discouraged; and, more generally, what the responses should be (McGranahan & Satterthwaite, 2014). Using techniques of land cover change (LCC) detection, a significant component of sustainability research (including ecological economics and climate change) can help to determine when and where growth that could be considered 'urban' is taking place (Meng et al 2017).…”

Predicting Urban Functional Zones with Twitter Data Using the Space-Time Scan Statistics Method and the Random Forest Classifier

“…A popular complimentary data source for POI attributes is high resolution satellite data (Bao et al, 2020;Hu, Yang, Li, & Gong, 2016;Xiaoping Liu et al, 2017;Song, Lin, Li, & Prishchepov, 2018;Song, Tong, Wang, Zhao, & Prishchepov, 2019). While this combination of data sources has resulted in an overall accuracy of up to 89.2% (Meng et al, 2017), high resolutions and up-to-date satellite data is not always easy to procure. Social media data such as Twitter, and Foursquare, in addition to VGI such as OpenStreetMap, overcome the issue of procurement and tend to be the only data source required when used for classification.…”

“…Taxi trajectories / GPS trajectories (Goddard, 1970;Qi et al, 2011 (Calegari et al, 2015;Fonte et al, 2017;Xingjian Liu & Long, 2016) Autonavi Map / Gaode Map (Meng et al, 2017), (Zhang et al, 2018), (Bao et al, 2020), (Wang, Gu, Dou, & Qiao, 2018) Baidu Maps (Song et al, 2019), (Yao et al, 2017), (Xiaoping Liu et al, 2017), (Zhai et al, 2019)…”

Investigating Road-Constrained Spatial Distributions and Semantic Attractiveness for Area of Interest

“…To validate the effectiveness of the generated road-constrained AOIs, we generate the traditional AOIs according to KDE. According to the research proposed by Meng et al [36], we set the bandwidth at 30 meters. Figure 5 displays the spatial distribution of traditional AOIs.…”