The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVIII-1/W2-2023
https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-539-2023
https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-539-2023
13 Dec 2023
 | 13 Dec 2023

TOWARD DATA LAKES FOR CRISIS MANAGEMENT

A. Krtalić, A. Kuveždić Divjak, and A. Miletić

Keywords: Data Lakes, Crisis Management, Humanitarian Demining, Heat Islands, Volcanic Activity

Abstract. The content of the data lake comes (is filled) from different sources, and different users (experts in various fields) of the same data can download and analyse the same data for their (different) needs and analysis. Big Data about the human environment and the effect of natural and human-caused disasters (in this case: heat islands, earthquakes and lava flows, and landmine contamination) on that environment have been available to many people for years and are the subject of discussions, but there are still numerous research challenges in the form of structuring and storing data and analysis results. This implies certain requirements for efficient integration, access and querying of the various data in the data repository for the described purpose. Data lakes and data warehouses are offered as solutions to this problem. Well-designed data lakes can be a basic building block for different solutions in the analysis of the effects of disasters on the environment, and high-quality data warehouses for modelling future potential disasters in the same area. This paper presents certain personal observations and certain proposals for the creation of efficient data lakes and data warehouses (based on many years of work on problems in areas: humanitarian demining, heat islands and volcanic activity) for the needs of decision-making in crisis based on examples from practice. The goal is to influence the development of a unique framework for the creation and maintenance of a data lake, in terms of its better utilization so that it does not become a data swamp.