Search Results (2)

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. Full article

Land cover classification is the most important element of land cover mapping and is a key input to many societal benefits. Traditional classification methods require a large amount of remotely sensed images, which are time consuming and labour intensive. Recently, crowdsourcing geographic information (CGI), including geo-tagged photos and other sources, has been widely used with lower costs, but still requires extensive labour for data classification. Alternatively, CGI textual information is available from online sources containing land cover information, and it provides a useful source for land cover classification. However, the major challenge of utilising CGI is its uneven spatial distributions in land cover regions, leading to less reliability of regions for land cover classification with sparsely distributed CGI. Moreover, classifying various unorganised CGI texts automatically in each land cover region is another challenge. This paper investigates a faster and more automated method that does not require remotely sensed images for land cover classification. Spatial clustering is employed for CGI to reduce the effect of uneven spatial distributions by extracting land cover regions with high density of CGI. To classify unorganised various CGI texts in each extracted region, land cover topics are calculated using topic model. As a case study, we applied this method using points of interest (POIs) as CGI to classify land cover in Shandong province. The classification result using our proposed method achieved an overall accuracy of approximately 80%, providing evidence that CGI with textual information has a great potential for land cover classification. Full article