# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 5 Experimentation and Evaluation
### One-Class SVM Experiments  
The second approach involves the use of One-Class SVM models using indices derived from the previously mentioned correlation tests. The model's hyperparameters were set to default for most of the classifiers, with only one case where we decided to reduce the nu parameter by 0.1 ([[https://scikit-learn.org/stable/modules/generated/sklearn.svm](https://scikit-learn.org/stable/modules/generated/sklearn.svm)]([https://scikit-learn.org/stable/modules/generated/sklearn.svm](https://scikit-learn.org/stable/modules/generated/sklearn.svm)). OneClassSVM.html, accessed on 1 September 2023). The experiment using just the EMBI index was conducted first. Using this method, with a \(15\times 15\) fishnet in the city center area, resulted in the detection of some abandoned buildings but not to a satisfying degree. Some of the bars and restaurants, especially the ones including some vegetation, were misclassified as abandoned. Increasing the number of polygons provided similar results, with the only difference being that some buildings were classified correctly as True Negatives. Moving to the area with sparse buildings, the results were a bit more promising, with some unused storage buildings and pubs being classified correctly. Unfortunately, the model classified various houses as abandoned due to the increased presence of vegetation in their yards. Contrary to our expectations, increasing the number of polygons in this case provided worse results, with errors in some previously correctly detected areas. In the case of the coastal areas, the model performed similarly where increased vegetation was present but classified some of the cafes as abandoned. Generally, the model cannot provide adequate results for the detection of vacant buildings. Since the EMBI made correct predictions but failed in cases of various buildings whose activity is more pronounced during the night, we combined it with the average radiance of nighttime lights. Checking the central area in various split instances, the majority of not-abandoned buildings were classified now correctly, including shops, pubs, and restaurants, independently of the vegetation presence. Some of the abandoned buildings or vacant lots were also categorized appropriately. When evaluating the area near the Old Town, the model's predictive capability was hit. Most of the nightlife amenities, as well as department stores, were False Positives. The only instance where the model was accurate was in areas with no buildings. The results in the coastal area followed the same pattern. Areas that are less busy during the night, such as restaurants, or that did not have a lot of buildings were classified as abandoned, regardless of other characteristics. However, this was also the case for some cafeterias. Splitting these larger areas to fit polygons more accurately did not improve the results by a lot. Overall, the model seems sensitive to nightlights, and since it performed better than the previous ones, we decided to tune some of its parameters. To be precise, we modified the nu parameter specified in [62], which fine-tunes the trade-off between overfitting and  
Figure 7: Experimentation results on all areas (25 \(\times\) 25) using NDVI/NDBI (**top three pictures**) and NDVI/NDBI/average radiance (**bottom three**) in Random Forests. generalization. Nu specifies a lower bound for the number of samples that are support vectors and an upper bound for the number of samples that are on the wrong side of the hyperplane. The default is 0.1. The nu parameter must be in the range \([0,1]\). For instance, for an nu value of 0.1, the decision boundary will allow a maximum of 10% of the training samples to be incorrectly classified or to be regarded as outliers. After experimenting with different values of nu using our constructed Volos dataset, we decided to use a nu equal to 0.4 to allow less of the training dataset to be misclassified and since it is less than the default value of 0.5.