The purpose of this thesis is to examine the political ambitions regarding culture in Värmland, and the situation for artists living on the countryside in Värmland. This means that the conditions surronding the artists situation will be looked upon. I have chosen the political document Kulturplanen, created by Region Värmland, as my guideline to what kind of political view there is on art and cultures role in the region. Furthermore, I have chosen Pierre Bourdieu`s theory about art as a field controlled by and under the influence of different rules within the filed itself and what or whom has the power to act and be a part of the filed. I also use interviews as my method for this study. The purpose is to learn more about and understand the experiences expressed by the artists themselves. The main focus of this study is the Art project and Art museum Alma Löv, and the artist Karin and Marc Broos. A comparison between the political agenda concerning culture in Värmland and the situation described by the artists themselves is also made. The analysis is held as a discussion where I look at the different parts and by comparing them create an understanding regarding the conditions and thee situation for art and culture. I found that the role and the support given to art and culture in the region often has to do with the idea of creating side effects rather than supporting art for arts own sake. According to my analysis there is a danger in losing sight of the values that art itself can offer and many artists struggle to make it work which I argue has to do with the political ambitions.

A current field of research today is machine learning because it can simplify everyday life for human beings. A functioning system that has learned specific tasks can make it easier for companies in both cost and time. A company who want to use machine learning is SCA, who owns and manages forests to produce products. They have a need to automate forest classification. In order to evaluate forests, and to plan forestry measures, the proportion of leafy tree that is not used in production must be determined. Today, manual work is required of people who have to investigate aerial photos to classify the tree types. This study investigates whether it is possible, through machine learning, to teach a computer to determine whether it is leaf or not in photographs. A program is constructed with the library ImageAI which receives methods for training and predicting information in images. It examines how the choice of neural network and the number of images affects the safety of the models and how reliable the models can be. Exercise time and hardware are also two factors that are investigated. The result shows that the neural network ResNet delivers the safest results and the more images the computer exercises, the safer the result. The final model is a ResNet model that has trained on 20,000 images and has 79,0 percent security. Based on 50 samples, the mean value for safety is 90,5 percent and the median is 99,6 percent. / Maskininlärning är idag ett aktuellt forskningsområde som kan förenkla vardagen för oss människor. Ett fungerande system som har lärt sig specifika uppgifter kan underlätta för företag i både kostnad och tid. Ett företag som vill använda maskininlärning är SCA, som äger och förvaltar skog för att producera produkter. De har behov av att automatisera klassificering av skog. För att värdera skogar, samt planera skogsåtgärder, måste andelen lövträd som inte används i produktionen bestämmas. Idag krävs det manuellt arbete av personer som måste undersöka flygfoton för att klassificera trädtyperna. Denna studie undersöker om det är möjligt, via maskininlärning, att lära en dator avgöra om det är löv eller inte i ortofoton. Ett program konstrueras med biblioteket ImageAI som erhåller metoder för att träna och förutsäga information i bilder. Det undersöks hur valet av neuralt nätverk och antalet bilder påverkar säkerheten för modellerna samt hur tillförlitlig modellerna kan bli. Träningstid och hårdvara är också två faktorer som studeras. Resultatet visar att neurala nätverket ResNet levererar säkrast resultat och desto fler bilder datorn tränar på, desto säkrare blir resultatet. Den slutgiltiga modellen är en ResNet-modell som tränat på 20 000 bilder och har 79,0 procents säkerhet. Utifrån 50 stickprov är medelvärdet för säkerheten 90,5 procent och medianen 99,6 procent.