Environmental damage assessment based on satellite imagery using machine learning

The aim of this thesis is to provide a source of information about damage assessment in forestry using deep learning. A large source of environmental information is provided by satellites imagery. Orbital devices are equipped with sensors that read the frequency variations in the terrestrial electromagnetic field. The information obtained by these devices is composed by collections of dots. Machine learning methodologies, however, have the ability to transform raw data into human-understandable output. Cloud and blur represent artefacts that need to be tackled to obtain high-quality imagery. For instance, a deep learning neural network, a Generative Adversarial Neural Network, can extrapolate the cloud compound from the image. Moreover, resampling techniques are used to improve their resolution. In this way, it is possible to correct the overall quality of satellite data. The Finnish Kvarken Region, situated in the province of Vaasa, comprises a delicate forestry zone. Climate changes and the rise of temperature are influencing the forest quality negatively. Moreover, the public company in charge of the operational management needs new tools in order to enhance the environment condition. Plenty of satellite data analysis frameworks are available for the consumer. In particular, SNAP, QGIS and ArcGIS offer capabilities to analyze environmental damage. Moreover, Google Earth Engine uses powerful programming languages such as Python to elaborate information from the Kvarken Region. It is also possible to study the historic forestry change from the past years until today. Unsupervised and supervised machine learning models are used to underline the difference between techniques. Deforested areas in the Kvarken Region are mapped using state-of-the-art deep learning architectures for image segmentation. The implementation is done using Python programming language and open-source libraries such as TensorFlow and Keras

Automated inventory application

The aim of this thesis was to create an automated inventory application to reduce the working hours and to minimize errors in the inventory process of a small enterprise. The application was done by using a Microsoft C# programming language. The goal was to combine business intelligence with computer engineering. The theoretical background demonstrates the language and framework used to build the application. Also a description about the connection with a GSM modem and the computer application is given. The practical part of this thesis describes the process used to create the application on the client and server side including the realization of graphical user interface. In addition, a deep view of the functions and the process of coding is given. The project was divided in two branches, the server side and the client side. The first one collects the inventory data used by the management, and the second one is used by the sales agents who send the data to the server side with an Android application or with SMS messages. The results show that the automated inventory application presented has been a successful implementation for both sides. The given feedback has been positive. The commissioner of this thesis has confirmed that the application has been a useful and essential update to change the old inventory process