Open Access

There have been a number of philosophical discussions whether functional programming or object-oriented programming is the better programming concept. In reality, programmers utilize both concepts and functional programming concepts improve object-oriented languages. Likewise the modeling of OO languages should also reflect these concepts in the modeling process. This paper improves the modeling of behavior (usually expressed through functional programming) in UML class diagrams. In UML class diagrams, behavior modeling is only achieved by modeling a new class containing the desired function. If several alternatives for a certain behavior have to be expressed, the modeling complexity increases because we need to introduce an interface and for each alternative an additional class. Therefore, we propose a new function element that circumvents these problems and reduces the complexity of the model. Due to the proposed <<Function>> stereotype, functions in the model can be identified at first glance. The new model is motivated by the strategy pattern and evaluated against a more complex design pattern. A possible first implementation is presented.

Forest management relies on the analysis of satellite imagery and time intensive physical on-site inspections. Both methods are costly and time consuming. Satellite based images are often not updated in a sufficient frequency to react to infestations or other occurring problems. Forest management benefits greatly from accurate and recent information about the local forest areas. In order to react appropriately and in time to incidents such as areas damaged by storms, areas infested by bark beetles and decaying ground water level, this information can be extracted from high resolution imagery. In this work, we propose UAVs to meet this demand and demonstrate that they are fully capable of gathering this information in a cost efficient way. Our work focuses on the cartography of trees to optimize forest-operation. We apply deep learning for image processing as a method to identify and isolate individual trees for GPS tagging and add some additional information such as height and diameter.