Modern advances in the field of neural network data recognition open new perspectives for the use of satellite and aerial photography in aerospace research. The use of unmanned aerial vehicles (UAVs) is becoming increasingly important in various fields, including military, environmental, and agro-industrial applications. However, the issue of automating the processing and analysis of data obtained from satellite and aerial surveys remains relevant in terms of the implementation of these technologies. In this study, we focus on the analysis of the effectiveness of cross-sectional neural network recognition, which was performed on both satellite and aerial (UAV). One of the key problems is the need to implement automated methods of analysis and classification of images obtained from different sources. In the context of using satellite data in aerospace applications, it is important to understand how effectively neural networks can adapt to changes in information sources.
It should be noted that the primary goal of the study was to check the possibility of expanding the dataset of aerial photography. That is why the present sets were studied in this ratio (4 satellite to 1 aerial survey). Roads, tracks Industrial building Pastures Agricultural fields with perennial crops A cluster of residential buildings Rivers Reservoirs, sea canvas
Baseball pitches with a distinctive configuration
Territories of educational institutions Sparse Residential Areas with a low density of residential buildings
Areas designated for aviation services, including runways and airport infrastructure Areas with no significant vegetation or cover, such as bare ground or areas without vegetation Places where the shore and the water meet, with a sandy or rocky surface Structures for crossing water obstacles or other areas, usually with a building structure Central parts of cities or settlements with intensive construction and infrastructure Religious buildings, such as churches or temples Areas designated for commercial activities, such as business centers, shops and offices Territories with industrial infrastructure Open spaces with natural grass vegetation Areas with a moderate density of residential development and residential buildings Mountain regions with a characteristic landscape Territories intended for recreation Areas for parking vehicles Areas with children's playgrounds The reservoir is smaller in size and shallow Areas of port infrastructures for sea transport Large open areas or squares in cities Areas with sports grounds for games and events Areas with tanks for storing various substances Bridge-like structures that cross various obstacles Dry areas with a characteristic landscape Territories for agricultural production with crops mobile_home_park
Territories with stationary or mobile homes Areas with images of clouds or cloud formations Areas designated for commercial activities, such as business centers, shops and offices Areas with a high density of residential buildings 700 Dry, sparsely populated areas with a characteristic landscape Areas with dense forest vegetation Territories with a highway and road infrastructure facilities Images of golf courses and their infrastructure.
Photos of sports grounds for running and other sports Areas with port infrastructure for receiving and servicing ships aTnerdriottohreiersinwdiuthstirniadlufsatcriilailtiiensfrastructure, factories 700 Road intersection areas with road infrastructure Areas with the image of islands surrounded by water bodies Areas with large bodies of water, such as lakes Open spaces with natural grass vegetation Areas with a moderate density of residential development and residential buildings baseball_diamond
Images of baseball fields and their infrastructure circular_farmland
Areas with circular or elliptical agricultural fields 700 Areas with images of aircraft in various operating 700 conditions Areas of airports with runways and infrastructure for aviation services Photos of basketball courts and their surroundings Coasts and coastal areas with sandy or rocky surfaces Structures for crossing water obstacles or other areas, usually with a building structure Image of areas with characteristic vegetation
Religious buildings, such as churches or temples basketball_court airplane airport beach bridge chaparral church cloud commercial_area dense_residential desert forest freeway golf_course ground_track_field harbor industrial_area intersection island lake meadow medium_residential 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 Sotbrjueccttusres that cross and pass over other roads or 700 Territories with railway infrastructure and roads 700 Areas of railway hubs and infrastructure Areas with rectangular or square agricultural fields 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 700 C.3.27 C.3.28 C.3.29 C.3.30 C.3.31 C.3.32 C.3.33 C.3.34 C.3.35 C.3.36 C.3.37 C.3.38 C.3.39 C.3.40 C.3.41 C.3.42 C.3.43 C.3.44 C.3.45 thermal_power_station iTnefrrraistotrruiecstuwreith thermal power plants and energy 700
Areas depicting wetlands and swamps
Areas with tanks for storing various substances sparse_residential
Areas with a low density of residential buildings baseballdiamond dense residential beach buildings chaparral forest freeway golfcourse harbor intersection mediumresidential mobilehomepark overpass parkinglot river runway sparseresidential storagetanks tenniscourt
Description Areas used for agricultural activities, such as fields where different crops are grown Images of aerial vehicles Image of baseball fields with a characteristic configuration Places where the shore meets the water, with a sandy or rocky surface Areas with a concentration of buildings, including residential, commercial and other structures Regions with dense shrub vegetation Areas with a high density of residential buildings Territories with dense forest vegetation Motorways and expressways for automobile traffic Golf courses Areas of port infrastructure for parking and maintenance of ships Images of road intersections and crossed roads Areas with a moderate density of residential buildings Areas with parks for mobile homes and residential facilities Above-ground structures for crossing other roads or obstacles Territories for parking vehicles Large water streams Runways for aircraft Areas with a low density of residential buildings 100 Areas with tanks for storing various substances 100 Tennis courts 2. Experiment results Tables 7–14 present the results of a pairwise mapping study. Since some satellite datasets contain a significant number of classes, all tables are reduced to a view where satellite classes are arranged horizontally (rows) and UAV classes vertically (columns).
In this case, you can understand which classes were included in the educational sample (the one in which it is displayed) from the name of the table. For example, in Table 8 it is B.1, vertical, and in Table 7 it is C.1, horizontal.
P.1.1 P.1.2 P.1.3 P.1.4 P.1.5 P.1.6 P.1.7 P.1.8 P.1.9
P.1.10 P.1.1 P.1 .2 P.1.3 P.1.4 P.1.5 P.1.6 P.1.7 P.1.8 P.1.9 P.1.10 C.2.1 C.2.2 C.2.3 C.2.4 C.2.5 C.2.6 C.2.7 C.2.8 C.2.9 C.2.10 C.2.11 C.2.12 C.2.13 C.2.14 C.2.15 C.2.16 C.2.17 C.2.18 C.2.19 C.2.20 C.2.21 C.2.22 C.2.23 C.2.24 C.2.25 C.2.26 C.2.27 C.2.28 C.2.29 C.2.30 C.3.1 C.3.2 C.3.3 C.3.4 C.3.5 C.3.6 C.3.7 C.3.8 C.3.9 C.3.10
From the given data can be drawn the following conclusions. First of all, the classes reflecting natural formations (forests, fields of various kinds, reservoirs and rivers) have great compatibility from the point of view of displaying satellite images in UAVs - analogues. For example, classes C.4.8 and C.3.14 appeared in B.1.2 almost in full and all of them are annotated as forests. Classes C.4.17 (rivers) and C.2.16 (ponds) are also strongly reflected in class B.1.10 (bodies in general). Indeed, it might seem obvious, but it is worth noting that when reflected in reverse, such ambiguity disappears. For example, the same class of reservoirs B.1.10 when displayed in satellite classes relatively is equally distributed between C.4.1 and C.4.4 or with an ambiguous advantage (0.32) is reflected in the class of ships C.3.17. This shows that, from the point of view of natural formations, it is the data obtained with the help of satellite imaging that can serve to expand the UAV dataset.
The most effective class from the point of view of mapping satellite images into UAV classes was class B.1.5 – non-industrial buildings. Classes C.4.5, C.4.13, C.4.14, C.2.9, by more than 80 percent, were reflected precisely in class B.1.5, while they themselves represent different types of buildings, which indicates that they you can supplement this class. On the other hand, the dataset as a whole can be supplemented with classes that have a not so high level of reflection in B.1.5 (from 0.3 to 0.75), but at the same time it is the largest. Examples of such are P.4.21, P.4.20, P.4.15, P.4.12, P.4.11, P.3.43, P.3.43, P.3.36, P.3.34. and other. Also, classes C.4.16, C.2.14 (parking lots) showed a rather high, but at the same time, false level of display - such indicators can also be considered a marker for selecting a new dataset class or expanding an already existing one (for example, motor vehicle class B.1.3).
An example of a central office
In general, the study confirmed the feasibility of creating mixed-type datasets and the possibility of supplementing the UAV dataset with images of satellite data, or creating new classes based on them. In the perspective of future research, it is possible to highlight the creation of a universal "framework" set that could be easily modified to meet the needs of various tasks. Declaration on Generative AI The author(s) have not employed any Generative AI tools.