Determination of city change in satellite images with deep learning structures
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Abstract
Uneven urban growth is a major problem worldwide as it causes serious losses in vital areas such as farmland and water bodies. In this context, the management of the factors such as agriculture, industry, housing, etc. of the cities as a whole and the studies carried out for the planning should be followed in real life. Change detection based on remote sensing images plays an important role in the field of remote sensing analysis and is widely used in many areas such as resource monitoring, urban planning, disaster assessment, etc. However, the detection of changes in the same areas from satellite images at different times makes it difficult to interpret and detect them with human capabilities due to their dense information content. Recently, with the developments in computer vision technology, deep learning structures have come to the fore in the interpretation of satellite data. In this study, using the Onera Satellite Change Detection (OSCD) data set, change detection from satellite images of different dates belonging to the same regions was tried to be extracted with deep learning structures.
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References
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