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• Geographic features and environmental consequences of coffee tourism and coffee consumption in Budapest

  10-15

  Lóránt Dávid

  Bulcsú Remenyik

  Gogo Fredrick Collins Adol

  Views:

  499

  As a survey by the Hungarian Central Statistical Office (2019) confirms coffee is consumed in 83% of Hungarian households, thus our country can be considered to be one of the major coffee drinking nations.   At the end of the 19th century and the turn of the twentieth Budapest with its internationally famous and unique coffee culture was known as the coffeehouse capital. Post-modern tourism revived this tradition and coffee became once again a favourite consumer item while cafés turned into scenes of community life. The latest stage of the coffeehouse renaissance was partly due to the increasing role of American type café chains including McCafé, Starbucks, California Coffee Company etc. and the drop in the price of the Arabica coffee. Our research focuses on the impact of this new type of coffee consumption wave on the coffee habits of Hungarians. The American café chains have become widespread in Europe and their ability to keep the price of coffee low worldwide demonstrates significant market power. While coffee consumption has several benefits from a physiological point of view, its environmental impact is detrimental to the planet. Coffee cultivation contributes to the destruction of rainforests, the changing of the soil and last but not least results in a high amount of solid waste due to the popularity of coffee capsules. Our treatise explores these concerns as well.

• Urban vegetation classification with high-resolution PlanetScope and SkySat multispectral imagery

  66-75

  Loránd Szabó

  Dávid Abriha

  Kwanele Phinzi

  Szilárd Szabó

  Views:

  607

  In this study two high-resolution satellite imagery, the PlanetScope, and SkySat were compared based on their classification capabilities of urban vegetation. During the research, we applied Random Forest and Support Vector Machine classification methods at a study area, center of Rome, Italy. We performed the classifications based on the spectral bands, then we involved the NDVI index, too. We evaluated the classification performance of the classifiers using different sets of input data with ROC curves and AUC values. Additional statistical analyses were applied to reveal the correlation structure of the satellite bands and the NDVI and General Linear Modeling to evaluate the AUC of different models. Although different classification methods did not result in significantly differing outcomes (AUC values between 0.96 and 0.99), SVM’s performance was better. The contribution of NDVI resulted in significantly higher AUC values. SkySat’s bands provided slightly better input data related to PlanetScope but the difference was minimal (~3%); accordingly, both satellites ensured excellent classification results.