Automatic Rural Road Centerline Detection and Extraction from Aerial Images for a Forest Fire Decision Support System

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Abstract

To effectively manage the terrestrial firefighting fleet in a forest fire scenario, namely, to optimize its displacement in the field, it is crucial to have a well-structured and accurate mapping of rural roads. The landscape’s complexity, mainly due to severe shadows cast by the wild vegetation and trees, makes it challenging to extract rural roads based on processing aerial or satellite images, leading to heterogeneous results. This article proposes a method to improve the automatic detection of rural roads and the extraction of their centerlines from aerial images. This method has two main stages: (i) the use of a deep learning model (DeepLabV3+) for predicting rural road segments; (ii) an optimization strategy to improve the connections between predicted rural road segments, followed by a morphological approach to extract the rural road centerlines using thinning algorithms, such as those proposed by Zhang–Suen and Guo–Hall. After completing these two stages, the proposed method automatically detected and extracted rural road centerlines from complex rural environments. This is useful for developing real-time mapping applications.

Original languageEnglish
Article number271
Number of pages20
JournalRemote Sensing
Volume15
Issue number1
DOIs
Publication statusPublished - 2 Jan 2023

Keywords

  • centerline extraction
  • convolutional neural network (CNN)
  • decision support system (DSS)
  • deep learning
  • forest fires
  • geographic information system (GIS)
  • rural roads
  • spatial pyramid pooling (SPP)
  • wireless sensor networks (WSN)

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