Object extraction from remote sensing images is critical for a wide range of applications, and object-oriented accuracy assessment plays a vital role in guaranteeing its quality. To evaluate object extraction accuracy, this paper presents several novel accuracy measures that differ from the norm. First, area-based and object number-based accuracy assessment measures are given based on a confusion matrix. Second, different accuracy assessment measures are provided by combining the similarities of multiple features. Third, to improve the reliability of the object extraction accuracy assessment results, two accuracy assessment measures based on object detail differences are designed. In contrast to existing measures, the presented method synergizes the feature similarity and distance difference, which considerably improves the reliability of object extraction evaluation. Encouraging results on two QuickBird images indicate the potential for further use of the presented algorithm.

Purpose: This paper aims to investigate how the contextual marketing strategy facilitated by mobile technology enhances consumers’ purchase intention and experiential quality in a hedonic service setting – museum. Specifically, this study explores the impacts of a context-triggered push notification promoting context-related souvenirs embedded in the museum mobile app on visitors’ souvenir purchase intention and visit experience. Design/methodology/approach: This study adopted a scenario-based experimental design with video stimuli and a follow-up survey questionnaire. A total of 151 participants were randomly assigned to one of the two art museum visiting scenarios (i.e. with vs without the in-app push notification). A series of one-way ANOVA comparisons and serial mediation tests were conducted for hypotheses testing. Findings: The results show that the mobile app promoting context-related souvenirs positively affects museum visitors’ souvenir purchase intention without undermining their experiential quality. The serial mediation effects through contextual perceived value and impulse buying tendency further explain how the in-app push notification influences visitors’ souvenir purchase intention. Originality/value: This study extends the contextual marketing research to a unique service domain (i.e. museum) and contributes to the service technology literature. The findings present empirical evidence for the effectiveness of mobile contextual marketing and its potential to enhance consumers’ experiential quality in a hedonic service setting. The current research provides practical guidelines to both the museum management and mobile app developers.

Land cover (LC) and LC change information are essential data in terrestrial surface research. However, the LC products are highly inconsistent, especially in the mountainous area. Most LC assessment studies focused on the consistency of spatial patterns, while ignoring the consistency of change and elevation heterogeneity. In this article, four LC products were assessed for spatiotemporal consistency on the Tibetan Plateau (TP), including the moderate resolution imaging spectroradiometer LC (MCD12Q1), the climate change initiative LC (CCI-LC), the 30-meter global land cover (Globeland30) and the multiperiod land use/ LC remote sensing monitoring database in China. The impact of elevation on the consistency of multiple LC products across spatiotemporal scales was further analyzed. The spatial consistency of the three and more products was about 70%, with higher consistency for grassland and bare land and lower consistency for wetland and shrubland on the TP. Globeland30 and CCI-LC were better than others for overall monitoring, with the inconsistency of less than 45% by Google Earth dataset validation. The temporal change consistency of the four LC products was less than 10%. With increasing elevation, the average spatial consistency decreased and the LC change area and temporal change consistency increased. There is a high inconsistency of LC changes on the TP in existing commonly used products, demonstrating the need to develop high-quality LC products in long time series.

New types of remote sensed land cover datasets provide key evidence for understanding global environmental change. However, low data consistency makes understanding the changes unclear. China has become a hot spot of land cover change in the world due to climate change and a series of human measures, such as ecological engineering, land consolidation, and urbanization. However, due to the inconsistencies in interpretation of signs and thresholds, the understanding of yearly-continued land cover changes in China is still unclear. We aim to produce China’s land cover fraction dataset from 2001 to 2015 by weighted consistency analysis. We compare the Moderate-resolution Imaging Spectroradiometer land cover dataset (MCD12Q1), the Climate Change Initiative Land Cover (CCI-LC) datasets, and a new land cover fraction dataset named China-LCF_MCD-CCI, produced with a 1 km resolution. The obvious increased forest areas only accounted for 4.6% of the total forest areas, and were mainly distributed in northeast China. Approximately 75.8% of the grassland and shrubland areas decreased in size, and these areas were relatively concentrated in northeast and south China. The obvious increased areas of cropland (3.7%) were equal to the obvious decreased areas (3.6%), and the increased cropland areas were in northwest China. The change in bare land was not obvious, as the obvious increased areas only accounted for 0.75% of the bare land areas. The results not only prove that the data fusion of the weighted consistency method is feasible to form a land cover fraction dataset, but also helps to fully reveal the trends in land cover fraction change in China.