Treffer: PYTHON WEB SERVER FOR SENSOR DATA VISUALIZATION.

The primary goal of this paper is to provide a solution how to visualize enormous amount of meteorological data which are generated from sensors into a map by Python scripting. The combination of Python scripting with powerful database platform based on PostgreSQL provides a high-performance tool for visualizing data from sensors. Sensor data are one of the main components of "Big data" and their post-processing is crucial for the better comprehension of the environment in the area of interest. Sensors measure meteorological phenomena such as temperature, humidity, wind speed and direction or rainfall. They can also measure carbon dioxide (CO2), nitrous oxide (NO2), volatile organic compound (VOC). These chemical substances are very dangerous air pollutant in cities. These quantities can be displayed as line graph for the particular period. Area of interest was supplemented by aerial photos from drone (UAV - Unmanned Aerial Vehicle) taken in fourteen days period. Geometric and radiometric correction were applied to all taken pictures and utilized for the background of the map. The result is a Python web server based on microframework Flask, where communication between Python web server and database is procured by Psycopg2 package. Data modification to machine readable format from a database to a map is secured by Vincent package. Moreover, Folium package ensures access to Leaflet JavaScript library through Python. [ABSTRACT FROM AUTHOR]

Copyright of Proceedings of the International Multidisciplinary Scientific GeoConference SGEM is the property of STEF92 Technology Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)