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EN
The Automatic Identification System (AIS) device is mandatory for ships that comply with the International Convention for the Safety of Life at Sea (SOLAS). AIS is intended for vessel traffic monitoring to improve shipping safety. In the examined area, the base station received 22 128 345 messages in April 2019. Approximately 80% of these messages included position reports, which were subjected to geospatial analysis. One possible utilization of AIS messages is used in an intelligent maritime transport statistics production system called TranStat in the Gospostrateg project. This specific study compares the speed of executing geospatial queries in a relational PostgreSQL database engine and a non-relational MongoDB database engine. For the purpose of this research, we have defined four AIS datasets, four test polygons of varied number of vertices, and a reference point on a fairway. The tests were used to assess the execution of the queries in a database that returns the number of ships located in a predefined area and the number of ships located at a preset distance from the defined point. It has been determined from the test results that test queries are performed faster and data stored in the database occupy less disk space in MongoDB than in PostgreSQL. Faster geospatial analysis of AIS messages may improve the navigation safety by earlier detection of dangerous situations.
EN
Timely and accurate detection of land use/land cover (LULC) change is important for the macro and micro level sustainable development of any region. For this purpose, geospatial techniques are the best tool for change analysis as they supply timely, cheaper, precise and up to date information. This paper examines the spatial temporal change trend in LULC in the case of Central Haryana. Landsat 2, 3, 5, 7 and 8 images for the years 1975–2020 for pre and post monsoon periods were analyzed for the study. Radiometric correction was performed to derive better information. ArcGIS 10.2 and ENVI 5.3 are used for thematic layout and thematic change preparation. An unsupervised classification using ERDAS IMAGINE 2015 has also been done to classify study area in eight classes. The year 1975 is considered as the base year for change detection analysis. Results showed an increasing trend for the land use classes of built up, water body, and agricultural land without waterlogging in the pre and post monsoon periods between 1975 and 2020. Remaining land use classes of agriculture with waterlogging, open waterlogged area, vegetation and fallow land/sand dunes decreased during the same period. Increased human activities have changed the LULC in the region and have had a great impact on its sustainable regional development.
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