The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 39–45, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-39-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 39–45, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-39-2021

  28 Jun 2021

28 Jun 2021

RLSNAKE: A HYBRID REINFORCEMENT LEARNING APPROACH FOR ROAD DETECTION

N. Botteghi1, B. Sirmacek2, and C. Ünsalan3 N. Botteghi et al.
  • 1Robotics and Mechatronics, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, The Netherlands
  • 2Smart Cities, School of Creative Technology, Saxion University of Applied Sciences, The Netherlands
  • 3Electrical and Electronics Engineering, Faculty of Engineering, Marmara University, Turkey

Keywords: Remote Sensing, Computer Vision, Hybrid Artificial Intelligence, Reinforcement Learning, Road Detection

Abstract. Road network detection from very high resolution satellite and aerial images is highly important for diverse domains. Although an expert can label road pixels in a given image, this operation is prone to error and quite time consuming remembering that road maps must be updated regularly. Therefore, various computer vision based automated algorithms have been proposed in the last two decades. Nevertheless, due to the diversity of scenes, the field is still open for robust methods which might detect roads on different resolution images of different type of environments. In this study, we picked an earlier proposed road detection method which works based on traditional computer vision and probability theory algorithms. We improved it by further steps using reinforcement learning theory. With the help of the novel hybrid technique (traditional computer vision method combined with reinforcement learning based artificial intelligence), we achieved a solution that we call RLSnake. This new method can learn new image scenes and resolutions rapidly and can work reliably. We believe that the proposed RLSnake will be a significant step in the remote sensing field in order to develop solutions which might increase performance by combining the power of traditional and new techniques.