End-to-end system for monitoring the state of rivers using a drone

Abstract

Automatic data analysis systems in the Internet of Things are a key element. One such case is the use of drones to monitor rivers, which are quite often located around built-up areas. This is an important element for the analysis of urban areas that are exposed to various environmental challenges such as pollution and animal habitats. Data analysis allows the detection of anomalies in the state of rivers, reducing the risk of ecological disasters or even floods. Additionally, constant control of areas enables analysis of the impact of urbanization on a given area as well as environmental protection. In this paper, we propose an end-to-end system, where the user performs measurements with a drone and the result is a segmentation mask from the U-Net network, but improved by image processing algorithms. The system is based on performing segmentation with a neural network, imposing the obtained mask on the image that was previously subjected to edge detection. All pixels under the mask are analyzed by the clustering method in terms of belonging to a river or bank. In addition, when there are other measurements from the same area, they are used to compare and analyze changes. The proposed system architecture is based on the automation of activities due to the combination of various graphics processing methods. Moreover, the method allows for obtaining more accurate segmentation results than classic methods. The proposition was tested on data gathered near river areas in southern Poland to show the possibilities and effectiveness of the system. Proposed methodology reached 0.8524 of Dice coefficient using VGG16 as encoder.