In this thesis, a method for detecting ditches using digital elevation data derived from LiDAR scans was developed in collaboration with the Swedish Forest Agency. The objective was to compare a machine learning based method with a state-of-the-art automated method, and to determine which LiDAR-based features represent the strongest ditch predictors. This was done by using the digital elevation data to develop several new features, which were used as inputs in a random forest machine learning classifier. The output from this classifier was processed to remove noise, before a binarisation process produced the final ditch prediction. Several metrics including Cohen's Kappa index were calculated to evaluate the performance of the method. These metrics were then compared with the metrics from the results of a reproduced state-of-the-art automated method. The confidence interval for the Cohen's Kappa metric for the population was calculated to be [0.567 , 0.645] with a 95 % certainty. Features based on the Impoundment attribute derived from the digital elevation data overall represented the strongest ditch predictors. Our method outperformed the state-of-the-art automated method by a high margin. This thesis proves that it is possible to use AI and machine learning with digital elevation data to detect ditches to a substantial extent.

Portfolio managers have a great interest in detecting high-performing stocks early on. Detecting outperforming stocks has for long been of interest from a research as well as financial point of view. Quantitative methods to predict stock movements have been widely studied in diverse contexts, where some present promising results. The quantitative algorithms for such prediction models can be, to name a few, support vector machines, tree-based methods, and regression models, where each one can carry different predictive power. Most previous research focuses on indices such as S&P 500 or large-cap stocks, while small- and micro-cap stocks have been examined to a lesser extent. These types of stocks also commonly share the characteristic of high volatility, with prospects that can be difficult to assess. This study examines to which extent widely studied quantitative methods such as random forest, support vector machine, and logistic regression can produce accurate predictions of stock price directions on a quarterly and yearly basis. The problem is modeled as a binary classification task, where the aim is to predict whether a stock achieves a return above or below a benchmark index. The focus lies on Asian small- and micro-cap stocks. The study concludes that the random forest method for a binary yearly prediction produces the highest accuracy of 69.64%, where all three models produced higher accuracy than a binary quarterly prediction. Although the statistical power of the models can be ruled adequate, more extensive studies are desirable to examine whether other models or variables can increase the prediction accuracy for small- and micro-cap stocks. / Portföljförvaltare har ett stort intresse av att upptäcka högpresterande aktier tidigt. Detektering av högavkastande aktier har länge varit av stort intresse dels i forskningssyfte men också ur ett finansiellt perspektiv. Kvantitativa metoder för att förutsäga riktning av aktiepriset har studerats i stor utsträckning där vissa presenterar lovande resultat. De kvantitativa algoritmerna för sådana prediktionsmodeller kan vara, för att nämna ett fåtal, support vector machines, trädbaserade metoder och regressionsmodeller, där var och en kan bära olika prediktiv kraft. Majoriteten av tidigare studier fokuserar på index såsom S&P 500 eller storbolagsaktier, medan små- och mikrobolagsaktier har undersökts i mindre utsträckning. Dessa sistnämnda typer av aktier innehar ofta en hög volatilitet med framtidsutsikter som kan vara svåra att bedöma. Denna studie undersöker i vilken utsträckning väletablerade kvantitativa modeller såsom random forest, support vector machine och logistisk regression, kan ge korrekta förutsägelser av små- och mikrobolags aktiekursriktningar på kvartals- och årsbasis. I avhandlingen modelleras detta som ett binärt klassificeringsproblem, där avkastningen för varje aktie antingen är över eller under jämförelseindex. Fokuset ligger på asiatiska små-och mikrobolag. Studien drar slutsatsen att random forest för en binär årlig prediktion ger den högsta noggrannheten på 69,64 %, där samtliga tre modeller ger högre noggrannhet än en binär kvartalsprediktion. Även om modellerna bedöms vara statistiskt säkerställda, är det önskvärt med fler omfattande studier för att undersöka om andra modeller eller variabler kan öka noggrannheten i prediktionen för små- och mikrobolags aktiekursriktning.