OpenStreetMap is a crowd sourced, free and open sourced data-set that contains geographical data. As any other data source OpenStreetMap contains a variety of errors, topological, geo-spatial and semantic to mention a few. This thesis focuses on using algorithms to detect and flag these errors. In particular algorithms built on the concept of conflation, eg. using another data-set in order to compare. The first step is to establish a matching between the two data-sets, which is done with an accuracy of above $94%$ in our test areas. After this match has been established differences between the data-sets can be found. These differences can be used to flag errors which can be forwarded for manual correction. We have looked at attributes such as names, where name dissimilarity can been used to differentiate different types of errors from each other, as well as investigated the correctness of speed limits in the OpenStreetMap.

Using motion detection in surveillance cameras is a decent way of detecting actions in environments. However, it is incapable of determining the causing source, such as animals, flying objects, or humans. This incapability tends to trigger alarms where more often then not, a human is not present.

In this report, we study the effect of adding another evaluation layer before triggering an alarm - an object detection layer identifying humans explicitly. Video alarms triggered by motion detection, and simultaneously containing human properties are weighed to be more likely of a real alarm.

We present methods, and choices of data used while applying object detection which filters up to 85% of false alarms without losing true ones.

The benefit of our approach is the reduction of human hours spent on evaluation of false alarms. It is also possible to train the detector to specific environments increasing the accuracy by using neural networks.