Geospatial Object Detection Using Deep Networks

In the last decade, deep learning has been drawing a huge interest due to the developments in the computational hardware and novel machine learning techniques. This progress also significantly effects satellite image analysis for various objectives, such as disaster and crisis management, forest cover, road mapping, city planning and even military purposes. For all these applications, detection of geospatial objects has crucial importance and some recent object detection techniques are still unexplored to be applied for satellite imagery. In this study, aircraft, building, and ship detection in 4-band remote sensing images by using convolutional neural networks based on popular YOLO network is examined and the accuracy comparison between 4-band and 3-band images are tested. Based on simulation results, it can be concluded that state-of-the-art object detectors can be utilized for geospatial objection detection purposes.


Geospatial object recognition using deep networks for satellite images
Barut, Onur; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2018)
Deep learning paradigm has been drawing significant interest during the last decade due to the recent developments in novel machine learning algorithms and improvements in computational hardware. Satellite image analysis is also an important scientific area with many objectives, such as disaster and crisis management, forest cover, road mapping, city planning, even military purposes. Spatial correlations of land cover or geospatial objects between different images lead to widely utilization of convolutional...
On the analysis of deep convolutional neural networks applied to building detection in satellite images
Karagöz, Batuhan; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2015)
Deep Learning has gained much interest recently, probably induced by the re- quirements to learn more complex and abstract concepts. As concepts to be learned become more abstract, their regions in the raw input space also become highly variational. In many cases, shallow architectures fail to learn highly varia- tional functions. One area of interest where concepts to be learned are complex is remote sensing. In this thesis, performance and suitability of deep architectures for recognition of building patc...
Mesh segmentation from sparse face labels using graph convolutional neural networks.
Sever, Önder İlke; Sahillioğlu, Yusuf; Department of Computer Engineering (2020)
The marked improvements in deep learning influence almost every area of computer science. The mesh segmentation problem in computer graphics has been an active research area and keep abreast of the trend of deep learning developments. The mesh segmentation has a central role in multiple application areas for 3D objects. It is chiefly used to produce the object structure in order to manipulate the object or analyze the components of it. These operations are primitive, and that primitiveness causes a variety ...
Case studies on the use of neural networks in eutrophication modeling
Karul, C; Soyupak, S; Cilesiz, AF; Akbay, N; Germen, E (2000-10-30)
Artificial neural networks are becoming more and more common to be used in development of prediction models for complex systems as the theory behind them develops and the processing power of computers increase. A three layer Levenberg-Marquardt feedforward learning algorithm was used to model the eutrophication process in three water bodies of Turkey (Keban Dam Reservoir, Mogan and Eymir Lakes). Despite the very complex and peculiar nature of Keban Dam, a relatively good correlation (correlation coefficient...
Neural networks with piecewise constant argument and impact activation
Yılmaz, Enes; Akhmet, Marat; Department of Scientific Computing (2011)
This dissertation addresses the new models in mathematical neuroscience: artificial neural networks, which have many similarities with the structure of human brain and the functions of cells by electronic circuits. The networks have been investigated due to their extensive applications in classification of patterns, associative memories, image processing, artificial intelligence, signal processing and optimization problems. These applications depend crucially on the dynamical behaviors of the networks. In t...
Citation Formats
O. Barut and A. A. Alatan, “Geospatial Object Detection Using Deep Networks,” 2019, vol. 11127, Accessed: 00, 2020. [Online]. Available: