The subject of this article is the concept of a geoinformation platform for landmine detection. Modern warfare and its increasing scale have become a relevant topic today. Undetected explosives threaten business (agriculture, logistics, etc.) and human lives. The problem becomes more acute with the rapid extension of minefield areas, which requires significant time and resources and carries high risks. Remote sensing leverages landmine detection possibilities, providing useful information about landmine displacement with no additional risk during data collection over a large area. This study aims to present a combined approach for revealing hidden landmines using UAVs equipped with different sensor types. The tasks to be solved are to define the overall structure and components of the geoinformation platform, choose the technological solutions for each of them, and implement the system prototype that makes it possible to extend its configuration in the future. The methods used are remote sensing, automated object detection, and centralized data processing in a geographic information system (GIS). Multispectral imagery and magnetometric remote measurements create the background information required to detect landmines and other explosive objects. The results of this study provide a general framework, i.e., a geoinformation platform for landmine detection and mapping. The tasks include UAV-based remote data gathering, UAV mission planning and flight control, data processing and mapping via general GIS, and updating new landmine signatures in the corresponding database. The landmine detection process uses information from the landmine signature database to verify suspicious objects. The results are presented in the form of a probabilistic map, which supports the decision-making process of demining. Conclusion. The proposed approach significantly decreases the time required for landmine detection and mitigates demining risks, which is crucial for dealing with the consequences of war. At present, the concept is being developed in the form of a geoinformation platform research prototype involving an open-source Quantum GIS (QGIS) software system and Python programming language, which is used to create plug-ins for QGIS. The entire landmine remote detection process can be fully automated. Future studies will involve extensive experimental testing and may involve convolutional neural networks (CNN) as a detection mechanism.

The approach to implementing the remote sensing technologies and geospatial modelling for smart city support is presented. The hierarchical structure and basic components of the smart city information support subsystem are considered. Some of the already available useful practical developments are described. These include city land use planning, urban vegetation analysis, thermal condition forecasting, geohazard detection, flooding risk assessment. Remote sensing data fusion approach for comprehensive geospatial analysis is discussed. Long-term city development forecasting by Forrester – Graham system dynamics model is provided over Kiev urban area.

Nowadays, Moroccan oasis agro-ecosystems are under intense effect of natural and anthropogenic factors. Therefore, this essay proposes to use Leaf Area Index (LAI) to assess the consequences of the oases long-term biodegradation. The index was used as a widely-applied parameter of vegetation structure and an important indicator of plant growth and health. Therefore, a new optical multispectral Sentinel-2 data were used to build a long term LAI time series for the area of the Erfoud and Rissani oases, Errachidia province in Drâa-Tafilalet region in Morocco. Nine images of LAI spatial distribution over the study area were obtained by means of SNAP Biophysical Processor over the period since July 2015 till May 2018. Time series analysis of the resultedmaps has revealed a stable trend towards the average LAI decreasing and vegetation structure simplification as a consequence.

The paper considers the possibility of hydrocarbon deposit remote detection using the specially developed software application entitled “geologist’s computer assistant”. Its tasks defined as logiccomputational operations on data, facts and other information, hypothesis processing, intellectual decisionmaking support. The appropriate algorithmic and software tools of expert systems are offered for this tasks solving.